This paper presents a linear model predictive control (MPC) framework for a two-wheeled inverted pendulum with a roll joint (TWIP-R). Traditional linear quadratic regulator (LQR) approaches effectively stabilize the sagittal motion of two-wheeled inverted pendulums (TWIP), but fail to account for lateral dynamics and constraint handling during curved trajectories. Our approach decouples the system into pitch, yaw, and roll subsystems, each controlled by a dedicated linear MPC. By predicting lateral inertial forces from the reference velocity and yaw rate, the roll controller computes the zero moment point (ZMP) deviation and incorporates it into both the cost and constraints for proactive stabilization. Simulation results on a figure-eight trajectory demonstrate precise velocity tracking, robust lateral balance, and feasibility, highlighting the potential of the proposed method for dynamic, constraint-aware robot control.

Wheeled-legged robots offer significant mobility advantages, yet their control is complicated by the coupled dynamics of the wheel and leg systems. To address this challenge, we propose a whole-body control framework built upon a decoupled architecture. In this structure, a two-wheeled inverted pendulum (TWIP) template exclusively manages wheel motion, freeing the whole-body controller to focus solely on the leg dynamics. To validate the generality of our approach, we conducted extensive simulations across various robot configurations, including both closed-loop and open-loop leg structures. The results demonstrate the robot’s ability to maintain stability across several challenging scenarios: a high-speed (5 m/s) slalom on flat ground, a low-speed (0.5 m/s) slalom on terrain with 10 cm height variations, and immediate stabilization after a 2 m free-fall. These findings highlight the potential of decoupled control as a promising direction for developing more agile and resilient robotic systems.

This paper addresses robust landing stabilization in humanoid locomotion on uneven terrain. The core idea is to find a configuration of the robot that results in small impulsive force when an unexpected obstacle is encountered, and to adjust post-contact reference for swing foot with which the pose of the foot is stabilized on the obstacle. This can be achieved by walking with heel strike motion (validated by the impact map analysis) and by employing hybrid admittance control combining the admittance control with reset of post-contact reference, embedded into the momentum-based whole-body control framework. The validity of the proposed algorithm is verified by simulation with a physics engine.

This paper addresses the balance recovery problem for biped robots in the sagittal plane, affected by an instantaneous push. It is particularly seen that, when future footsteps of the robot should be re-generated to deal with a strong push, taking the foot size into account significantly reduces the number of footsteps required for the balance recovery. As an illustration of this idea, we generalize the so-called reactive step generator in the authors' previous work, by employing variation of the zero-moment point inside the supporting region as an additional control input for stabilization of divergent component of motion. On the basis of a mathematical formulation of the balance recovery problem, we verify the validity of the proposed algorithm with both theoretical and simulation results.

This paper proposes a 6 degree of freedom (DoF) manipulator for haptic application. The proposed haptic device, named GHap, is designed based on the four-bar-linkage mechanism for linear motion with the ring-type gimbal mechanism. To improve the force display ability, the device is designed to compensate the gravity force of the manipulator by its own weight. The conceptual mechanical design is compared by placing the third joint, which controls the four-bar mechanism, in two different configurations. The forward kinematics and the jacobian of GHap are presented. Finally, the gravity compensation method and open-loop force display performance of the proposed haptic device are validated by an experiment with the GHap prototype.

In this paper, we propose the stabilization strategy for a soft landing in a biped walking using impedance control and the optimization-based whole-body control framework. Even though proper contact forces and desired trajectories of the robot are given, the robot can be unstable easily if unexpected forces are applied to the robot or impulsive contact force is produced in the landing state while the robot is walking. Therefore, the impedance control approach using contact forces is performed to obtain the modified references that regulate the modified desired position, velocity and acceleration of the swing foot, and improves the walking stability. Moreover, we perform a whole-body control using quadratic programming (QP) that tracks the modified trajectories constrained with the centroidal momentum dynamics. To validate the algorithm, a walking task on uneven terrain using a humanoid robot is shown.

