To significantly improve the tracking performance of electro-hydraulic servo actuators (EHSAs) subject to unknown disturbances, uncertainties, and unstructured system dynamics, this paper proposes a control strategy based on a self-organizing radial basis function neural network (SORBFNN) augmented by a finite-time disturbance observer (DO). The SORBFNN, which employs time-varying Gaussian basis functions, is first utilized to effectively approximate unstructured control coefficients. Simultaneously, a DO is designed to suppress external disturbances and mismatched uncertainties. Furthermore, to further enhance system robustness, output constraints and input saturation techniques are incorporated into the design. The stability of the closed-loop system is rigorously demonstrated using the Lyapunov stability theorem, and the method’s effectiveness is experimentally validated through comparative benchmarks on a rotary EHSA.

Direct teaching in collaborative manipulators, an essential method for intuitive trajectory control, faces significant challenges due to friction in robot joints. To address this, we present a novel friction compensation framework to improve direct teaching methods for robots. Our approach focuses on mitigating friction in the joints most susceptible to frictional effects, ensuring smoother and more precise motion. The proposed framework uses deep neural networks (DNN) to model the complex friction behavior. This approach circumvents the difficulties associated with traditional friction compensation model selection. We develop specific data input preprocessing algorithms that optimize friction estimation when paired with standard encoders commonly used in collaborative robots. In addition, our custom loss function is specifically designed to improve DNN training in these low-velocity regions. To evaluate the effectiveness of our framework, we conduct comprehensive ablation studies assessing the impact of two critical components: the preprocessing algorithms and the custom loss function. These studies provide insight into the contributions of each element to overall performance. Experimental validation using two 6-DoF collaborative robots demonstrates the practical applicability and effectiveness of our approach.

This work proposes a path generation policy for self-actuating soft grippers by converting external deformation conditions into intrinsic load conditions. This transformation enables anisotropic material orientation control of functional materials — which can deform under stimuli — aligning with the deformation requirements of soft grippers to enhance controllability. Given a desired deformation for an arbitrary geometry, finite element method (FEM) analysis is used to determine the internal stress distribution. The second-order stress tensor is transformed into a traction vector field, guiding the alignment of material anisotropy for optimal deformation. A computational framework is developed to generate smooth, continuous printing paths by integrating along the vector field, ensuring internal morphology control of the target geometry. The proposed method is validated through FEM analysis, demonstrating a positional deviation rate of under 5% relative to the largest geometric feature in each test case across various tested shapes and deformation conditions. The results demonstrate that the algorithm effectively generates 4D printing paths that enable soft grippers to achieve target deformations with high matching rate.

This paper introduces an active fault-tolerant control (FTC) for servo-valve-driven electro-hydraulic actuators (EHAs) subject to unstructured dynamics, uncertainties, and actuator and sensor faults. The proposed methodology is established based on the backstepping control scheme integrated with observers and a fuzzy logic engine. The fuzzy logic-based approximation is utilized to compensate for the unstructured dynamics, while the observer is hybridized to suppress the effects of disturbance and lumped uncertainties. Moreover, under the adverse impact of the sensor malfunction, a disturbance-state observer (DSO) is developed to decouple the unknown residual signal. Notably, unlike other techniques using a bank-of-observers (BOb) to detect and isolate faults with a linear matrix inequality (LMI) tool to determine observer gains, the proposed observer views the residual as an extended state to identify it directly. Subsequently, the backstepping-based active FTC is developed to fulfill specific control requirements. The stability of the closed-loop system is theoretically achieved through the Lyapunov theorem, while the effectiveness of the proposed methodology is validated through comparative simulations under different faulty scenarios.

Impedance control is a widely adopted approach that ensures the compliant behavior of robot manipulators as they interact with their environment according to specifically designed dynamics. For tasks involving six degrees of freedom (DoF), it is crucial to appropriately manage the position and orientation of the end-effector by controlling dynamic behavior. However, describing orientational displacement and designing the corresponding rotational impedance can be challenging, especially when we use a minimal representation. The well-known minimal representation for orientation, the Euler angle, suffers from representation singularity. As a remedy, the quaternion or dual quaternion can be an alternative, but with non-minimal representations. This lack of minimal representation, which does not suffer from the representation singularity, often leads to handling the impedance design by directly defining the potential energy function in the matrix Lie group. This paper proposes a framework for the six-DoF impedance control design that takes advantage of Lie group theory with minimal representation, known as the exponential coordinate. Since the exponential coordinate can be treated as the Euclidean variable within the injectivity radius, it allows for the formulation of the impedance control more systematically and familiarly. In our framework, a detour strategy is utilized; the impedance is designed in the Lie group$SE(3)$, and the control is designed in the Lie algebra$\mathfrak {se}(3)$, which is isomorphic to the vector space$\mathbb {R}^{6}$. The group structure of$SE(3)$can be maintained using the proposed conversion formula between the Lie group and the Lie algebra, called the differential of the exponential map and its time derivative, with a closed-form expression. Experiments with a 6-DoF robot manipulator verified that the proposed impedance control framework effectively reflects the$SE(3)$group structure and achieves the desired dynamic behavior as the functionality of the impedance control with minimal parameters.

This paper proposes an optimization-based control framework for the torque-controlled robot, which can satisfy the joint position, velocity, and acceleration constraints under a bounded jerk. The optimization filter is incorporated as a module to modify the nominal controller output to ensure joint constraints. To formulate the optimization problem as a QP, the torque optimization problem is converted to the jerk optimization problem using the augmented state, and the constraints are reformulated to be affine in the jerk. Here, the viable constraints are derived using the time-optimal braking policy to guarantee the feasibility of the QP. The proposed method was validated in simulation and with a 6-DOF robot manipulator.

Requirements of the system dynamics and unknown disturbance and uncertainty suppression bring certain challenges in developing advanced control algorithms for electro-hydraulic actuators (EHAs). Usually, radial basis function neural network (RBFNN) is employed to address unknown nonlinear dynamics; however, traditional approaches require more effort in adopting multi-estimated parameters when designing adaptive laws, especially in the case of completely unknown system dynamics. Hence, this paper proposes a novel adaptive neural network control, based on the backstepping control (BSC) framework, to compensate for unknown system dynamics and also to reduce the problem of the multi-estimated parameters based on the norm estimation technique. To facilitate the proposed control implementation, a new system transformation is first expressed in such a way that the same key properties as the original system are upheld. Besides, extended state observers (ESOs) are employed to fundamentally address the redundant remaining dynamics of unknown terms, disturbance, and uncertainty. In addition, the command filter (CF) technique is also involved to deal with the explosion complexity in the BSC design. The stability of the closed-loop system and the effectiveness of the proposed controller are theoretically guaranteed via mathematical proof with comparative experiments.Note to Practitioners—This article was motivated by the shortcomings of the published works in addressing the problem of completely unknown system dynamics for Electro-hydraulic Actuators subject to disturbances and unstructured uncertainties. To overcome these concerns, an approximation technique for the dynamical behavior compensation and extended state observers for the disturbances and uncertainties suppression have been carried out in such a new way that reduces a number of estimated parameters compared to the conventional approximation mechanism. Moreover, this new-way approximation also facilitates a combination of the command-filter technique to cope with the complexity explosion issue, which always exists when employing the backstepping control scheme to guarantee the closed-loop system stability. From the derived control strategy and with good experimental results, this proposed method can be considered a premise to expand to other topics in the field of automatically controlled systems and contributes to broad interest in both academic and industrial applications of system modeling and dealing with unexpected impacts.

Admittance control is a control scheme to enable physical interactions of a robot, but it easily induces instability when the robot contacts a rigid surface. In this study, a passivity analysis was conducted on a robotic system with admittance control. The results showed that coupled stability with the environment can be ensured when the velocity error between the proxy and the real robot is eliminated. Thus, an adaptive structure modification method is proposed to suppress the possible source of instability. In addition, the energy tank method is combined with the proposed method to ensure system passivity. As a proof of concept, three robot experiments were performed, and the results of the proposed method were compared with those of conventional admittance control and impedance control (with friction compensation). The comparison showed that the proposed method could make the system passive while realizing the desired dynamics during the interaction.

Unstructured dynamics, un-modeled parameters, and uncertainties in electro-hydraulic servo-valve-controlled actuators (EHSAs) bring difficulty in designing controllers for output tracking performance with stability and robustness satisfactions. Therefore, this article proposes an output feedback-based control for position regulation subject to fully unknown system behavior and uncertainties. With this idea, a coordinately transformed canonical system is utilized where all mismatched/matched uncertainties are lumped into one term with unknown dynamics. Then, a radial basis function neural network (RBFNN) with a norm of weighting vector estimation combined with a time-delayed estimation (TDE) is employed to effectively compensate for the system behavior. Accordingly, a second-order sliding-mode-based output feedback control is conducted to avoid following the step-by-step backstepping control (BSC) design. Interestingly, the proposed methodology requires only the measured output for the control law implementation with only one estimated variable for the system dynamics compensation due to using the hybrid RBF-based TDE (RBF-TDE). Moreover, to lower this approximated error, a modified sliding-mode-based nonlinear disturbance observer (DOB) is extensively involved. The closed-loop system stability is mathematically proven through the Lyapunov theorem with simulation and experiment on EHSA protocols to realize the effectiveness of the proposed algorithm.

This paper proposes a control structure for accurate tracking and compliant behavior of industrial manipulators without additional sensors. To achieve control objectives, friction, one of the biggest causes of performance degradation, should be compensated. For tracking performance, the estimated friction cancels most friction effects as a feed-forward, and the modified robust control structure eliminates the remaining friction uncertainty, which was originally equivalent to the disturbance observer. For compliant behavior, the compensation force fed to the real plant is bounded in contrast to the conventional disturbance observer structure. The compensation bound could be determined through the experiments. The proposed method is validated by experiments with a 6-DOF collaborative industrial manipulator.

This paper proposes a teleoperation framework to exploit the maximum manipulation capability during teleoperation. Here, exploiting maximum manipulation capacity means that the robot moves with its maximum control input while not violating the given constraints, and it is a nonlinear optimization problem with nonlinear constraints which is hard to be solved. The proposed framework relaxes the optimization problem into a simple QP problem and unifies the various constraints in the joint configuration space and Cartesian task space by utilizing control barrier function and control Lyapunov function techniques. The joint angle, velocity, acceleration limits are imposed on a teleoperated robot so that the robot does not generate any emergency stop during the teleoperation. Especially, the robot shows stable motion even near the kinematic singularity, so the operator can explore almost every reachable and admissible state of the robot via teleoperation.

This paper analyzes the effect of control param-eters of feed-forward and inner loop velocity controller in an admittance control scheme on the performance and passivity. The interaction force, inertia, and damping compensation were considered as the feed-forward input. Sufficient conditions and guidelines for each parameter were provided to enable the implementation of a wide range of desired admittance satis-fying passivity. The proposed guidelines were verified through experiments.

Non-linear dynamics, model uncertainties due to hydraulic fluid, and disturbances in hydraulic systems make it difficult to obtain accurate torque tracking performance. In this study, a learning-based torque-tracking method is proposed, which does not require approximating the torque dynamics. The proposed method can capture disturbances and model uncertainties of system. The applied neural network comprises a nonlinear autoregressive model with exogenous inputs (NARX) and a long shortterm neural network (LSTM). NARX is employed due to its ability to predict time series control input from the states of system, and LSTM is used to overcome the vanishing and exploding gradient, which causes long-term memory loss in NARX, leading to inaccurate torque tracking performance. LSTM with NARX achieved a better prediction performance with a mean square error and standard deviation of$0.0015 \pm 0.4\times 103$compared to only NARX with a mean square error of$0.004 \pm 1.0\times 103$at 10 K training data size.

This paper proposes an admittance controller-based teleoperation system for contact-rich tasks. Based on the analysis of the motivating task (deposited iron lump removal task in the steel mill), the system concept is focused on the practical aspects of the system, and various components are combined to enhance the safety of the teleoperation of the robot. To connect the large inertia difference between the teleoperated robot and the command device, the admittance control is utilized in the teleoperation system, and the virtual spring saturation is adapted with the damping injection to ensure safe motion during the task. Lastly, the inertia and damping adaptation rule based on the contact force frequency is developed so that the system can selectively dissipate energy when the system shows oscillatory behavior. The proposed techniques have shown their effectiveness through the experiments. Although this study has started from a specific target, it suggests a practical solution for various contact-rich teleoperated tasks in the hazardous industrial workspace.

In corneal transplantation, 16 submillimeter-sized sutures are generated around the circular-shaped graft cornea. A major challenge in corneal transplantation is the generation of uniformly shaped sutures, where the suture shapes need to be changed depending on the patient. In this study, a corneal suturing robot that can produce sutures of the desired shape with high uniformity is described. The proposed robot manipulates the corneal tissue deformation before inserting the needle for suture shape control. A simulation using the finite element method was proposed to predict the suture shape. The suturing experiment was conducted on a porcine model, and the result was compared with the simulation. As a result, the proposed robot was able to generate sutures with a standard deviation of 108 μm in length and 36 μm in depth. The mean absolute error between the simulation and the experiment was 95 μm in length and 31 μm in depth. This is the first study to suggest a method for controlling the suture shape and quantitatively analyzing it in ophthalmic surgery.