This paper addresses reactive generation of step time and location of biped robots for balance recovery against a severe push. Key idea is to reformulate the balance recovery problem into a tracking problem for "hybrid" inverted pendulum model of the biped, where taking a new step implicitly yields a discrete jump of the tracking error. This interpretation offers a Lyapunov-based approach to reactive step generation, which is possibly more intuitive and easier to analyze than large-scaled or nonlinear optimization-based approaches. With the continuous error dynamics for the divergent component of motion (DCM), our strategy for step generation is to decrease the "post-step" Lyapunov level for DCM error at each walking cycle, until it eventually becomes smaller than a threshold so that no more footstep needs to be adjusted. We show that implementation of this idea while obeying physical constraints can be done by employing a hybrid tracking controller (together with a reference model) as our reactive step generator, consisting of a simple DCM-based continuous controller and a small-sized quadratic programming-based discrete controller. The validity of the proposed scheme is verified by simulation results.

The aim of this paper is to construct a theoretical framework for stability analysis relevant to standing balance of humanoids on top of the linear inverted pendulum model, in which their dynamics between the center of mass (CoM) and the zero moment point (ZMP) is dealt with. Based on the well-known sufficient condition that the contact between the ground and the support leg is stable if the corresponding ZMP is always inside the supporting region, this paper aims at characterizing three types of the associated stability regions. More precisely, assuming no external force disturbances affecting the motion of the humanoids, the stability region of the initial CoM position and velocity values can be explicitly computed by solving a finite number of linear inequalities. The stability regions of time-invariant force disturbances such as impulsive force and constant force disturbances are also dealt with in this paper, where the former is exactly obtained through a finite number of linear inequalities while the latter is approximately derived by using an idea of truncation. Furthermore, time-varying force disturbances of finite energy and finite amplitude are concerned with, and their maximum admissible${l} _{2}$and${l} _{\infty }$norms are computed in this paper, where the former can be exactly obtained by solving the discrete-time Lyapunov equation while the latter is approximately derived through an idea of truncation. It is further shown for both the truncation ideas that the approximately obtained stability regions converge to the exact stability regions with an exponential order of${N}$, where${N}$is the truncation parameter. Finally, the effectiveness of the computation methods proposed in this paper is demonstrated through some simulation results.

In this paper, we address the problem of generating a trajectory of the zero-moment point (ZMP) and the rate of angular momentum for a bipedal robot in the sagittal plane, with which the balance of the robot is recovered from external push. Unlike most previous works that adjusted a pre-designed ZMP or solved (possibly too heavy) nonlinear optimization problems, our main purpose is to develop a fully reactive step generator in the sense that (a) no pre-calculation of nominal trajectory is required, and (b) the algorithm is simple enough to operate in real time, only by utilizing the current state of the robot. For the design, it is seen by reinterpreting the centroidal dynamics in the hybrid model framework that the balance recovery problem can be recast as the problem of stabilizing a hybrid-type (linear) inverted pendulum model. On the basis of the concept of the divergent component of motion, a simple hybrid control law is then constructed to stabilize the hybrid system, which serves as a step generator that automatically determines where and when to step. This paper briefly sketches a mathematical proof on the performance of the proposed generator from a control-theoretic perspective, which is also supported by simulation results.

This paper revisits computed torque method for robot manipulators and aims at developing its new framework based on the$L_{1}$robustness, in which the$L_{\infty}$norm together with its induced norm is employed to characterize model uncertainties and a performance measure. More precisely, we consider the$L_{1}$robust stability and performance for a given robot manipulator with a computed torque controller. We first show that the modelling errors in the computed torque method can be divided into an exogenous disturbance and a multiplicative model uncertainty, which are bounded in terms of the$L_{\infty}$norm and its induced norm, respectively. It is next shown that the robot manipulator with the computed torque controller can be equivalently represented by an interconnection of a continuous-time linear time-invariant (LTI) nominal plant and a stabilizing controller together with the$L_{\infty}$-induced norm bounded model uncertainty. Based on the interconnected representation, the$L_{1}$robust stability condition and an upper bound of the$L_{1}$performance against the exogenous disturbance with respect to all model uncertainties in a class of a bounded$L_{\infty}$-induced norm are dealt with by using the small-gain theorem. Finally, the effectiveness of the theoretical results is demonstrated through some experiment results.