Surface electromyography contains muscular information representing gestures and corresponding forces. However, conventional sEMG-based motion recognition methods, such as pattern classification and regression, have intrinsic limitations due to the complex characteristics of sEMG signals. In this paper, motion intensity, a highly selective sEMG feature proportional to the level of muscle contraction, is proposed. The motion intensity feature allows proportional and simultaneous recognition of multiple degrees of freedom. The proposed method was demonstrated in terms of simultaneous recognition of wrist/hand motions. The result shows that the proposed method can successfully decompose sEMG signals into highly selective signals to target motions. In future works, the proposed method will be adapted for more subjects and to sEMG applications for practical evaluation considering various grasping motions.

Robust particle manipulation is a challenging but essential technique for single-cell analysis and processing of microfluidic devices. This paper proposes a micro-particle manipulation system with a microfluidic channel network. We built gravity-induced pressure actuators, which can generate high-resolution output pressure with a wide range so that the multiple particles can be delivered from the inlet of the chip. In this paper, we studied how to model the proposed multi-input-single-output system and sources of disturbances, and designed a robust controller using disturbance observer technique. The performance of the proposed system was verified through experiments.

Impedance control allows robots to manipulate physical interactions delicately. However, issues associated with path planning and impedance remain unresolved. Herein we propose a path and impedance planning method for impedance control in a robot based on programming by demonstration through telemanipulation using a surface electromyogram. We considered a task that requires quick and precise adjustment of path and impedance, that is, ball trapping. We implemented a teleoperated robot that can deliver an operator's impedance as well as position during the ball trapping task to the slave side. The operators were asked to perform demonstrations of ball-trapping tasks using the implemented teleoperated robot, where the slave side is a vertical robot with one degree of freedom. The path and impedance were recorded and programmed as control input profiles from a set of successful demonstrations using Gaussian mixture regression. The result showed that using the human demonstration, the robot could learn how to catch a dropped ball without rebounding.

Displacement information is essential in many robotic applications involving contact, especially with viscoelastic objects. In this paper, we provide a novel displacement estimation method using contact pressure distribution. Our method can estimate displacement using only a typical tactile sensor, which can be shared by a force measurement. As a result, our method can reduce the number of required sensors for stiffness estimation, so it is suitable for stiffness-required applications with strict spatial constraints. Simulation and experimental results demonstrate the performance of our method.

Muscle activation signals are measured from the skin surface as surface electromyography (EMG) signals that contain information on human intentions; therefore, they are widely used in various robotics applications owing to their usability. However, selective muscle activation extraction is difficult because of the complexity of muscle structures. This study investigated muscle activation source model-based sEMG signal decomposition that considers the anatomical factors of muscle structures. The main advantage of the proposed model-based signal decomposition is that sEMG interface rotation can be recognized by comparing source parameters identified before and after rotation. To assess the performance of the proposed model-based decomposition method, hand motion estimation and rotation recognition were conducted. Additionally, two-dimensional simultaneous control was conducted with an inertial measurement unit to verify the usability of the proposed model. The results indicate that the proposed model decomposes an sEMG signal based on motion with good performance and demonstrate feasibility of motion estimation independent of sEMG interface rotation.

Skin impedance should be minimized to obtain reliable and precise surface electromyography (sEMG) signals. High skin impedance decreases sensitivity to muscular activation and makes sEMG signals vulnerable to external noise. Microneedle-based electrodes have been proposed to achieve low skin impedance and high spatial resolution. However, unstable skin contact can occur during dynamic motion due to the electrodes small contact area, and the signal is easily influenced by motion artifacts. In this study, a pneumatic microneedle-based high-density sEMG sleeve is proposed that guarantees stable sEMG signal measurement by pressing the electrodes to the skin. Pneumatic air control, sEMG signal processing, and wireless signal transmission are processed in a single processor. The proposed interface automatically controls the air volume according to sEMG signal quality and is comfortable for users. The usability of the proposed interface was compared to conventional Velcro armbands by examining acquired sEMG signals. The results indicated that the proposed pneumatic sleeve guarantees reliable sEMG signal measurement during dynamic motion. Additionally, optimal pneumatic pressure and needle length were investigated.

Sensory information from an Earth-fixed reference is necessary to guarantee a high localization accuracy of an unmanned underwater vehicle (UUV). However, the implementation of these sensors in an underwater environment is challenging because of signal uncertainties and strong signal attenuation. In this paper, we propose an underwater localization scheme with a sensor fusion of inertial navigation system (INS) and received signal strength of electromagnetic (EM) waves sensors. In the proposed sensor-fusion-based localization scheme, the UUV predicts its location by using INS based on dead-reckoning and corrects the predicted position by Kalman filter using EM waves sensor information when the UUV receives the signals of EM waves sensors in underwater wireless sensor networks. The proposed scheme enables localization with high accuracy and high sampling rate during a long-term task. The results of an experiment performed in a basin environment shows the feasibility of the proposed scheme. The scheme achieved reliable localization accuracy by comparing the pre-measured ground-truth position and long-term navigation. These results show the feasibility of exploiting EM waves attenuation as Earth-fixed reference sensors.

A position-based proportional-derivative (PD) controller with inner torque feedback loops can be used as a simple controller to render compliant behaviors to hydraulic robots. However, using this control framework involves a tradeoff between the stability and performance of the closed-loop system (which includes the external environment) because the joint position sensors and control units (i.e., electrohydraulic servovalves) used in the control are separated (non-collocated) from the robot by their joint flexibility. To effectively overcome this limitation, this paper proposes a joint position-based PD control strategy for constructing an inherently stable and compliant hydraulic robot system. To this end, we use virtual internal leakage to separate the system by frequency into subsystems that include control units (i.e., servovalve), flexible joints, and robot manipulator, respectively. This architecture allows the hydraulic robot to behave as a passive system within its operating frequency region under the guidance of controllers designed to guarantee the stability of the passive subsystems within the respective frequency regions. The proposed approach was verified experimentally.

There has been consistent trials to make a hydraulic actuator to be effective in interactive robot applications that require high power and durability. However, conventional strategies to obtain this goal, such as force/torque servo control or adding elastic components, suffer from complexity, sensor problems, and reduced system robustness. In this paper, we show that introducing backdrivability to hydraulic system may be an ultimate solution to constructing interactive hydraulic robot systems, by analyzing the advantages of backdrivability for this application. Moreover, we show that the backdrivability of valve-controlled hydraulic system can be achieved by adding a load pressure feedback mechanism on the spool of the servovalve, using the similarity of its dynamics structure to that of a flexible joint robot. Finally, we show that the derived backdrivable servovalve dynamics is actually the dynamics of the pressure control valve, and we report experiments that verify the analyzed advantages of backdrivability.

Position-based proportional derivative (PD) controllers are known to be able to render compliant behaviors to a robot, and they are usually used in conjunction with a friction compensator to improve control performance. Existing methods are effective when applied to actuation systems with mechanical transmissions; however, they cannot be applied to actuation systems that use fluid transmissions owing to the characteristics resulting from fluid parameters. To solve this problem, we propose a stability-guaranteed PD control method that incorporates two observers: one for observing friction and the other is for observing flexible joint effects due to fluid compliance and internal leakage. This allows robots with fluid transmissions to asymptotically converge to the desired position. We verified the proposed approach by conducting simulations and experiments.

Inertial microfluidics has facilitated promising applications of lab-on-a-chip devices for high-throughput biological analysis. However, as the mechanism of inertial microfluidics remains largely unknown, there are many difficulties in obtaining the desired equilibrium positions; hence, most researchers have tried to modify the channel geometry to obtain the desired equilibrium positions. In this paper, to improve the consistency of inertial focusing experiments, we apply feedback control to inertial focusing devices. The feedback control was realized by an image processing algorithm for real-time measurement of equilibrium positions using a high-speed vision system. The proposed image processing algorithm and feedback control scheme were verified through experiments.

The human hand shows complex motor skills and is widely used in activities in daily living. Thus, finger movement recognition has many potential applications in rehabilitation, tele-operation, and prosthetic hands. Several surface electromyography (sEMG) interfaces have been developed to recognize finger motion without impeding finger movement. However, conventional interfaces are impractical because they require a large skin contact area, which inconveniences users and restricts their clothing. In this paper, we propose a metal microneedle-based high-density (MNHD) sEMG interface for finger movement recognition, which is smaller than a traditional wet electrode. The highly selective extraction of the sEMG signal was demonstrated in static finger flexion tests and fingertip force estimation.

This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.

This paper proposes a simple model-free joint torque control strategy for hydraulic robots. The undesirable effect called “natural velocity feedback effect” is discussed by introducing mechanical impedance of the system. The proposed model-free joint torque control consists of internal-loop control and external-loop control (2 degrees of freedom (DOF)). Based on a mechanically represented hydraulic robot joint, the first-order internal-loop control is designed to reduce the mechanical impedance of a hydraulic robot joint in a passive manner. To improve joint torque tracking performance, a simple proportional-integral-derivative external-loop control is applied to the modified system by the internal-loop control. Robustness to external disturbance is demonstrated by transforming the proposed 2DOF control structure to the disturbance observer form. The proposed joint torque control strategy is validated on a 1DOF rotary hydraulic robot joint.

Previously, we proposed a scheme that determines the position of a remotely operated underwater vehicle (ROV) from the signal strengths of commercial radio-frequency sensors and antennas. This scheme provides accurate position information in a structured environment but is limited to two-dimensional (2D) environments because the radiation power of the antenna depends on the elevation angle between the sending and receiving antennas. To overcome this problem, we propose a 3D localization scheme that considers the electromagnetic (EM) wave attenuation over the range of reliable elevation angles. In order to determine the reliable elevation scope, we analyzed the radiation patterns of dipole antennas. The feasibility of our approach is demonstrated in distance estimation and 3D localization experiments by varying the distance and elevation angle. Encouraged by these results, we constructed an underwater wireless sensor network in the experimental basin, and performed ROV position tracking with the depth sensor. The scheme achieved reliable localization accuracy at a fast sampling rate, demonstrating the feasibility of exploiting EM wave attenuation in localization.

We propose a fluidic monostable multivibrator (fMMV) circuit, which is a new flow-switching mechanism in microfluidic chips. Flow control using parallel fMMV circuits does not require an external controller; this is a distinct advantage over various conventional micro-flow control methods. The fMMV circuit mainly consists of an inverter and an RC circuit. Each of the parts uses a microchannel as a resistor, a membrane chamber as a capacitor and a membrane valve as a transistor. The inverter acts as a trigger which opens the membrane valve and the RC circuit delays the closing of the valve, so that flow switching can occur over a time interval. A serial fMMV circuit can switch flows in multiple channels and regulate flowrates with pre-defined durations. By modifying the resistances in fMMV circuits, the flow rates and duration of each phase could be adjusted. For a proof of concept demonstration, we performed experiments using 3 parallel fMMV and 4 parallel fMMV circuit systems and these experiments show the applicability of fMMV circuit to various biochemical processes.

Admittance control is a well-established and popular control strategy in modern robotics. However, the standard admittance controller has several limitations. First, the passivity can be guaranteed by finding a set of control parameters that makes the admittance function positive real. However, this approach cannot be applied to multi degrees-of-freedom robot because it requires transfer function analysis. Second, standard admittance controller is not suitable when the system is exposed to unexpected environmental interaction (of which interaction force is not measured) due to the wall sticking effect. To overcome these limitations, this paper proposes an admittance controller of which structure can be constructed by feedback interconnection of passive subsystems. The proposed approach was verified using experiments and simulations.

This paper proposes joint torque servo control of an electro-hydrostatic actuator (EHA) for effective dynamic control of robot manipulators. In the control design, the fluid parameters are not considered to develop a simple low-level controller used for multi degree-of-freedom (DOF) robot manipulators. Instead, mechanical impedance, which represents the effect of joint velocity on joint torque, is reduced using a disturbance observer. The torque tracking performance of the modified system is then improved by a simple proportional-integral-derivative control. When evaluated on two 1-DOF EHAs, the proposed controller guaranteed robustness to external disturbances and variations in the fluid parameters, even though the fluid parameters are not considered.

Microneedle insertion is useful for elucidating the processes in living cells and their organelles. However, mechanical puncture by the needle causes traumatic damage to the cell. As a less invasive process, we have developed intracellular needle insertion using femtosecond laser cell ablation. To quickly and precisely position the spot ablated by the laser, we accurately located the needle tip in a 2D image plane. When the needle approaches the cell surface, the only accessible information is the microscope image; the vertical approach of the needle is unknown and must be detected. To this end, we propose an image process that integrates needle recognition, needle-tip localization, image focus measure, and vertical approach detection. The proposed image process was tested in 15 experimental sessions at various locations. The needle tip was reasonably and consistently positioned in the image, and the vertical approach was detected within the safe and plausible ranges.

We developed a ‘hybrid actuator system’ to precisely control the position of a micro-particle at high speed in a microfluidic chip. The hybrid actuator system benefits from two actuator systems by combining an electroosmotic flow generator and pressure-driven flow induced by a piezoelectric actuator-driven syringe pump system. We also implement an image processing algorithm using a high speed vision system and design a supervisory controller for the hybrid actuator system to apply it to a microfluidic system. The proposed system can be used to overcome current limitations of conventional pump systems for microfluidic devices.

Dynamic motion of human shows kinematic aspects related to storing elastic energy in skeletal muscle. This results from joint stiffness modulation and as a consequence, countermovement which is opposite to the intended motion is observed. We propose a segmentation algorithm based on a hidden semi-Markov model that infers dynamic motion phases probabilistically from sEMG observations during countermovement jump. In addition, parameter re-estimation of both left-right state transition and restriction of state duration is applied to reduce frequent state transition due to large variation of sEMG observation probability. In experiments, the segmentation of motion phases using sEMG identified the phases of the vertical position of torso successfully and the parameter re-estimation reduced both the error rate and the transition occurrence.

A series elastic actuator (SEA) consists of a system with an intrinsically compliant mechanism, and introduces the possibility of external force/torque sensing and control. However, this mechanism is also known to reduce torque servo-control bandwidth and to separate link-side dynamics from actuator-side dynamics. The conventional solution to overcoming reduced actuation bandwidth and separated link motion problems is to increase the control gain, which is limited by stability problems and actuator saturation. Here, we demonstrate that treating the actuator side as a near-ideal position source gives the compliance-independent actuation bandwidth of an SEA, so that separated link-side motion can be simply but effectively compensated. Based on these observations, we developed an SEA force/torque servo controller that consists of actuator-side robust motion control and link-side motion feedback. We performed comparison experiments with conventional cascaded two-loop SEA torque servo control using a rotational flexible-joint test-bed, and the results demonstrated that the proposed control strategy yields a wider bandwidth and more robust link motion.

We propose a novel flow control method to realize flow regulation and valveless flow switching in a microfluidic network. The proposed controller enables precise regulation of flow rates (or velocity fields) in micro-channels regardless of model uncertainties such as cross-sectional area and microchannel configuration. The controller can also minimize the pressure exerted on the microfluidic chip. The proposed system can dramatically improve performances of flow regulation and can overcome the limitations of conventional pump systems for microfluidic devices.

This paper presents a novel indicator for lump detection that uses only contact pressure distribution, as well as its feasibility evaluation. The conventional lump detection methods are based on a pressure map obtained from contact pressure distributions, but they requires an adequate regulation of the contact condition during measurements. An alternative approach is to use a stiffness map, for which displacement measurement is necessary using additional devices or precise motion control. The proposed indicator relies on only the difference between the normal force and the surface normal obtained from contact pressure distributions. Thus, contact condition or probe motion does not affect the performance of the proposed method. The feasibility of the proposed method was evaluated by simulations and experiments, both of which showed promising results.

Carrying unknown heavy payload is one of the classical problems in robotics. To realize heavy payload capability, the use of high reduction gears, which causes large motor-side inertia and large friction, is inevitable. This paper proposes a disturbance observer (DOB)-based approach that (1) reduces highly amplified motor-side inertia (2) compensates for large friction (3) compensates for the unknown heavy payload without sensory information. However, in principle, the DOB is applicable only for linear system. To overcome this, a concept of effective joint torque is introduced, which allows us to separate linear motor-side dynamics from the nonlinear robot dynamics. As a result, it becomes possible to apply high order DOB to compensate for the uncertainties. Mathematical analysis shows closed-loop stability, optimality, and passivity. Experiments and simulations are performed to verify the method.

We propose a scheme that uses the signal strength received from commercial RF sensors to determine the position of a sensor underwater. The scheme can give accurate position information and has fast update rate, but is limited to short sensor range with the signal identification problem. The infrastructure-based localization and a method that assigns frequency bands to RF sensors can be a solution to overcome this range limitation without loss of update rate or position accuracy. The RF sensor modules are used as ANs, and are easily identified by their channel assignments. To verify the proposed localization scheme, we organized an experimental environment and performed repetitive experiments to track the positions of mobile nodes and stationary nodes in 2D environments. The experimental results showed reliable localization accuracy with fast sampling rate and demonstrates the feasibility of localization that exploits EM wave attenuation.

This paper proposes a robust PD control scheme for flexible-joint robots based on a disturbance observer (DOB). In this paper, the DOB is applied only to the motor-side dynamics of the robot, and the uncertainties on the motor-side are successfully eliminated. It is shown that the proposed DOB-based approach guarantees global asymptotic stability. To this end, two special treatments are required. First, unlike the typical configuration of the DOB, nominal states of the motor-side are fed back to the PD controller. Second, a control input that makes the nominal states stable is additionally introduced. The proposed approach was verified using multi-degree-of-freedom experiments.

This paper proposes a framework called nonlinear robust internal-loop compensator that enables us to bring nonlinear H∞optimality to robot controllers in a unified and simple way. Using the framework, a controller designed for the nominal plant can achieve additional robustness by simply adding PID-type auxiliary input to the original control law. Robust performance is guaranteed by the nonlinear 1-1 optimality and robust stability is guaranteed by proving the extended disturbance input-to-state stability. Moreover, the framework preserves the passivity property of the original controller. Finally, the performance bound can be predicted and leads to the gain tuning rules. By virtue of the tuning rules, the performance can be tuned using only a single variable. The proposed method was validated through the simulations and experiments.

In this paper, we propose a visual place recognition algorithm which uses only straight line features in challenging outdoor environments. Compared to point features used in most existing place recognition methods, line features are easily found in man-made environments and more robust to environmental changes such as illumination, viewing direction, or occlusion because they are more likely to be extracted from structures. Candidate matches are found using a vocabulary tree and their geometric consistency is verified by a motion estimation algorithm using line segments. The proposed algorithm operates in real-time, and it is tested with a challenging real-world dataset with more than 10,000 database images acquired in urban driving scenarios.

This paper proposes a robust control scheme based on a disturbance observer (DOB) for flexible joint robots. In this paper, the DOB is applied only on the motor-side dynamics of the robot, and the uncertainties on the motor-side are successfully eliminated. It is shown that, to guarantee the stability, the estimated motor-side position should be fed back into the controller, which is different from usual setup of the DOB. The experiments/simulations show that the estimated signal feedback indeed guarantees the stability, whereas the measured signal feedback does not.

This paper proposes a contact force decomposition method using tactile information for haptic augmented reality (AR). Haptic AR modulates real haptic interaction to desired interaction by adding virtual haptic feedback. For haptic AR rendering, the measurement of real haptic interaction including deformation and friction force is important for accurate rendering, but the current haptic AR algorithms do not support it properly. The proposed method, derived from a simple model of rigid-deformable body contact, decomposes contact force into deformation force and friction force using force and tactile information. A finite element method (FEM) based simulation of contact between two objects was performed for the performance verification of the proposed method. The results showed the proposed method can decompose contact force more exactly than the conventional decomposition methods in haptic AR.

We propose a method to decode surface electromyogram (sEMG) data into the dynamic state that delivers the characteristics of dynamic motor control strategy of humans. First we propose the clustering and the segmentation of the dynamic state by using a Bayesian mixture of Gaussian model (Bayesian MoG) in the augmented space of both hand position and sEMG. Second, we introduce a hidden semi-Markov model (HSMM) to decode sEMG into the dynamic state similar as much as with the segmented result without hand position. Experimental data were collected to train both Bayesian MoG and HSMM, and cross-validation between segmentation and the decoding result were performed to verify the decoding accuracy of the HSMM. Finally, we verified that the decoding results successfully extracted a dynamic motor control strategy from sEMG data.

Since a flexible-joint robot (FJR) consists of two subsystems centered around joint-torque (J-T) sensors, there are two independent resultant torques, i.e., an actuating motor torque and an external link torque, applied to the system. In this paper, an external torque sensing algorithm for the FJR is proposed to estimate both torques simultaneously by using the disturbance observing property of the disturbance observer (DOB). The proposed algorithm has no restriction of selecting Q-filter such that a high-performance low-pass filter can be applied for accurate estimation. The estimated torques can be very useful for FJR applications. As an illustrative example, the estimated actuating motor torque containing the motor disturbance is utilized to the motor disturbance compensation of the FJR with modified Q-filter due to the compensation feedback loop. The basic structure of the proposed algorithm is illustrated, and the performance is verified though a custom-designed experimental testbed for a vertical configuration with a gravitational load.

In this paper, we discuss a novel underwater localization system using EM(Electro-Magnetic) wave. Maxwell equation and Friis transmission formula were reconstructed in terms of propagation constant and antenna property, then by combining these two formulas, we have defined the EM wave attenuation function over underwater distance. Using this function, we have developed an underwater sensor model for EM waves. Finally we organized an experimental environment and performed experiments for different frequencies, and through 2D localization using obtained sensor model, we meet with very confident results. The experimental results show a close correlation with the developed sensor model and the possibility of underwater localization systems using EM wave.

In this paper, nonlinear H∞optimal design of impedance controllers is proposed based on the nonlinear robust internal-loop compensator (NRIC) framework. Simply adding PD-type auxiliary input to the original control law, the robust performance and the robust stability are achieved. Nonlinear H∞optimality is guaranteed by solving Hamilton-Jacobi-Isaacs (HJI) equation and the disturbance input-to-state stability (ISS) is guaranteed by finding ISS-Lyapunov function. Moreover, it is shown that the proposed method preserves the passivity of the impedance controllers. The proposed method can be applied to various types of impedance controllers in a unified way. Through simulations and experimental studies, the proposed method is verified.

The scan matching is widely used for localization and mapping of mobile robots. In this paper, a direction of data point in the scan is approximated and this is incorporated into the scan matching algorithm to improve the performance. The direction of data point is the normal direction of the least squares fitted line based on neighbors of the data point. Owing to this incorporation, the performance of the scan matching can be improved. The number of iterations decreases, and the tolerance against a large relative-rotation between scans increases. Using real sensor data of a laser range finder, experimental results verify the performance of the proposed algorithm, the direction augmented probabilistic scan matching.

This paper describes a practical approach to solve a path planning problem in a home environment. The proposed approach incrementally constructs the hierarchical roadmap which has a multi-layered structure using a sonar grid map when a mobile robot navigates in unexplored area. The hierarchical roadmap can almost completely cover the traversable areas in the environment. The mobile robot path planner using the hierarchical roadmap can efficiently search for appropriate paths under the limited computing power and time by reducing the search space size. The benefits of the hierarchical roadmap representation were verified by experiments in a home environment.

One of the main reasons for using a joint torque sensor in a robot is to measure body forces acting on the system. Especially, the motor actuating torque and the external link torque are important for a flexible joint control. However, it is difficult to measure or estimate those two torques simultaneously with a single joint torque sensor only due to the lack of information. In this paper, Kalman Filter based two-port Body Force OBserver(KF 2-port BFOB) was proposed to estimate two output torques from the motor-side port and the link-side port simultaneously using the two-port system dynamics, the joint angle and the sensor torque from the joint torque sensor based on the Kalman filter. The basic idea of KF 2-port BFOB is illustrated and its performance is verified by implementing the algorithm with disturbance observer(DOB) in the motor actuated one-DOF flexible joint robot. The experiments are executed in the planar situation (under the gravity-free condition) and in the vertical situation(under the gravity condition) respectively to verify the performance of KF 2-port BFOB. The results show something with conclusion.

Robustness is a very classical issue in the robotics field. Disturbance observer (DOB) can be a good choice to improve the robustness of the system. It is easy to implement and shows successful results. DOB, however, cannot bring nonlinearity of the system into the formulation. To overcome this problem, nonlinear robust internal loop compensator (NRIC) is proposed in this paper. NRIC is attached to a existing controller and improves robustness of the system like DOB. NRIC makes an auxiliary input that compensates the difference between the output from the real plant and that from the nominal model. An auxiliary input is designed in a nonlinear ℋ∞optimal control framework and the resulting input is given as a simple PID form. The stability of a real plant is guaranteed if a controller is designed as an exponentially stable one (a sufficient condition). The benefit of NRIC is that it is easy to implement and it effectively brings nonlinearities of the system into the formulation successfully. The performance of NRIC is verified through the simulation and experiments.

This paper addresses the limitation of the conventional Rao-Blackwellized unscented particle filters. The problem is on the usage of the overconfident optimal proposal distribution caused by perfect map assumption, so that predictive robot poses are sampled from the underestimated error covariance in the particle filtering process. The proposed solution computes more precise error covariance of the robot which contains uncertainties of the robot, map, and measurement noise. Experimental results using the benchmark dataset confirmed that the covariance of the proposed method is always larger than that of the conventional method while inducing slower increasing rate of the weight variance with less resamplings.