In a human-robot interaction (HRI) device that performs physical collaboration operations in constant contact with the user, admittance control and impedance control are generally used. Since the two controllers exhibit opposite performances depending on the stiffness condition, controllers capable of dealing with various magnitudes of stiffness are required. As such, this study proposes hybrid control that adjusts the control distribution ratios of admittance control and impedance control based on the operating frequency analysis to react to the user intention and various stiffness conditions in real time. The proposed controller algorithm exhibited lower overshoot than impedance control in the step input response simulation, faster response speed compared to admittance control in the response simulation for 0-5 Hz input frequencies, and the smallest vibration magnitude and number of vibrations in the case of a virtual wall collision, resulting in improved performance compared to existing control methods.

Based on the liner inverted pendulum model (LIPM) for the dynamics of biped humanoid robots, an analytical method for computing stability regions relevant to standing balance of the biped humanoid robots is introduced in this paper. More precisely, two types of the stability regions are discussed in this paper with the consideration of that the zero moment point (ZMP) should be located in the supporting region to guarantee stable standing of the biped humanoid robots. First, assuming no external disturbances affecting the motion of the biped humanoid robots, the set of the initial values of the center of mass (CoM) position and velocity with which the location of the ZMP is limited to be inside the supporting region can be explicitly obtained by solving a finite number of linear inequalities. Second, two admissible sets of external force disturbances (impulse and finite energy) with which the ZMP does not deviate from the supporting region are characterized by solving finite number of linear inequalities or the discrete-time Lyapunov equation, respectively. The validity and effectiveness of the analytical method proposed in this paper are verified through a simulation result.

Feedback controller for robotic bipedal walking models can be multi-layered, consisting of low-level continuous-time controller and high-level event-based controller. Stimulated by the success in our preceding study that demonstrates the validity of the l∞-induced norm as an adequate performance measure, we suggest a systematic methodology to design optimal event-based feedback controller for bipedal models walking on rough terrains. More precisely, we first assume that the system is already equipped with a low-level continuous-time feedback controller, capable of stable flat-ground-walking, and then formulate the design problem of the high-level event-based feedback control as the l1-optimal control problem for discrete-time linear systems defined on the linearized Poincarè map. In order to validate the proposed methodology, nonlinear dynamic simulations are conducted with a simple biped model walking on rough terrain. The terrain slope randomly varies at each footstep while the magnitude of slope variation is bounded by some maximum value. Simulation results indicate that the optimal system equipped with the proposed controller can successfully overcome a rough terrain on which the original system could not walk and fall.

In this paper, we consider the L1optimal proportional-derivative (PD) controller synthesis by which the L∞gain of trajectory tracking systems can be reduced. The notion of input-to-state stability (ISS), which has been equipped with the L2norm of a vector-valued signal, plays important roles in evaluating the effect of disturbances on the system states. In connection with this, we first redefine the conventional ISS with the L∞norm to deal with bounded persistent disturbances because the disturbances should be mathematically regarded as elements of the L∞space. A tractable model for trajectory tracking control of robot systems is then given by using ideas of extended disturbance and composite error. We next introduce a design method of the L1optimal PD controller for such a model, by which the trajectory tracking system satisfies the redefined ISS and the L∞gain is less than a performance level γ. Finally, we examine the effectiveness of the design method through experimental results for a typical robot manipulator.

This paper presents a novel haptic device, named VirtuaPower, having abilities of 6 degrees of freedom (DOF) position measurement with a high-force display capability and wide workspace. The VirtuaPower is designed as a parallel mechanism with double in-parallel supporting chains. The mechanical design is presented with constraints and space Jacobians. The workspace and force display capability are computed via kinematic analysis. The VirtuaPower has a wide workspace up to 1 m3 (semicylinder, diameter 1.8 m × height 1.0 m). Moreover, the force display capability is enhanced compared to any existing haptic device, i.e., approximately 100 N of linear force and 10 Nm of torque. The force display ability is validated by an experiment with a 6 DOF force/torque sensor. Finally, a simple virtual reality environment is experientially realized with the VirtuaPower to show its utility as a haptic device.