In this paper, an advanced preoperative planning framework for spinal fusion is presented. The framework is based on spinal pedicle data obtained from computed tomography (CT) images, and provides optimal insertion trajectories and pedicle screw sizes. The proposed approach begins with a safety margin estimation for each potential insertion trajectory that passes through the pedicle volume, followed by procedures to collect a set of insertion trajectories that satisfy operation safety objectives. Among the trajectory candidates, the insertion trajectory, which maximizes the insertable depth of a pedicle screw into the vertebral body, is then chosen as optimal, because the insertable depth enhances the strength of the transpedicular screw-vertebra interface after spinal fusion surgery. The radius of a pedicle screw was chosen as 70% of the pedicle radius. This framework has been tested on 68 spinal pedicles of 8 patients requiring spinal fusion. It was successfully applied, resulting in an average success rate of 100% and a final safety margin of 2.11±0.17mm.

This paper presents a method of topological localization in a home environment using only low-cost sonar sensors. The proposed method utilizes a relative motion model to obtain a prior information of node probability. Moreover, the size of a local gridmap used for gridmap matching is determined adaptively by an entropy test of node probability. The proposed method results in a reliable and convergent localization result even under the uncertainty and sparsity of sonar sensros. Experimental results verify performance of the proposed topological localization in a real home environment.

In bilateral teleoperation system, conventional position sensor based force reflection method, known as position error feedback, may generate inaccurate force reflection directions, when motion of a slave robot is constrained by unexpected obstacles and link collisions. Restriction Space Projection method is a novel position sensor based force reflection framework that was proposed to address this issue. It provides accurate force reflection in unstructured environments when motion of a slave robot is constrained by unexpected obstacles and link collisions, regardless of kinematic dissimilarity between the master and slave manipulators of the bilateral teleoperation system. This paper discusses the applications and limitations of the Restriction Space Projection method through examples.

In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.

In this paper, we present a global localization and local pose error compensation method in a known structured environment using magnetic landmarks. In previous our research, it was possible to compensate the pose error (xe, ye, qe) of a mobile robot correctly on the surface of structured environment with magnetic landmarks. In this work, we propose a methodology of arranging magnetic landmarks on the map such that properly arranged magnetic patterns ease the global localization of a mobile agent. Among total six patterns of magnetic-bar in square arrangement, five unique landmarks are obtained. Therefore, a heuristic pattern search method is applied to build the virtual map using five landmarks. In order to obtain the global pose information, the robot identifies the pattern of magnets, and obtains the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. Experimental results show the effectiveness of the magnetic-pattern landmarks for the global localization and local pose control of a mobile robot.

In the paper, the new force control, which is called the D'Alambertian force Error based Force Integral Control (DEFIC), is proposed based on the force integral control by considering the ability of the system input. From the system input point of view, the external force and system acceleration cannot be distinguished from each other. Thus, the best solution is to handle those quantities together. DEFIC is motivated from this observation. The external disturbance robustness and the plant/model mismatch compensation of DEFIC are explained based on the passivity-based control and Disturbance OBserver(DOB). Furthermore, it is illustrated how DEFIC can be extended to n-DOF manipulator control. The performance of DEFIC is verified with simulations, and the conclusion is followed directly.

This paper presents a method of topological representation and classification in home environment using only low-cost sonar sensors. Approximate cell decomposition and normalized graph cut are applied to sonar gridmap to extract graphical model of the environment. The extracted model represents spatial relation of the environment appropriately by segmenting several subregions. Moreover, node classification is achieved by applying template matching method to a local gridmap. Rotational invariant matching is used to obtain candidate location for each node and the true node can be classified by considering detail distance information. The proposed method extracts well-structured topological model of the environment and classification also results in reliable matching even under the uncertain and sparse sonar data. Experimental results verify the performance of proposed environmental modeling and classification in real home environment.

State estimation problem with time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in a filter. A filter not considering this time delay cannot be used since a current measurement is related with a past state. These delayed measurements problem is solved with augmented state Kalman filter, and uncertainty of the delayed time is also resolved based on the probability distribution of the delay. The proposed method is analyzed by a simple example, and its consistency is verified.

In this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict-evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.

This paper presents a method of topological modeling in home environments using only low-cost sonar sensors. The proposed method constructs a topological model using sonar gridmap by extracting subregions incrementally. A confidence for each occupied grid is evaluated to obtain reliable regions in a local gridmap, and a convexity measure is used to extract subregions automatically. Through these processes, the topological model is constructed without predefining the number of subregions in advance and the extracted subregions are guaranteed the convexity. Experimental results verify the performance of proposed method in real home environment.

This paper proposes an adaptive node sampling method for the probabilistic roadmap (PRM) planner. The proposed method substitutes the random sampling in the learning phase of the PRM planner and improves the configuration of the roadmap. This method uses two phase to determine nodes in order to construct the roadmap. First, the proposed method extracts initial nodes using the approximated cell decomposition and the Harris corner detector. Second, the positions of these nodes are optimized using a construction process of the centroidal voronoi tessellation (CVT). The proposed method determines the adequate number and positions of the nodes to represent the entire free space, and the PRM planner based on the proposed method finds out efficient paths even in narrow passages. These properties have been verified though experiments.

This paper investigates a novel position-sensor-based force reflection framework for multi-degree-of-freedom (DOF) bilateral teleoperation systems in unstructured environments. The conventional position-sensor-based force reflection method, which is known as position error feedback, may generate grossly inaccurate force reflection directions during collisions involving the slave manipulator links. The proposed restriction space projection framework calculates the instantaneous restriction space to provide the accurate force reflection, regardless of kinematic dissimilarity (KDS) conditions of bilateral teleoperation systems. Simulation results confirmed the validity of the proposed framework in a KDS bilateral teleoperation system under various constraint conditions.

In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors and does not require adjustment of parameters for various environments.

There are two main limitations in the conventional robot-assisted spinal fusion surgery. Since the end effector in the state of art has a role of guiding the insertion pose of a screw only, i) convenience that can be obtained when the robot intervenes in the surgery more actively could be limited, ii) The insertion pose of a screw provided by the robots could be deteriorated by surgeon's resisting force since he should insert a screw with his own hand withstanding the large reaction force transmitted through the drilling handle. To overcome those limitations, this paper proposes a novel approach for spinal fusion, wherein the robot performs the spinal fusion using the equipped end effector following surgeon's guide. We developed a dexterous small-sized the end effector that can perform previous gimleting and screwing tasks into the vertebrae. A five-DOF robot body that has kinematically-closed structure guides the insertion pose of a screw and resists strong reaction force firmly during the screwing process. Based on admittance control framework, the surgeon controls the pose of the end effector precisely to compensate induced static/dynamic errors during the operation. A torque feedback method without torque sensor that suggests the haptic information about the status of drilling is also included. The performance of the CoRASS was verified by experiments.

Odometry calibration is a first and essential step to do for a successful navigation because most of control algorithms are based on odomety information. Odometry error can be categorized as systematic and non-systematic error. In this paper, we suggest a novel method to calibrate systematic error using inherent home positioning capability of home cleaning robot. The method is designed for a differential drive type and take advantage of Augmented extended Kalman Fil- ter(AKF) Algorithm to estimates systematic error parameters. Our approach has both characteristics of on-line and off-line. By simulation and experiment, we evaluate the method and the result shows that the proposed method gives odometry error reduction by several times.

In this paper, coordinated motion control of Underwater Vehicle-Manipulator System (UVMS) is addressed. In order for UVMS to carry out manipulation tasks alone, motion planning with consideration of redundancy and tracking control under disturbances are required. We propose redundancy resolution with optimization of restoring moments as motion planning and inverse optimal nonlinear H℞control as robust tracking controller. Numerical simulations are presented to demonstrate performance of proposed coordinated motion control of UVMS. As a result, control input for tracking is reduced and UVMS can track generated trajectories under parameter uncertainties.

The Rao-Blackwellized particle filtering SLAM (RBPF-SLAM) still suffers from a large-loop or nested-loop closure due to a lack of hypotheses. This makes the filter optimistic and results in the RBPF to be a short term solution. This paper provides an important insight to the current resampling scheme and proposes an approach to delaying the resampling decision until the loop closing. A discussion about the issue of preserving the importance weight is also presented with developing the weight-smoothing method. This approach is a necessary step for the RBPF-SLAM, but to the best of our knowledge, no literature deals with this concept. Since the delayed resampling maintains the possible hypotheses, the filter consistency is significantly improved. Moreover since the resampling takes the loop closure into account, the required number of particles can be proportional to the loop size of the environment. The computational burden, caused by the resampling process, is also drastically reduced due to the delaying decision. Experimental results in large-scale environment with large and nested loops illustrate the effectiveness of our approach.

We present robust localization algorithm using particle filter for mobile robots. The blueprint of building that cannot completely represent the real environment or a simple map of environment can be used in this work. With this incomplete information, it will not be easy to estimate the pose of robots because un-modeled obstacles will make the sensor observation inconsistent. Proposed algorithm provides robustness in such a dynamic environment because the effect of corrupted sensor observations can be suppressed by a selective update or a sampling from non-corrupted window. Results of experiments and simulations show the robustness of proposed algorithm.

We present a novel filtering method for building a grid map using sonar sensors. Recognizing that conflicting information from sonar sensor readings is the main reason of poor map quality, we established a proposition that put the conflicting information in order. Based on the proposition, we effectively filtered sonar readings that cause conflict. In addition, some information of filtered readings were not discarded but reprocessed to enhance the quality of the map. We call this filtering and reprocessing operation the conflict evaluation method. We evaluated the proposed method by experiments under various conditions.

The Rao--Blackwellized particle filter (RBPF) and FastSLAM have two important limitations, which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. These can make the filter inconsistent. Another challenge is to reduce the number of particles while maintaining the estimation accuracy. This paper provides a robust new algorithm based on the scaled unscented transformation called unscented FastSLAM (UFastSLAM). It overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations. This approach improves the filter consistency and state estimation accuracy, and requires smaller number of particles than the FastSLAM approach. Simulation results in large-scale environments and experimental results with a benchmark dataset are presented, demonstrating the superiority of the UFastSLAM algorithm.

This paper presents an algorithm for visual SLAM based on a visual plane, a reliable grouping of salient visual features along sonar line features. The grouping of visual features improves data association and reduces the number of landmarks against individual visual features. To accomplish this, we propose three techniques: 1) selection of visual features which are invariant to image changes in indoor environment and suitable candidates for the visual plane, 2) extraction of sonar line features with current sensor data, which filters out uncertain outliers efficiently and 3) a scheme on grouping visual features with respect to sonar line features and maintaining database of the extracted visual planes for reliable data association. We integrate above three techniques into one framework and propose a SLAM algorithm for the visual planes. Experimental results in two types of real home environment show that the algorithm can successfully be executed with no human intervention.

This paper addresses the problem of Simultaneous Localization and Map Building (SLAM) using a Neural Network aided Extended Kalman Filter (NNEKF) algorithm. Since the EKF is based on the white noise assumption, if there are colored noise or systematic bias error in the system, EKF inevitably diverges. The neural network in this algorithm is used to approximate the uncertainty of the system model due to mismodeling and extreme nonlinearities. Simulation results are presented to illustrate the proposed algorithm NNEKF is very effective compared with the standard EKF algorithm under the practical condition where the mobile robot has bias error in its modeling and environment has strong uncertainties. In this paper, we propose an algorithm which enables a biased control input in vehicle model using neural network.

This paper presents a quantitative comparison framework for bilateral teleoperation systems which have different dynamic characteristics and sensory configurations for a given task dependant performance objective, mu-synthesis is used to develop the framework since it can efficiently treat systems containing uncertainties and disturbances. The framework consists of i) a feasibility test, and ii) a comparison methodology using prioritized task dependent performance objectives. This framework is applied to a bilateral teleoperation system including an uncertain human operator and environment in a practical case study. The validity of the proposed quantitative framework is confirmed through experiments. The proposed framework can be used as a tool to design bilateral teleoperation systems, especially when there are constraints in designing drive mechanisms and choosing sensory configurations.

In order to provide improved convenience for a surgeon in spinal fusion surgery, a robot system should i) closely engage in surgeon's operation using an end effector, and ii) protect the surgeon from being exposed to harmful radiation due to repeated shootings of fluoroscope. This paper proposes a bilateral teleoperation system for spinal fusion, BiTESS-II, to accomplish the goals. We developed an end effector that can substitute the surgeon's manual operation and a novel closed-loop type slave robot that can exert strong reaction force to complete gimleting and screwing tasks. Master devices are used to control the position and orientation of the slave robot and to generate haptic information identical to that of the slave side. A novel force reflection method without force sensors allowed to design the end effector simple and light. BiTESS-II is among the first human guided teleoperation system for spinal fusion with an adequate end effector. The performance of the BiTESS-II was verified by experiments.

Rao-Blackwellized Particle Filter and FastSLAM have become popular tools to solve the Simultaneous Localization and Mapping (SLAM) problem. However, the above techniques have two important potential limitations, which are the derivation of the Jacobian matrices and the linear approximations to the nonlinear functions. Also, one of the major challenges of both Rao-Blackwellized particle filter and FastSLAM is to reduce the number of particles while maintaining the estimation accuracy. This paper proposes a new algorithm based on unscented transformation called Unscented FastSLAM (UFastSLAM) that overcomes important drawbacks of the Rao-Blackwellized particle filter frameworks by directly using nonlinear relations. Experimental results in large scale environments are presented that demonstrate the effectiveness of the UFastSLAM algorithm over the previous approaches.

Our previous work, SLAM with visual plane, allowed correct data association and computationally feasible solution for vision-based SLAM. In this paper, we propose a scheme of generating a hybrid visual map based on the visual plane framework. The hybrid visual map has two levels of map representations: 1) absolute map representation of distinctive visual planes via EKF-SLAM and 2) relative map representation of dense visual features for each visual plane via sparse information filter update. It can inherit the advantages of the visual plane by maintaining the absolute map. Moreover, the relative map can reconstruct a 3-D map of visual features efficiently without loosing dense visual information. The performance of the proposed method was verified by the experimental results of consistent hybrid visual maps in two real indoor environments.

In this paper, redundancy resolution of underwater vehicle-manipulator system(UVMS) is addressed. In general, UVMS has redundant degrees of freedom(DOFs) as many as DOFs of manipulator and these redundant DOFs can be used to optimize the configuration of UVMS while satisfying given tasks. We propose a performance index for redundancy resolution which minimizes the restoring moments of UVMS. The restoring moment is induced from gravity and buoyancy forces and it can cause unintentional change of poses of UVMS. If the restoring moments remain small, control effort for keeping the poses of UVMS decreases. This means that energy consumption can be reduced by minimizing the restoring moments during conducting tasks. We solve inverse kinematics of UVMS using well-known weighted pseudo-inverse matrixes and we optimize the restoring moments by adopting gradient projection method. Numerical simulations are presented to demonstrate performance of the proposed algorithm.

As robotics research is directed towards intelligent robot behavior, mobile robots need a capability to explore known / unknown environments autonomously. The ability of the autonomous navigation begins with a path planner local and global. In this paper, we address a new local path planner using a Navigable Voronoi Diagram(NVD) which comes from the Voronoi Diagram(VD). The characteristics of the NVD are safety, smoothness, fast reaction and so on. The NVD is constructed from sonar sensor readings and is integrated with a cost function. Another important advantages of the NVD is that it can drastically reduce the uncertainty of cheap sonar sensor readings due to its nature. The proposed local path planner based on the NVD has been successfully implemented on a Pioneer 3 equipped with 12 sonar sensors and its advantages were verified by 2 experiments in different indoor environments.

This paper presents a quantitative comparison framework for bilateral teleoperation systems (BTSs) that have different dynamic characteristics and sensory configurations for a given task-dependent performance objective (TDPO).$\mu$-synthesis is used to develop the framework since it can efficiently treat systems containing uncertainties and disturbances. The framework consists of: 1) a feasibility test and 2) a comparison methodology using prioritized TDPOs. As the formulation used is based on$\mu$-synthesis, the system, operator, and environment models are represented in the form of linear nominal models with frequency-dependent multiplicative uncertainties. This framework is applied to a BTS including an uncertain human operator and environment in a practical case study. The validity of the proposed quantitative framework is confirmed through experiments. The proposed framework can be used as a tool to design BTSs, especially when there are constraints in designing drive mechanisms and choosing sensory configurations.

This paper proposes restriction space projection (RSP) method to generate accurate direction of force reflection when a bilateral teleoperation system (BTS) is kinematically dissimilar. Through examples, it is shown that the previous force reflection methods are not applicable to the kinematically dissimilar BTSs. Two kinds of RSP methods using a novel concept, instantaneous restriction space (IRS), are implemented for impedance and admittance type of BTSs. Especially, new developed obstacle avoidance algorithm using the redundancy of the slave manipulator makes the RSP method applicable to all kinds of kinematically dissimilar BTSs. Experiments verify that the RSP method is powerful to describe the restriction space at the slave side without force sensors

To improve the maneuverability of underwater vehicles, accurate thruster control is required based on a precise thrust model. Especially, in real systems, the ambient flow velocity and angle effects on the thrust force are very important, because vehicles are continuously moving and/or hovering with thruster actions. In this paper, we execute preliminary experiments with the model containing ambient flow effects as CAR (critical advance ratio) and CIA (critical incoming angle). The distinguished characteristics of the above model is the three axial flow states classification. The whole thrust map is divided into three states according to the state of ambient flow velocity, angle, and propeller shaft velocity. The several controllers are tested using the three flow states information. The preliminary experimental results show the best performance can be obtained by the open loop control with accurate model, because the thrust force cannot be measured directly, so the force map from the propeller shaft velocity to thrust force plays important roll in control performance. However, the DOB (disturbance observer) with closed loop controller is realistic solution, because it can compensate various unknown disturbances in real systems

This paper addresses a new feature map building method that can minimizes the appearance of phantom features by using only sparse sonar data. The approach is composed of extraction of features and building a probability grid map using only the footprint of sparse sonar data, estimation of position uncertainty of the feature, and evaluation of the reliable features. A virtual circle association frame model has been developed, which associates two sonar footprints into a virtual circle frame. Using this model, the geometric primitives such as lines, points, and arc features are separately extracted. While extracting the features, a grid map is also built using the orientation probability approach. The position uncertainty of each extracted feature is, then, estimated by considering both the position uncertainty of the robot and the measurement uncertainty of the sonar sensor. Finally, the reliable features among all extracted ones are evaluated from grid association method. The proposed methods have been tested in a real home environment with a mobile robot

Reliable data association is crucial to localization and map building for mobile robot applications. For that reason, many mobile robots tend to choose vision-based SLAM solutions. In this paper, a SLAM scheme based on visual object recognition, not just a scene matching, in home environment is proposed without using artificial landmarks. For the object-based SLAM, the following algorithms are suggested: 1) a novel local invariant feature extraction by combining advantages of multi-scale Harris corner as a detector and its SIFT descriptor for natural object recognition, 2) the RANSAC clustering for robust object recognition in the presence of outliers and 3) calculating accurate metric information for SLAM update. The proposed algorithms increase robustness by correct data association and accurate observation. Moreover, it also can be easily implemented real-time by reducing the number of representative landmarks, i.e. objects. The performance of the proposed algorithm was verified by experiments using EKF-SLAM with a stereo camera in home-like environments, and it showed that the final pose error was bounded after battery-run-out autonomous navigation for 50 minutes

To increase the intelligence of mobile robot, various sensors need to be fused effectively to cope with uncertainty induced from both environment and sensors. Combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency vision based SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes: extracting robust point and line features from sonar data, and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. Fusing sonar features and visual objects through EKF-based SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in home environment. The performance of the proposed algorithm was verified by experiments in home environment with dynamic obstacles

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information

This paper describes a new sensor system for improving the accuracy of the range information using multiple IR range sensors. Environment and obstacle sensing is the key issue for mobile robot localization and navigation. Laser scanners cover 180deg and are accurate but too expensive. Radial range sensors such as laser scanners, IR scanners and ultrasonic range sensor rings have blind spots so that a small obstacle not close enough to the sensor may be easily missed. It is necessary to develop a low cost sensor system which covers 360deg with small blind spots. A sensor system with 12 IR range sensors is designed and implemented. The problem of the 2D sensor systems is, they measure the nearby obstacles on a plane where the sensors are located. In order to scan the environment vertically, a 2.5D sensor system is designed and the experimental result shows the effectiveness of the proposed sensor system

This paper presents a practical algorithm for topological navigation in corridor environment using cheap sonar sensors. Topological navigation consists of topological mapping and obstacle avoidance navigation. In this environment, it has two major problems which are 1) identifying nodes under globally similar but locally slightly different shape of corridor environment for topological mapping and 2) obstacle avoidance navigation using cheap sensors. In order to detect nodes robustly, we define the eigenvalue ratio (EVR) which converts geometrical shape of the environment to a quantitative value based on the principal component analysis (PCA). This method is demonstrated by simulation and experiments to be robust, with the notable detail that at a given corridor intersection the method can detect a cluster of nodes defined in this manner. For safe obstacle avoidance navigation, we introduce circle following (CF) algorithm which uses geometry of environment and characteristics of sonar sensors. Experiments and simulation validate reliability of the CF algorithm

Edges in topological maps have rich information such as shape, numbers of obstacles, locations of obstacles which can be used for a data association. However, no systematic approach on using the edge data for the data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by 27-28 times for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations and experiments show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.

A localization algorithm for topological maps with dynamics is proposed in this paper. Especially, this algorithm considers a localization when some nodes are deleted by door closing and has two main features. First, lots of edge information are used in efficient way. Second, the algorithm calculates probability of a node to be the current location in a systematic and general way. Experiments in a topological map with dynamics are conducted to show the performance of the proposed algorithm.

The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. However, an accurate and practical thruster model has not been utilized yet. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within ± 2N force error. The comparison results show that the new model’s matching performance is significantly better than conventional models’.

In order for stable control of humanoid robots, ground contact forces should be properly controlled for compensating the dynamic disturbances caused by unactuated body movement. The stability in the sense of the zero-moment point (ZMP), guaranteeing secure contacts during control, is a necessary condition for stable motion control. Therefore, we propose a method to control the ground interaction at the ZMP, or ZMP interaction in short, by modifying the system reference acceleration. We also show that simultaneous control of the ZMP interaction and the body movements is not allowed in general. Simulation result is provided to corroborate the theoretical result.

Sonar sensor is an attractive tool for the SLAM of mobile robot because of their economic aspects. This cheap sensor gives relatively accurate range readings if disregarding angular uncertainty and specular reflections. However, these defects make feature detection difficult for the most part of the SLAM. This paper proposes a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method is based on the TBF (Wijk and Christensen, 2000) scheme. Moreover, three additional processes improve the performance of feature detection as follows; 1) stable intersections; 2) efficient sliding window update; and 3) removal of the false point features on the wall. The line feature detection method is based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors give similar range readings. Using this sensor property, we propose a novel algorithm for line feature detection, which is simple and the feature can be obtained by using only current sensor data. The proposed feature detection algorithm gives a good solution for the SLAM of mobile robots because it gives an accurate feature information for both the point and line features even with sensor errors. Furthermore, a sufficient number of features are available to correct mobile robot pose. Experimental results of the EKF-based SLAM demonstrate the performance of the proposed feature detection algorithm in a home-like environment.

This paper proposes an accurate kinesthetic haptic display method for a multi-DOF haptic interface. Position-position (p-p) architecture plays an important role in a haptic interface like four channel architecture since force reflection using only force sensor information induces a serious problem if a slave manipulator is constrained by unexpected obstacles the force sensor cannot detect. However, the conventional p-p architecture in literature has limitations to apply to a multi-DOF haptic interface. This paper indicates the limitation through an example and proposes a novel haptic display method using instantaneous restriction space (IRS). IRS can be calculated using Jacobian and joint angle error of a slave manipulator. Since the proposed method has the form of impedance two port architecture in the sense of data flow, it can be easily combined with the previous results of two-port haptic display framework in literature. The method is compared to the conventional p-p architecture through experiments.

To improve the maneuverability of underwater vehicles, accurate thrust model is required. However, its modeling is not only difficult but also inaccurate, because it has highly nonlinear dynamics and many parameters which cannot be modeled exactly. Generally, a propeller's steady-state axial thrust is proportional to the signed square of propeller shaft velocity under bollard pull condition. And, it is reported that the velocity of the ambient flow of the thruster affects the thrust force largely. From these facts, we can easily make a reasonable inference that the thrust force may also be affected by the thruster spouting angle with respect to the vehicle moving direction. However, the effect of this non-parallel ambient flow is not well understood in the previous works. Hence, in this paper, a new parameter, what we call incoming angle, is introduced and its effect on thrust force is shown. Incoming angle is defined as the angle between the direction of thrust and that of vehicle velocity. The effect of this angle can be dominant during an underwater vehicle changes its direction, or an omni-directional vehicle carries out its task. The effect cannot be compensated by including the only parallel component of vehicle velocity into previous models. In this paper, based on the three axial flow states classification model, the effect of the incoming angle of ambient flow is analyzed, and critical incoming angle (CIA) is also defined to describe the thrust force states according to incoming angle. In order to characterize the effect of non-parallel ambient flow, the proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within /spl plusmn/2N force error.

A new feature based map building model that uses only the footprints of sparse sonar data has been developed and implemented. An arc feature association model was developed, which associates two sonar footprints into an arc feature. The association model provides information on the regions of center points of the arc feature. The information makes it possible to decide weather the two sonar footprints are associated with a line, point or arc. Lines, points, or arc features are extracted from more than two independent sonar footprints using the arc feature association model. We also propose a method that estimates the position uncertainties of the extracted features by considering both the pose uncertainty of the robot and the measurement uncertainty of the sonar sensor. This pose uncertainty is also used in the arc feature association process of the sonar footprints. The proposed method has been tested in a real home environment with a mobile robot.

Second-order contact kinematics for regular contacts, such as surface-surface, curve-curve, curve-surface, and vertex-surface, are formulated in a unified framework, extending Montana's (1988) first-order contact kinematics for surface-surface contact only. Given two rigid bodies in contact with each other, the contact kinematics is the mathematical expression of the relative motion between them and the five dimensional contact coordinate vector. Second-order contact kinematics describes the second-order derivative of the contact coordinate vector in terms of the derivative of the relative twist. The result can be directly applied to geometric integration of contact dynamics.