While the central pattern generator (CPG) controller based on biologically inspired neural oscillator networks is known to achieve repeatability and entrainment of rhythmic output motion, no systematic approach to implement the controller has been provided so far. In this study a series of schemes are introduced to tune the control parameters and to set up the network coupling of artificial neural oscillators combined with the virtual force control. Application of the schemes to the proposed controller for a three-link planar robot arm demonstrates that the optimized controller with proper neural oscillator networks can produce minimize the energy consumption with an enhanced-entrained motion with the change of a desired motion and interaction of unknown environments.

Despite successful demonstrations of outdoor walking by a few biped robots, performance measure for such robots has not been formally defined yet. The performance measure should be adequately defined by which one can evaluate how well the robot keeps from falling in the presence of disturbance. If such performance measure is suitably determined, designing a sort of optimal controller for stable outside walking would be possible. This paper firstly suggests to adopt the l∞-induced norm defined on the linearized Poincarè map as a novel performance measure for biped robots, in the presence of bounded persistent disturbance. In order to validate the measure, a nonlinear dynamic simulation is conducted by extending an existing simple model to walk on rough terrain, of which height variance is bounded by some maximum value. The measure exploited for the model is compared with the numerical result obtained from nonlinear dynamic simulation. Finally, an example of application of this measure is provided, which successfully predicts whether the system could overcome upcoming terrain roughness.

In this paper, we propose a novel planar biped model which achieves asymptotically stable walking, based on ankle actuation and the virtual pendulum concept. Simple models for walking have provided useful insights in understanding fundamental principles of human locomotion as well as developing controllers for biped robots. Existing simplest walking models include a point mass with either rigid legs or compliant legs. Both models are able to describe different gaits such as walking and running, but are not able to address posture stabilization, which is another important issue in bipedalism. Recently, the virtual pendulum (VP) concept was proposed as an intuitive posture stabilization strategy and successfully demonstrated on the compliant leg scheme. However, the model does not address inherent peripheral mechanics of walking, i.e., ankle actuation and collisional energy loss from foot-ground interaction. The model proposed in this paper consists of a rigid trunk, rigid legs and ankle actuation. The energy dissipation due to collision and compensation by ankle actuation play an essential role for asymptotically stable walking, while the trunk posture is stabilized based on the VP concept. Nonlinear dynamic simulation verifies that the model constructs a stable limit cycle with a small angular oscillation of trunk.

When a robotic manipulator is controlled, computing gravity force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the gravity force is presented by a combination of gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the gravity force using the generalized gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational force and control the robot. It is shown that the generalized decomposition of gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.

In this paper, we present a real-time motion and force capturing system for tele-operated robotic manipulation that combines surface-electromyogram (sEMG) pattern recognition with an inertia measurement unit(IMU) for motion calculation. The purpose of this system is to deliver the human motion and intended force to a remote robotic manipulator and to realize multi-fingered activities-of-daily-living (ADL) tasks that require motion and force commands simultaneously and instantaneously. The proposed system combines two different sensors: (i) the IMU captures arm motion, (ii) and the sEMG detects the hand motion and force. We propose an algorithm to calculate the human arm motion using IMU sensors and a pattern recognition algorithm for a multi-grasp myoelectric control method that uses sEMG signals to determine the hand postures and grasping force information. In order to validate the proposed motion and force capturing system, we used the in-house developed robotic arm, K-Arm, which has seven degrees-of-freedom (three for shoulder, one for elbow, and three for wrist), and a sixteen degrees-of-freedom robotic hand. Transmission Control Protocol Internet Protocol (TCP/IP)-based network communication was implemented for total system integration. The experimental results verified the effectiveness of the proposed method, although some open problems encountered.