Numerical integration methods based on the Lie group theoretic geometrical approach are applied to articulated multibody systems with rigid body displacements, belonging to the special Euclidean group SE(3), as a part of generalized coordinates. Three Lie group integrators, the Crouch-Grossman method, commutator-free method, and Munthe-Kaas method, are formulated for the equations of motion of articulated multibody systems. The proposed methods provide singularity-free integration, unlike the Euler-angle method, while approximated solutions always evolve on the underlying manifold structure, unlike the quaternion method. In implementing the methods, the exact closed-form expression of the differential of the exponential map and its inverse on SE(3) are formulated in order to save computations for its approximation up to finite terms. Numerical simulation results validate and compare the methods by checking energy and momentum conservation at every integrated system state.

Based on the linear complementarity formulation of a unilaterally inextensible wire model, the behavior of motion and stability of wire-suspended dynamical systems is addressed in this paper. The concept of tension properness is developed to determine kinematical motion property, particularly the instantaneous degrees-of-freedom. Further, motion determinacy in spite of tension indeterminacy is proven. The notion of tension closure is described to help analyze the stability behavior of wire-suspended dynamical systems. By analyzing the maximal closure-domain of relative tension closure, one can assess the stability behavior of wire-suspended systems qualitatively as well as somewhat quantitatively. Some numerical examples and simulations will clarify and corroborate the theoretical results of this paper.

There are several kinds of singularity in controlling robotic manipulator. Among those, the kinematic and algorithmic singularity avoidance have been investigated intensively. What seems to be lacking, however, is excessive performance reduction in non-singular region and difficulty in performance tuning. In this paper, we develop a new method to solve kinematic and algorithmic singularities using task reconstruction approach. This new method drives the manipulator singularity-free path, and simultaneously guarantees task performance. And it can be easily extended singularity avoidance of multiple tasks case in task priority based method of redundant manipulators. The advantage and performance of the proposed method is validated by simulation works.

We define that the end effector is the device which interacts with the environment or contacts objects to execute tasks. Up to now, many researchers have developed anthropomorphic robotic hands as end effectors. We discuss the problems in the development of a human-scale and motor-driven anthropomorphic robot hand. In this paper, PRH's design concept, kinematic design and developments of the actuator, transmission, and sensing device are presented. By imitating the physiology of human hands, we devised new metacarpalphalangeal joints and interphalangeal joints suitable for human-size motor-driven robot hands.

There was a paper on robust controller design framework of teleoperation. This design method strongly depends on the initial setup of the system and can not be applied to the realistic situation which includes the transition motion as free to contact motion because the controllers were separately derived for the free motion and contact motion. Therefore, we want to propose a new design framework which can solve this dependence. To prove the effectiveness of the proposed method, comparative simulation with the existing four channel design method was performed.

Since teleoperation systems are mostly executed in the extreme environment, there are constraints in designing the mechanism and choosing sensors. This paper presents a novel quantitative comparison method of teleoperators based on H/sub /spl infin// framework. The upper H/sub /spl infin// norm bound of the system including H/sub /spl infin// sub optimal controller is used as the performance index. As a case study, the method is applied to a real teleoperation system to study the effects of sensory configuration and back-drivability of the mechanism on the performance of the system in tasks, which involve different environment impedances. It can be important criteria to design a teleoperator from the control point of view.

Numerical integration methods based on Lie group theoretic geometrical approaches are extended to articulated multi-body systems with rigid body displacements belonging to the special Euclidean group SE (3) as a part of generalized coordinate. Two Lie group integrators, Crouch-Grossman method and Munthe-Kaas method, are formulated for the equation of motion for articulated multi-body systems. The proposed methods provide singularity-free integration, unlike the Euler-angle method, while the integration always evolves on the underlying manifold structure, unlike the quarternion method. Numerical simulation result validates the methods by checking energy and momentum conservation at even integrated system state.

A straightforward inverse kinematic algorithm for multilink flexible robots is proposed to improve the control performance. The inclusion of a dynamic constraint maximizes the performance of feedback controllers in high-speed applications. To obtain a numerically feasible solution, the singular perturbation approach is employed, which decomposes the inverse kinematics into an averaged part (slow part) and a parasitic part (fast part). The solution of the averaged part is considered the desired inverse kinematics, while the parasitic part is intentionally removed. The parameter expansion is carried out to obtain the solution sequentially. The implicit expansion method, which is a refined version of the expansion method, reduces computing time considerably. The formula in discrete time offers efficiency in computer applications. In addition, a requirement on differentiability of the desired task trajectory is derived.

All mobile robots suffer from odometry error. Relative localization from odometry has both the systematic and the non-systematic errors. However, once a precise system error model and its parameters are given, the accuracy of odometry can be remarkably improved. Most previous works on this effort focused on the differential drive robots with little attention to the other types of mobile bases. In this paper, we analyze sources of odometry error and propose an error model for the synchro drive robot. We then describe a novel procedure to accurately estimate the error parameters of the derived error model and the covariance matrix of the synchro drive robot. However, this procedure is general for all mobile bases, so we also apply our method for the differential drive robots and show experiments. This new process uses the shape of the path, as opposed to just end points, to estimate the error parameters and covariance matrix. We happen to use the generalized Voronoi graph to generate this path. Experimental results validate the error model of the synchro drive robot and precise estimation ability of the proposed method for the synchro and the differential drive robots.

GVG has been effectively used as a sensor based navigation tool using 360/spl deg/ sensor data. For mobile guide robot applications, however, we can only use 180/spl deg/ sensor data and the robustness of the navigation algorithm is critical for successful applications. For that purpose, the robot should be equipped with three capabilities. Those are 1) incremental GVG construction, 2) robust GVG navigation and 3) navigation strategy that just uses half of the sensor scan, i.e. 180/spl deg/. In this paper, we propose a GVG navigation algorithm that has above 3 capabilities. We firstly propose a method that can estimate the invisible 180/spl deg/ range from previous range data. Moreover, we suggest a way of robust GVG navigation algorithm by using a sensor data matching technique. The simulation result validates that the proposed algorithm can incrementally and robustly navigate the semi-unstructured map by using 180/spl deg/ sensor scan.

This paper deals with the sequential inverse kinematics for the high speed tracking of flexible robots. As a continuation of the implicit expansion method in, the explicit version of the sequential inverse kinematics is presented which is easier to be understood conceptually. The accuracy and stability of the two algorithms are compared and proved. Numerical study verifies the validity of the theoretical results.

In this paper, an algorithm for hyper symmetric environment simultaneous localization and mapping (SLAM) is developed based on the generalized Voronoi graph. The hyper symmetric environment is a challenging environment for the SLAM and the most difficult problem on this is a data association. To redeem this problem, we propose three techniques. The first one is a breadth first node navigation algorithm which highly reduces the complexity of the data association. The second scheme is an odometry calibration method which converts untrustable odometry into reliable one. The third method is a multi layered data association scheme. We integrate above three techniques into one framework and propose a SLAM algorithm for the hyper symmetric environment. The simulation result shows that the proposed algorithm can successfully cover a large hyper symmetric environment under 20% odometry uncertainty.

Underwater vehicle-manipulator (UVM) system becomes popular for various applications although it has complexity in control and analysis when compared to the vehicle itself. To resolve the complexity, firstly, thorough kinematic and dynamic analysis on the whole UVM system is performed. Then, the whole dynamics is divided into several meaningful terms. A robust coordinated motion control algorithm for autonomous UVM systems is considered next based on the analysis of divided dynamics. Since the vehicle has relatively smaller bandwidth than manipulators, active damping control with two-time scale approach for the manipulator is proposed. Using this algorithm, the manipulator controller can cover the vehicle state. The performance of the proposed algorithm is verified by computer simulations.

Most tele-operation to manipulate an object consists of grasping and manipulation, and two or more 6-DOF haptic devices are usually used in master side. In this article, a new simply designed 7-DOF haptic device, PATHOS II is proposed for 1-DOF grasping and 6-DOF manipulation. The merits of a parallel type haptic device such as high stiffness and accuracy are natural characteristics of PATHOS-II with optimized workspace. Due to its unique symmetric structure, the isotropic manipulability is enhanced within the reachable workspace. This parallel type haptic device can be used in applications which need high precision, stiffness and isotropic manipulability.

In this paper we describe an online solution for avoiding the occurrence of both algorithmic and kinematic singularities in task-priority based kinematic controllers of robotic manipulators. The proposed approach uses a secondary task correction and a successive task projection in order to maintain the measure of manipulability of the correspondent space augmentation approach over a minimum value. It shows a gain in performance and a better task error especially when working in proximity of singular configurations. It is particularly suitable for autonomous systems where an offline trajectory control scheme is often not applicable.

In this paper, we present hopping strategy which is more human-like for legged robot through stiffness modulation of a body. This method enables to reduce impact force on touch-down and adaption on ground stiffness change. Simple selected models are used to validate this method and singular perturbation method is used for control.

This paper shows how to find the optimal trajectory with respect to a certain cost when a kinematically redundant manipulator is subject to joint kinematic limits. Relevant mathematical properties in accordance with our intuition are strictly enlightened to be useful for this seemingly easy albeit complicated problem. Key terms of 'reducibility' and '(weak) optimal extensionability' are defined for further discussion of the solution. The final solution is basically achieved by the composition of applying the extended Jacobian method on each joint space where the joint coordinates saturated with the limits are eliminated. We would then be able to understand how the transition query is characterized and why the solution should be in that way. The simulation example would reveal how the solution gets links with our physical intuition and this would make us cope with unexpected algorithmic singularities.

The dynamic stability of a mobile manipulator using ZMP compensation is considered. A unified approach for the two subsystems is formulated using a redundant scheme. First, to conserve the dynamic stability of the system, we define the performance index for the redundant system using the ZMP (zero moment point). This performance index represents the stability of the whole mobile manipulator system. Then, the redundancy resolution problem for optimizing the given performance index is solved using the null motion. Finally, the performance of this method is demonstrated by simulation study.

In autonomous system, it is important to establish a control scheme that works with stability even near singularity configurations. We describe an online trajectory control scheme that uses the manipulability measure as a distance criteria to avoid manipulator singularities. The proposed approach consists in a method for limiting the minimum value of the distance criteria. The performance is simply affected by the choice of the lower limit. Based on a real-time evaluation of the measure of manipulability, this method does not require a preliminary knowledge of the singular configurations. The proposed algorithm is validated by experimental results.

In redundant manipulator research community, the phenomenon called torque instability has not yet been completely characterized using unanimous agreement on its mechanism. This article shows that the behavior is indeed caused by incomplete compensation of null dynamics related with self-motion. This instability can be avoided by proper control with compensation of nonlinear null dynamics, and we propose a method to stabilize conventional redundancy resolution schemes extended to dynamic controls.

There have been many bilateral micro-teleoperation systems handling a small object. Historically, Lawrence (1991) proposed the transparency-optimized architecture and passivity theorem for stability analysis of bilateral teleoperation. He claimed that unless the task(or environment) impedance contains a significant inertial behavior, passivity condition for transparency-optimized architecture can not be satisfied. To overcome this problem, we analyze and propose a method which can satisfy passivity condition for the micro-teleoperation system handling a small inertial object based on the structures originated by Lawrence and Hashtrudi-Zaad et al. (1999) and velocity-force scaling property of micro-teleoperation.

This paper proposes a PID-type composite controller for controlling flexible arms modeled by the singular perturbation approach, and investigates a tuning method based on the proposed controller structure. For the slow sub-controller, a PD plus disturbance observer is used, which eventually takes on PID form, and for the fast sub-controller, modal feedback PID control is utilized. The effects of design parameters of the controller on the closed loop response are investigated. Through simulation and experiments, the adequacy and performance of the proposed method are verified.

This paper proposes an improved differential inverse kinematics algorithm which works good for high speed motion control. The dynamic constraint as well as the kinematic constraint is utilized for the inverse kinematics. The singular perturbation method helps to reduce inverse kinematics to be solvable. The numerical and experimental results show the performance of the proposed method clearly.

In this paper, we describe an online trajectory control scheme for robotic manipulators with dynamic change of the task priority. First, given tasks are reconstructed using a geometric projection on the given index function. Then, the reconstructed tasks are analyzed in the framework of task priority based method. From this analysis, the proposed algorithm is shown to have the property that the task priority is assigned dynamically. Using this algorithm, we easily set the criteria of changing task priority and estimate the performance of the given index function. Considering the measure of manipulability as a index function, the approach is suitable for avoiding kinematic singularities for autonomous operation of, for example, an underwater robot. The result shows a good performance near the singular configurations, as shown by simulation results.

Enveloping grasp systems not in static equilibrium result in dynamical motion. In this article, we develop a method based on dynamical-balance to solve motion analysis problem for an active-external grasp, that is to determine contact forces and accelerations consistent with the dynamics and friction law for a given torque-wrench pair. Since the method is analytic in nature, it may give the chance for better efficiency and more direct insight than a relevant linear complementarity formulation, although the solutions are the same. The method is verified using two examples: one is a simple grasp system with one frictional contact, and the other is a relatively general one with all frictionless contacts.

This paper suggests an inverse optimal PID control design method for mechanical manipulators. We find the Lyapunov function and the control law satisfying the disturbance input-to-state stability by using the characteristics of Lagrange system. Also, we show that the inverse optimal PID controller satisfies the Hamilton-Jacobi-lsaacs equation. Hence, the inverse optimality of the closed-loop system dynamics was acquired through the PID controller, if some conditions for the control law are satisfied. Also, simple coarse/fine performance tuning laws are suggested based on the analysis for performance limitation of the inverse optimal PID controller.

In this paper, an input preshaping control method with robust disturbance attenuation algorithm is proposed to suppress vibration of a translating flexible beam carrying a fixed or moving mass. The input preshaping method is based on the analytic modeling of the system and frequency equation as a function of various parameters including the position of moving mass. By using a generalized framework for disturbance attenuation, it is shown that an input preshaping method can be easily designed to reduce single mode residual vibration and to get accurate positioning of a beam-mass-cart system in the presence of uncertainty and disturbance.

A dual-stage, fast and fine robotic manipulator is presented. By adopting merits of both coarse and fine actuator, a desirable system having the capacity of large workspace with high resolution of motion is enabled. We constructed an ultra precision XY-manipulator with dual-stage structure where the PZT driven fine stage is mounted on the motor driven XY positioner and applied it to fine tracking controls and micro-teleoperations as a slave manipulator. We describe essential merits of the compound actuation mechanism and the control strategy to successfully utilize it with a proper servo system design. Through experimental results, the effectiveness of the coarse/fine manipulation by the dual stage manipulator is shown.

One of recent achievements in the field of nonlinear /spl Hscr//sub /spl infin// optimal control theories for Euler-Lagrange systems is the analytic solution to the Hamilton-Jacobi-Isaacs (HJI) equation associated to the so-called nonlinear /spl Hscr//sub /spl infin// inverse-optimal control. In this paper, we address the problem of nonlinear /spl Hscr//sub /spl infin// optimal control design for an Euler-Lagrange system, rather than the inverse-optimal problem. By introducing a technique of control weight loosening and state weight strengthening, we show that the associated HJI inequality, not the equation, for nonlinear /spl Hscr//sub /spl infin// optimal control can be solved also analytically using the inverse-optimal solution.

This paper proposes a method to accommodate multiple tasks for redundancy utilization, which is based on a specific weighted pseudo-inverse. The proposed method also has task priority imposition property same as those conventional task priority based methods. In order to deal with general situations of task specification, the so-called semi-definitely weighted pseudo-inverse is devised.

A simple direct parameter update rule is presented to suppress the vibration more quickly by considering the dynamics of the flexible subsystem. Different from most of adaptive control schemes in multilink flexible robots, which are a model independent approach, the proposed adaptive control utilizes model parameters and updates them. Usually, the precise modeling of multi-link flexible robot is hard to obtain and even if we can get it, it is difficult to use in the online control tasks. For these reasons, a simplified model is used to describe the robot dynamics. The modelling errors and other structured uncertainty are considered as parameter perturbation. We verify the effectiveness of the proposed algorithm through experiments.

Although most robotic manipulators have used the conventional PID or PID plus something, e.g., gravity compensator, friction compensator, disturbance observer and so on, there exist still no general tuning rules which can adjust the PID control performance. The paper explains the tuning methods of inverse optimal PID control assuring the extended disturbance input-to-state stability and shows its validity through many experiments for a robotic manipulator.

In this paper, we propose and develop a spatial 3-DOF tele-micromanipulator for precise position control of micro-objects. Typical feature of this device is a one-module flexure hinge that consists of a revolute joint and a spherical joint. Based on preliminary kinematic analysis and stiffness modeling of the system, optimal design and actuator sizing for the device are performed. Furthermore, FEM analysis and resonant frequency analysis are executed to validate the results of analytic design process. The designed device was successfully implemented to tele-micromanipulation system.

Present the development of a human-mobile robot communication system using electrooculogram (EOG) signals. An ideal velocity shape signal processing algorithm is proposed to extract position data where the eyes are focusing on from the noise and drift included in EOG signals. Additionally, an efficient algorithm for the detection of various eye-lip movements such as blink and wink is suggested. Two experiments were performed for the validation of the human-mobile communication system. One is point stabilization of a mobile robot, using extracted eye focusing position data. The other is a moving target following experiment using various eye-lip movements as mobile robot commands.

A PTP motion control method composed of a feedforward and feedback controller is proposed to get accurate positioning and to suppress vibration of a XY positioning system with a flexible beam. The input preshaping based on the analytic modeling and frequency equation of the system is proposed as a feedforward controller to produce desired responses. A feedback controller is proposed based on a robust internal-loop compensator to stabilize the whole system and to meet closed-loop performance. Experiments are carried out to show the validity of the proposed controller.

A new perturbation attenuation method is proposed for motion control of robotic systems. It effectively attenuates all the perturbation to the joint dynamics of a plant including inertial cross coupling force, nonlinear friction, and external disturbances. As a result, it achieves a decentralized control system. A remarkable feature of the algorithm is that it has multiloop structure which can hierarchically compensate the "residual perturbation." An inherent property of the recursive algorithm which enables to overcome the limit of existing perturbation observers is described through the stability/performance analysis. Experimental results on the 2 DOF direct drive arm verifies the effectiveness of the proposed perturbation compensator.

This paper suggests an inverse optimal PID control design method to track trajectories in Lagrangian systems. The inverse optimal PID controller exists if and only if the Lagrangian system is extended disturbance input-to-state stable. First, we find the Lyapunov function and the control law that satisfy the extended disturbance input-to-state stability by using the characteristics of the Lagrangian system. The control law has a MID control form and satisfies the Hamilton-Jacobi-Isaacs equation. Hence, the H/sub /spl infin// inverse optimality of the closed-loop system dynamics is acquired through the PID controller if the conditions for the control law are satisfied. Also, simple coarse/fine performance tuning laws are suggested based on a performance limitation analysis of the inverse optimal PID controller. Selection conditions for gains are proposed as functions of the tuning variable. Experimental results for a typical Lagrangian system show that our analysis provides performance and H/sub /spl infin// optimality.

Focuses on the design of rigid part motion control and the selection of bandwidth of the rigid subsystem. Based on the passivity approach and disturbance observer, we investigate the relationship between macro joint tracking performance and vibration suppression capability using the joint motion bandwidth parameter. For the flexibility isolated system, the independent sub-controllers for the rigid and flexible part are proposed just like the singular perturbation approach. The validity of the proposed method is verified by experiments.

In this paper, we consider hard contact transition such as hammering when the manipulator's approaching velocity is high or the environment is very stiff. In contact transition, phase transition can be divided into two definitely different phases, "pre-transition phase" and "transition phase". Here we define the "pre-transition phase" and we propose three control methods. First, we propose a novel controller named as "suppression controller" which is not only stable but also simple to implement. Second, we present passive damper named as "flexible-damped joint" which is a good solution to circumvent the pre-transition phase. Third, we suggest a stable and simple controller which can maximize joint damping and minimize recontact velocity in the flexible-damped joint. It is named as "joint damping controller". The effectiveness of these three approaches are verified via experiments using the POSTECH 1-DOF DD arm.

We investigate the instantaneous impact phenomenon due to the collision between the manipulator and the environment for kinematically redundant manipulators. First, we define a normalized impact geometry in the viewpoint of normalized velocity change. We then reduce the impulsive effects under uncertain environment for redundant manipulators. A new impact performance index based on velocity direction is proposed. Finally, the performance of the proposed index is demonstrated by numerical examples and experiments.

A general formulation to obtain an analytic Jacobian matrix for in-parallel manipulators has not been developed yet, although several authors addressed it. This paper suggests a consistent approach to obtain the instantaneous kinematic relation of in-parallel manipulators, by extending the results of screw theory to the forward and inverse instantaneous relations, and gives a formula that can obtain a reciprocal screw analytically without considering geometric relations.

A robust and time optimal control strategy for dual-stage manipulator is presented. The time optimal trajectory for a mass-damper system is determined and given as a reference input to the system. The feedback controller is constructed so that the fine stage tracks the coarse stage errors. The controller is robustly designed as the "perturbation compensated sliding mode control" law, which is a combination of sliding mode controller and perturbation observer. In addition, a null motion controller which regulates the fine stage at its neutral position is designed based on the "dynamic consistency" and, as a result, the overall dual-stage servo system exhibits robust and time-optimal performance. The inherent merit and performance of the dual-stage system is shown by experiments.

The kinematically decoupled joint space decomposition (KD-JSD) is proposed as a systematic method for synthesis of dynamic control of kinematically redundant manipulators. The method leads to a proper control for direct specification and control of the self-motion of kinematically redundant manipulators, as well as the task motion. To cover various possibilities in specifying desired self-motion behaviors the conventional gradient projection method and task priority based method are reformulated within the framework of the KD-JSD. The validity of the proposed control method is shown experimentally adopting a three degrees-of-freedom direct-drive planar redundant manipulator.

In this paper, we consider hard contact force control law which shows good performance as well as enhanced stability and robustness. First, we consider hard contact transition such as hammering when the manipulator's approaching velocity is high or the environment is very stiff. In hard contact, phase transition can be divided into two definitely different phases, "pre-transition phase" and "transition phase". Here we define the "pre-transition phase" and propose a novel controller named as "suppression controller" which is not only stable but also simple to implement. Based on this result, we suggest a hard contact force control scheme which is named "hybrid suppression controller". The effectiveness of these approaches are verified via experiments using POSTECH 1 DOF DD arm.

The success in nonlinear H/sub /spl infin// control design is applied to the control of Euler-Lagrange systems. It is known that the existence of H/sub /spl infin// optimal control depends on solvability of the so-called Hamilton-Jacobi-Isaacs H/sub /spl infin// partial differential equation. In the article, the associated HJI equation for nonlinear H/sub /spl infin// inverse-optimal control problem for a Euler-Lagrangian system is solved analytically. The resulting control is referred to as the reference error feedback, which takes conventional PID controller form. Consequently, robust motion control can be designed for robot manipulators using L/sub 2/-gain attenuation from exogenous disturbance and parametric error.

Deals with the parking problem of a car-like mobile robot. The parking problem corresponds to the point-stabilization problem of nonholonomic systems. We propose a simple and efficient algorithm for the problem with stability analysis. Using this algorithm, a car-like mobile robot could be controlled to move to a desired posture within a prescribed boundary. The performance is verified through simulations and experiments using a car-like mobile robot.

Industrial manipulators are under various limitations against high quality motion control, for example friction and dynamic uncertainties, and simple control structure. A robust linear PID motion controller, called the reference error feedback, is proposed, which solves the nonlinear L/sub 2/-gain attenuation control problem. Making use of the fact that the single parameter L/sub 2/-gain /spl gamma/ controls the performance and robustness, we propose a simple and stable method of tuning the controller called the "square law". The analytical results are verified through experiments of six degrees of freedom industrial manipulator.

Deals with the posture stabilization problem of car-like mobile robots. For a long distance, path-planning should be done first. But, over a short distance like a parking maneuver, path planning is difficult since car-like mobile robots, different from the conventional differential drive mobile robot, have a limit on turning radius. The derivation of a control law is also difficult in the sense that there exists no smooth feedback control for this problem. We propose a simple and efficient algorithm for the problem with stability analysis. Using this algorithm, we can control a car-like mobile robot to move to a desired posture within a prescribed boundary. The performance is verified through simulations and experiments using a car-like mobile robot.

In this paper design of a robust impedance control is proposed for kinematically redundant manipulators. To achieve this goal, we propose a new disturbance observer scheme which can handle the nonlinear dynamics of a manipulator. An extended task space formulation to describe the dynamics of redundant manipulator is employed. Using this extended task space formulation, a robust impedance control method is proposed based on the momentum feedback disturbance observer (MFDOB). The performance of the proposed extended impedance controller is verified through experiments with a planar three-link direct-drive manipulator.

The kinematically decoupled joint space decomposition is proposed as a general coordinate transformation between the joint space and the task space of kinematically redundant manipulators. The method can describe the kinematic, force, and dynamic redundancy in a unified way using a minimal number of systematic motion variables. Based on the method, a set of equivalent dynamical models of redundant manipulators is constructed and its characteristics are studied focusing on the dynamical decoupling between the task and the null motion dynamics.

An analytic expression for the singularities of a six-degree of freedom Stewart-platform type parallel manipulator (six-DOF SPM) is obtained using the concept of a local structurization method (LSM) with extra sensors. The resulting equation, however, does not include sensor values. It is only a function of design variables. The equation is basically a third-degree polynomial and it exactly provides singularity information of general SPMs. Several symbolic examples are shown to verify the exactness of the singularity equation by comparing it with previously reported singularities. Some other numerical examples are also shown using the proposed singularity equation.