This paper addresses a control method for friction-existing robot manipulators and safe motion with its environment. In order to control the robot manipulator with unknown effects, a time-delay control(TDC) method that eliminates the nonlinear effects is used to control the joint torque servo. Although the TDC is very adaptive in nonlinear systems, there is limitation of the TDC in a high friction robot manipulator; hence, a friction model is considered. A collision detecting method is proposed to secure safety for human and robot-interacting environment. Using the torque sensor attached at the joints of the robot arm, the collision is detected more effectively. After detecting collision, a safety reaction method is applied. A torque sensor based 3-joints robot arm is used to verify the performance of the proposed methods.

When one uses a hand-held tool, the fingers often make the tool to be in contact with the palm in the form of multi-contact manipulation. Multi-contact manipulation is useful for object-environment interaction tasks because it can provide both powerful grasping of the object body and dexterous manipulation of the object end-effector. However, dealing with the internal link contact with the object is not trivial. In this paper, we propose Reactional Internal Contact Hypothesis that regards the internal contact force as a reaction force so that the desired finger force can be reduced. By taking a handwriting task as an example, optimal configuration search and grasping force computation problems are addressed based on this hypothesis and validated via dynamic simulation.

Biologically inspired control approaches have been attracted much attention as alternatives in recent time, for efficiently solving problems in controlling multi-DOF robotic systems, since most human beings or animals exhibit their behaviors in a natural way without explicit computation. Also, they show natural adaptive behaviors irrespective of unexpected external forces or changes of environment. This work is inspired from these novel features. Thus, a self-adapting robotic arm-hand control is proposed exploiting a control scheme based on central pattern generators (CPGs). Instead of a trajectory planning and inverse kinematics problem, this work endeavors to exploit robotic systems coupled with neural oscillators and virtual forces with joint velocity damping. We demonstrate self-adapting motions without the ill-posedness from extensive simulations that enable a robotic arm-hand to make adaptive changes from the given motion to a compliant motion. In addition, it is verified that reaching-to-grasping motion is possible by adopting only transit points sustaining motion repeatability under kinematic redundancy of joints.

In this paper, the grasping force control of a robotic hand based on a torque to velocity transformation using force/torque (F/T) sensors with gravity compensation is addressed. The force controller is designed and the task force is transformed into command torque. Then, the torque is converted into command velocity for the velocity servo control through torque to velocity transformation. Inner velocity controller is modeled, and the additional torque due to the gravity effect is augmented using superposition principle; then, the analysis of the overall system and controller is conducted through the grasping experiment. As a result of this, competent results are obtained.

Human's action strategy is a good source of robot controller design. For there is no decisive criterion on balance control during manipulation tasks, human motion data are obtained and analyzed in this paper. Based on the observation of the center of mass (CoM) being proportional to target object distance but limited inside the supporting polygon, the bound-proportional CoM planner is proposed. Along with the CoM planner, whole-body balance and grasping controller for a dualarm robot is suggested in a simple and computationally efficient structure. Dynamic simulation is conducted for validation, and showed competent results.

This paper discusses an unified method of the tracking and balancing controls for leg-wheel hybrid structures in an effort to improve the mobility over hard, flat surfaces. Preliminarily, we analyzed the contact constraint to formulate a dynamically decoupled model in the task space. Then, inequality constraints were determined to restrict the dynamic behavior of the system within the given bounds for the dynamic stability and the actuator saturation. The inequality constraints were applied to the reference control input that was designed for the mechanism to traverse the desired trajectories without the constraints. To find the constrained control input, a quadratic objective function was proposed to minimize the modification error of the control inputs. We tested the effectiveness of the proposed algorithm by comparing simulation results with our previous research.

There are many difficulties in dexterous object manipulation by multi-fingered hands, due to redundant degree-of-freedom of the entire system and uncertainties in interaction with the object. In this paper, however, 3D object manipulation without any external sensors are attempted. Under the assumption of point contact without rolling, the object position and orientation are computed in relative sense and used as the feedback for object manipulation. Overall system dynamics including two cone-shaped finger robots and an arbitrary object is modeled, and the closed-loop system stability is analyzed based with the proposed controllers for stable grasping and object position/orientation control. In order to validate the proposed method, dynamic simulation is conducted, and showed complacent results.

This paper proposes a new manipulator concept applied to a mobile robot manipulation system for reducing robot size and weight or increasing its work capacities such as a payload, operating radius, and operating speed. In detail, we propose a new robotic manipulator that uses stackable 4-BAR mechanisms for mobile manipulation robot. The proposed mechanism provides a clear advantage in which all the actuators can be separated from the working joints. Thus, the mechanism is able to select the Center of Mass (CoM) and the Zero-Moment Point (ZMP) in arbitrary points without any support from ZMP controller or ZMP compensation method. To confirm efficiency of the new manipulator, this paper addresses a design method using the simplified beam theory, based on the well-known Finite Element Method (FEM) for structural stiffness analysis of linkages. The reason behind this is that the CoM and ZMP are dependent on the weight of the motors and the linkages. Ultimately, we show the efficiency of the proposed stackable manipulator through simulations and experiments.

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.

This paper discusses a balance control method with kinodynamic constraints for leg-wheel hybrid structures in an effort to improve the mobility of locomotion over hard, flat surfaces. Preliminarily, we defined a prioritized Jacobian and a prioritized inverse of Jacobian to formulate the dynamically decoupled model in the task space for the constrained multi-contact multi-rigid-body system with a floating base. Our strategy has two tracks to accommodate the uncertainty and the complexity of the system dynamics. 1) The time-delay estimation and control are combined with the nonlinear programming. 2) Whole kinodynamic constraints are derived as functions of the control input. The proposed balance control algorithm allows the system to traverse desired trajectories satisfying the kinodynamic constraints and improves the mobility of locomotion. The effectiveness of the algorithm is tested with the dynamic simulations.

This paper addresses a torque control method in a high friction robot manipulator. A stiction feed-forward compensator is proposed to eliminate the control problem caused by the nonlinear friction and disturbance. In order to control a robot manipulator with unknown effects, a time-delay control method is used to control the torque. One degree of freedom flexible joint robot manipulator with a joint torque sensor is used to show the performance of the proposed control method.

In recent, control approaches for the human-like behavior in the field of service robotics have been attracting considerable attention, since most humans or animals perform various tasks uncomplicatedly. Hence simple control methods based on gaining a physical insight into human reaching movement in redundancy of DOFs have been proposed. In comparison with the conventional approaches, the proposed method tries to control directly robotic systems in task-space with the control signal composed of linear superposition of three terms 1) joint-damping, 2) virtual spring, and 3) virtual damper in task-space. In particular, our work contains a muscle tension effect of a human under the gravity. This give birth to energy efficient natural motions avoiding problems on repeatability of the motion and ill-posedness problems emerged in most of redundant DOF systems. Thus, this paper exhibits expendability of the position control into the orientation control and compliant behavior. It is verified with a real robotic arm that satisfies human-like movements and motion repeatability under kinematic redundancy of joints.

Redundant manipulator control usually brings about a lot of complexity. This paper proposes a quite simple approach for concurrent set-point regulation of position and orientation of a redundant manipulator using the virtual spring-damper hypothesis, which offers human-like movements without solving the inverse dynamics. For orientation, quaternion representation is applied to give singularity-free orientation control. Augmenting quaternion feedback to the state vector of the system, the stability is analytically proved, and dynamic simulation is conducted for validation of the method. Competition between position and orientation control is also briefly discussed based on the simulation results.