In this paper, a force estimation method is proposed for force control without force sensor. A disturbance observer is applied to each joint of an n degrees of freedom manipulator to obtain a simple equivalent robot dynamics being represented as an n independent double integrator system. To estimate the output of disturbance observer due to internal torque, the disturbance observer output estimator (DOOE) is designed, where uncertain parameters of the robot manipulator are adjusted by the gradient method to minimize the performance index which is defined as the quadratic form of the error signal between the output of disturbance observer and that of DOOE. When the external force is exerted, the external force is estimated by the difference between the output of disturbance observer and DOOE. A force controller is designed for force feedback control employing the estimated force signal. Several numerical examples and experimental results are illustrated for a 2-axis direct drive robot manipulator.

An approach to resolve the kinematic redundancy and to control the motion/force of redundant manipulators is presented. By defining a proper metric in joint space, minimal parametrization of motion and force controlled subspaces as well as the null motion component is realized. With this formulation, control of both motion/force and internal motion of redundant manipulator can be achieved via a new hybrid impedance control method with inertial decoupling of each space. Some numerical examples are given to demonstrate the performance of the proposed control method.

Recent successes in nonlinear H/sub /spl infin// control design has been applied to control of robot manipulator systems. It is known that the existence of H/sub /spl infin// optimal control reduces to solvability of the Hamilton-Jacobi-Isaacs partial differential equation, which is very difficult to solve. In this article, a robust control in the sense of L/sub 2/-gain attenuation from an external disturbance and one due to model uncertainties is designed for a class of Euler-Lagrange system based on a class of analytic solution to the associated Hamilton-Jacobi-Isaacs equation.

The analytic expression for the singularities of 6-DOF SPM (Stewart-platform type parallel manipulator) is obtained using the concept of LSM (local structurization method) with extra sensors. The resulting equation, however, does not include sensor values, but is a function of design variables. The equation is basically a third-degree polynomial and it exactly provides singularity information for general SPMs. Several symbolic examples are shown to verify the exactness of the singularity equation by comparing it with the previously reported singularities.

An impedance control approach based on extended task space formulation is addressed to control kinematically redundant manipulators. Defining a weighted inner product in joint space, a minimal parameterization of the null space can be achieved. Based on this formulation, we propose a control law called inertially decoupled impedance controller by expanding the conventional impedance control approach to control the motion of the end-effector as well as the internal motion. Experimental results are given to demonstrate the performance of the proposed control methods.

A robust motion control with special focus on kinematically redundant manipulators is proposed based on the kinematically decoupled (KD) joint space decomposition and the analytic nonlinear /spl Hscr//sub /spl infin// control. Within the /spl Hscr//sub /spl infin// control design, robustness can be achieved by formulating the disturbance input to include model uncertainties and external disturbances as well. The proposed /spl Hscr//sub /spl infin// control is based on the analytic solution of the related Hamilton-Jacobi-Isaccs equation, and does not require any further assumptions except the Euler-Lagrange properties. Two components of motion, task and null motion, of a redundant manipulator are robustly controlled by the proposed KD reference motion compensation (RMC) reference error feedback (REF) control.

Although nonlinear H/sub /spl infin// control theory for systems with uncertainties has been developed, it has been rarely applied to robot manipulators. We derive Hamiltonian equations of motion and Hamiltonian matrix (Riccati equation) for robot manipulators to get an optimal controller. Using the H/sub /spl infin// framework, we specify the L/sub 2/ norm of the performance measure, which consists of the position and momentum error during the task when there are uncertainties in the dynamic model of robot manipulators. For practical application, a stationary solution of the Riccati equation for the end-point is suggested and evaluated by simulation.

A modular robot manipulator is a robotic system assembled from discrete joints and links into one of many possible manipulator configurations. This paper describes a task based design method of modular robot manipulators. A locking mechanism which provides quick coupling and decoupling is developed and a parallel connection method is devised reducing the number of components on each module. To automatically determine the optimal link lengths of a modular manipulator for a given, task, the algorithm is two step: determine the necessary configuration of the robot using kinematic equations and then determine the optimal link length using the proposed efficient genetic algorithm. Some of design examples are shown.

Since parallel manipulators have relatively small workspace when compared to serial manipulators, it is important to determine the workspace in the design stage. However, due to the complexity of the closed-loop chain mechanism, the determination of the workspace is difficult. In this paper, a fully geometrical method for the determination of the workspace of 6-DOF parallel manipulators is presented using the concept of a 4-bar linkage. The reachable and dexterous workspace can be determined from the proposed algorithm. In order to evaluate the workspace, each leg is considered as an open chain, and two kinematic constraints-position and mechanism constraints-are developed. The proposed method is verified by simulation.

An impedance control approach based on extended task space formulation is addressed to control the kinematically redundant manipulators. Defining a weighted inner product in joint space, a minimal parametrization of the null space can be achieved. Based on this formulation, we propose a control law called inertially decoupled impedance controller by expanding the conventional impedance control approach to control the motion of the end-effector as well as the internal motion. Some numerical simulations are given to demonstrate the performance of the proposed control methods.

Generally, singularity analysis of 6-DOF parallel manipulators is very difficult and, as a result, the velocity relation has many uncertainties. Many researchers have reported that the forward position kinematics can be solved more easily with extra sensors. However, the relation of the velocity kinematics with extra sensors has not been fully studied yet. In this paper, an alternative method using the local structuralization method (LSM) for the analysis of singular configurations is presented. With LSM, the velocity relation can be represented in a simple form, and the result is totally equivalent to the conventional velocity relation. The velocity relation suggested in this paper gives a closed-form solution of singularities.

Design of a motion tracking controller is proposed for kinematically redundant manipulators. Using the weighted inner product in joint space, a minimal parametrization of the null space can be achieved. Combining this minimal set of null motion with kinematic relation, we obtain a new extended task space formulation. Based on this formulation, a trajectory tracking control law is proposed based on the momentum feedback disturbance observer (MFDOB). The performance of the proposed controller is verified through experiments with a planar three-link direct-drive manipulator.

The kinematics and dynamics of kinematically redundant manipulators are to be decomposed in consistent way with the joint space decomposition induced by the weighted pseudo-inverse operation. The weighted kinematically decoupled joint space decomposition results to a family of decomposed kinematics and dynamics, parametrized by the symmetric positive-definite weight matrix, in the task motion and weighted null motion space. The inertially-decoupled weighted decomposition is shown to be only possible with the inertia as the weight. Also, the feedback motion controller which is consistent with a specific joint space decomposition is developed. The controller realizes the decoupled and linearized reference acceleration tracking controller in the task motion and weighted null motion space.

Various physical limitations which intrinsically exist in the manipulator control system, for example kinematic limits and torque limit, cause some undesirable effects. Specifically, when one or more actuators are saturated in torque the expected control performance can not be achieved. The effect of torque limit, especially for redundant manipulators, is studied in this article, and a local and analytic method to reconstruct the control input using the redundancy is proposed based on the kinematically decoupled modeling of redundant manipulators. It results in exact compensation of the task motion dynamic error at the cost of the least null motion dynamic error. Numerical simulations help to verify the advantages of the proposed scheme.

Teleoperating system has been developed for several decades, and many control schemes for it have been suggested. But the implementation for real applications needs a very effective but simple controller. In this paper, an advanced control scheme to achieve this objective is suggested, which is the combination of a modified internal model controller and a variable filter for force reflection. The effectiveness of the proposed scheme is verified through experiment. Both responses of unconstrained and contact motions for the testbed-walls with different stiffness are shown.

Various physical limitations which intrinsically exist in the manipulator inverse kinematic system, for example joint travel and velocity limits, cause some undesirable effects. Specifically, when one or more joints are limited the desired task velocity can not be anticipated. The effect of joint kinematic limits, especially for redundant manipulators, is studied in this article, and an analytic method to reconstruct the inverse kinematic solution using the redundancy is proposed based on the kinematically decoupled modeling of redundant manipulators. It results in no error in the task motion at the cost of the least error in the null motion. Numerical simulations help to verify the advantages of the proposed scheme.

As a redundancy resolution method, the conventional extended Jacobian method (EJM) has two problems: One is the algorithmic singularity, and the second is nonexistence of a sufficient condition. Their impact on the inverse kinematic performances are exemplified. To remedy the related problems, we propose the notion of solution characteristics. It is based on an analytic sufficient condition for the EJM. This serves to confirm the right direction of optimization in the EJM and to characterize the algorithmic singularity problem. The above local characterization of an optimal solution by solution characteristics is globally extended by the invariance of the solution characteristics of the EJM. Specifically the characteristic of a solution with the EJM is invariant before crossing an algorithmic singularity. The ideas in this article also have practical implications since the EJM can now be fully analyzed with the theoretical results. To demonstrate its exactness and usefulness, planar three degrees of freedom (DOF) and spatial 4-DOF regional redundant manipulators are analyzed using the proposed solution characteristics.

This paper presents a new extra sensing approach of local structurization to the forward kinematics of 6-DOF Stewart platform manipulators (SPMs). It is shown that one extra sensor is sufficient for both a 3-3 SPM and a 6-3 SPM to exactly resolve the forward kinematic problem (FKP) of the SPMs in closed form, two for a 6-6 SPM. In previous researches, at least three extra sensors were needed for closed-form resolution of the FKP of a 6-6 SPM. As a basis for the new approach, two concepts, local structurization and mechanism partition, are introduced. The new scheme is described with graphical illustrations and verified through numerical examples. Consequently, the new scheme is suitable to real implementation, because its computation is very efficient.

Useful specification and stable control of motions for redundant manipulators are two important application aspects. In this article, the dynamic controller which can stabilize the output motion and the null motion is proposed, which has also a unified null motion specification algorithm in it. It is distinctive in that it renders the closed loop system dynamics linear and decoupled in the null space as well as the output space. All the work in this article are based on the kinematically decomposed modeling.

In this paper, two points are addressed as for the stiffness control of redundant manipulators. The first is, through the joint stiffness analysis, a generalized stiffness model between the joint and taskspace stiffness. Differing from the previous stiffness model, this model shows the existence of the additional stiffness concerning the configuration change and force. The second is the joint stiffness control scheme of redundant manipulators based on the developed model, called orthogonal stiffness decomposition control. The effectiveness of the proposed model and control method is verified through simulations and experiments.>

This paper deals with the modeling and implementation of a robot system for force and impact control using a newly developed passive hardware damper. The system with passive damper is modeled and based on the model, the stability of the whole system with respect to force feedback gain is analyzed. The limitations of the conventional velocity feedback to produce damping characteristic is discussed. Experiments are performed with/without passive damper to verify the effectiveness of the passive damping method and it is shown that the passive damper can help the system make stable contact during the contact period especially with highly stiff environment.>

The characteristic of an inverse kinematic solution for redundant manipulators using EJM is exploited with the sufficient condition for EJM. A new analytic perspective other than the well-known algebraic and less-known geometric one on algorithmic singularity of EJM is proposed, on the basis of optimization process. The characteristic of a solution with EJM is shown to be able to change only by crossing the algorithmic singularity thereof. A 3-DOF planar redundant robot is analyzed when EJM is applied with the manipulability as an example.>

The stiffness of a grasp is analyzed on the basis of the generalized virtual stiffness (GVS) model. Considering that the normal and lateral stiffness of the finger usually are not decoupled due to kinematics and mechanical design, GVS is formulated as coupled virtual springs. The authors relate GVS to the effective fingertip stiffness including the additional stiffness at the joint space. Based on the grasp stiffness formulation, simulations were carried out, focusing on the grasp stability.

A dynamic control method that outperforms existing local torque optimization techniques for kinematically redundant manipulators is proposed. The proposed method resolves redundancy at the acceleration level. The command acceleration is composed of two terms: the minimum-norm acceleration and the null-space acceleration, which is intermittently added to the minimum-norm acceleration according to a kinematic criterion to globally reduce excessively large torque requirements. In particular, both the generation and the control of null-space acceleration is based on the property of full row-rank minors of a Jacobian matrix, i.e., the aspect, which is a function of a manipulator's configuration. Numerical simulations illustrated good capability from the viewpoint of torque optimization and global stability.

A method for the inverse kinematic problem of planar manipulators with multiple degrees of redundancy is proposed. The method starts by decomposing a redundant arm into a series of the local arms which are either two-link or three-link planar manipulator modules and by connecting the conjunction points between local arms with virtual links. In addition to virtual links, a displacement distribution scheme where the displacement of an end-effector is allocated to those of local arms according to displacement distribution criteria is also proposed. To effectively guide the proposed schemes, a dexterity index called the configuration index is utilized. Computer simulations of redundant manipulators using the proposed method are demonstrated as numerical examples.>

This paper presents a real-time dynamic control method for kinematically redundant manipulators. The proposed method aims at reducing excessive joint torques, which have caused the instability of conventional local torque minimization algorithms, by preserving the signs of full row rank minors of the manipulator Jacobian. It is shown by computer simulations that these minors have close relation with the dynamics of redundant manipulators, which has not been treated carefully. The proposed method is found to be effective for torque reduction when compared with conventional approaches including the inertia-weighted pseudoinverse method, the null space vector method, and the redundancy decomposition control method.>