In this paper, we have developed a novel control law to exhibit human-motion characteristics in redundant robot arm-trunk systems for reaching tasks. This newly developed method nullifies the need for the computation of pseudo-inverse of Jacobian while the formulation and optimization of any artificial performance index is not necessary. The time-varying properties of the muscle stiffness and damping as well as the low-pass filter characteristics of human muscles have been modeled by the proposed control law. The newly developed control law uses a time-varying damping shaping matrix and a bijective joint muscle mapping function to describe the human-motion characteristics for reaching motion like quasi-straight line trajectory of the end-effector and symmetric bell shaped velocity profile. The aspect of self-motion and repeatability, which are inherent in human-motion, are also analyzed and successfully modeled using the proposed method. Simulation results show the efficacy of the newly developed algorithm in describing the human-motion characteristics.

There are many difficulties in operating a humanoid which has high degree-of-freedom and instability in balancing its body. In addition, due to the shape of a humanoid, it is expected to have motions like a human. In order to overcome its operational difficulties and to provide a humanlike motion, a teleoperation with the motion imitation is studied in this paper. Specifically, a framework for online generation of a footprint from a human walking motion is proposed. The human walking motions acquired from a motion capture device are parameterized and normalized to give a human independent foot motion. The normalized parameters are restored by a humanoid considering its hardware limit. The restored footprints generate a walking trajectory of a humanoid, which imitates the human walking motion in terms of the footprint. Experiments are conducted with MAHRU-R, a humanoid robot developed in KIST.

Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

This paper proposes a new walking pattern generation method for humanoid robots. The proposed method consists of feedforward control and feedback control for walking pattern generation. The pole placement method as a feedback controller changes the poles of system in order to generate more stable and smoother walking pattern. The advanced pole-zero cancellation by series approximation (PZCSA) as a feedforward controller plays a role of reducing the inherent property of linear inverted pendulum model (LIPM), that is, non-minimum phase property due to an unstable zero of LIPM and tracking efficiently the desired zero moment point (ZMP). The efficiency of the proposed method is verified by three simulations such as arbitrary walking step length, arbitrary walking phase time and sudden change of walking path.

A closed-loop observer to extract the center of mass (CoM) of a bipedal robot is suggested. Comparing with the simple conversion equation of using just joint angle measurements, it enables to get more reliable estimates by using both joint angle measurements and F/T sensor outputs at the ankle joint. First, a nonlinear type observer is constructed in the extended Kalman filter framework to estimate the flexible rotational motion of biped. It is based on the inverted pendulum model with flexible beam which is to simply address the flexible behavior of a biped, specifically in the single support phase. Then, the predicted estimates of CoM by the flexible motion observer are combined with the outputs of the CoM conversion equation and the final estimates will be determined according to the weighting value which penalizes the flexible motion model and the CoM conversion equation. Simulation results are followed to show the effectiveness of the proposed scheme.

This paper proposes a neural oscillator based control to attain rhythmically dynamic movements of a robot arm. In human or animal, it is known that neural oscillators could produce rhythmic commands efficiently and robustly under the changing task environment. In particular, entrainments of the neural oscillator play a key role to adapt the nervous system to the natural frequency of the interacted environments. Hence, we discuss how a robot arm controls for exhibiting natural adaptive motions as a controller employing the entrainment property. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a real robot arm. Then this work shows the performance of the robot arm coupled to neural oscillators in various tasks that the arm traces a trajectory. Exploiting the neural oscillator and its entrainment property, we experimentally verify an impressive capability of self-adaptation of the neural oscillator that enables the robot arm to make adaptive changes corresponding to an exterior environment.

It is known that biologically inspired neural systems could exhibit natural dynamics efficiently and robustly for motion control, especially for rhythmic motion tasks. In addition, humans or animals exhibit natural adaptive motions without considering their kinematic configurations against unexpected disturbances or environment changes. In this paper, we focus on rhythmic arm motions that can be achieved by using a controller based on neural oscillators and virtual force. In comparison with conventional researches, this work treats neither trajectories planning nor inverse kinematics. Instead of those, a few desired points in task-space and a control method with Jacobian transpose and joint velocity damping are merely adopted. In addition, if the joints of robotic arms are coupled to neural oscillators, they may be capable of achieving biologically inspired motions corresponding to environmental changes. To verify the proposed control scheme, we perform some simulations to trace a desired motion and show the potential features related with self-adaptation that enables a three-link planar arm to make adaptive changes from the given motion to a compliant motion. Specifically, we investigate that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

This paper proposes a new walking pattern generation method for humanoid robots. This paper uses the linear inverted pendulum model (LIPM) which is composed of zero moment point (ZMP) and center of mass (CoM). Based on LIPM, the proposed method consists of feedforward control and feedback control for walking pattern generation of humanoid robots. The linear quadratic regulator(LQR) as a feedback controller tracks the desired ZMP according to footprints of humanoid robots and makes poles of LIPM stable. The feedback controller, pole-zero cancelation by series approximation (PZCSA) plays a role of reducing the inherent property of LIPM and approximating the transfer function of the overall system including LIPM and controllers to be unity. The usefulness of the proposed method is verified by simulations such as arbitrary time intervals of support phases, arbitrary desired ZMP position and sudden changed desired ZMP position. And the validity of the proposed method is confirmed by the experiment of a humanoid robot using a joystick.

This paper presents balance and vibration control algorithm for bipedal humanoid robots in the motion embedded CoM Jacobian framework. The vibration control is employed during a single supporting phase, which can suppress residual vibration of the un-modelled flexibility. Because the previously proposed walking control method in the resolved momentum control framework is based on the rigid body motion, vibration control algorithm which compensates for residual vibration can make the humanoid motion into rigid body motion. The vibration control consists of the modified global planning CoM trajectory and modified ankle joint controller in the motion embedded CoM Jacobian framework. The parameters of the controller are acquired easily using MATLAB System Identification Tool box. Also, balance control algorithm which controls body orientation is applied to the whole body coordination framework. By dynamic walking experiments using a humanoid robot MAHRU-R, the validity of the proposed control methods is verified.

This paper introduces an omni-directional walking pattern generation method according to various footprints for a humanoid robot. We propose three step modules for generating stable walking pattern based on zero moment point(ZMP) and linear inverted pendulum model. The proposed step module uses both the characteristics of periodicity and the least square method in order to reduce the fluctuation range of the ZMP trajectory according to various footprints. In order to implement it, the trajectory of the desired ZMP is designed with the quartic polynomials. This method is more insensitive to variation of the walking pattern owing to the initial value of the ZMP and CoM and various walking step length. And it makes more efficient trajectory of CoM on account of the slope of ZMP in the single support phase. The effectiveness of the propose method is verified by simulation.

This paper proposes the walking pattern generation method, the kinematic resolution method of center of mass (CoM) Jacobian with embedded motions, and the design method of posture/walking controller for humanoid robots. First, the walking pattern is generated using the simplified model for bipedal robot. Second, the kinematic resolution of CoM Jacobian with embedded motions makes a humanoid robot balanced automatically during movement of all other limbs. Actually, it offers an ability of whole body coordination to humanoid robot. Third, the posture/walking controller is completed by adding the CoM controller minus the zero moment point controller to the suggested kinematic resolution method. We prove that the proposed posture/walking controller brings the disturbance input-to-state stability for the simplified bipedal walking robot model. Finally, the effectiveness of the suggested posture/walking control method is shown through experiments with regard to the arm dancing and walking of humanoid robot.

This paper presents a walking algorithm for bipedal humanoid robots in the motion-embedded CoM Jacobian framework with angular momentum resolution. Walking constraints in the previous resolved momentum control framework are reformulated to utilize motion-embedded CoM Jacobian and reduce computational complexity. In this method, the conventional linear momentum control is replaced by the explicit CoM manipulation and the angular momentum equation only is used for upper body motion resolution without any other subject variables; whole body cooperative motions are completed with the walking constraints expressed by motion-embedded CoM Jacobian. The conventional resolved momentum control is able to play a more unified framework role owing to this method and lose computational weight while making compatibility with the motion-embedded CoM Jacobian framework. Validity and walking stability are demonstrated by the experiment on the real robot, MAHRU-II

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.

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.

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.

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.

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.

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.>