This paper deals with the problem of dynamic trajectory planning for a group of unmanned aerial vehicles (UAVs) in unknown environments. The existing methods often suffer from excessive computational burden, which creates the gap between theoretical approaches and practical swarm deployment. To overcome these limitations, this paper proposes a distributed cooperative planning system (DCPS). The system consists of two levels: a trajectory planning level and a cooperative trajectory planning level. On the trajectory planning level, a kinodynamic Gaussian potential B-spline (KGPB) approach is designed by combining the local kinodynamic-A-star method and the Gaussian potential field B-spline method. Specifically, in the front-end, a trajectory is first generated by using the local kinodynamic-A-star method based on kinematics-dynamics constraints. And then, in the back-end, the trajectory is further optimized by using the Gaussian potential B-spline (GPB) method. On the cooperative trajectory planning level, a back field neighbor replanning (BFNP) approach is proposed, where each UAV only needs to communicate with the nearest neighbors. According to the potential collision region of the front UAV, the trajectory of the current UAV is dynamically improved to ensure safe and stable flight such that communication costs are significantly improved. Finally, the simulation results demonstrate that the proposed DCPS achieves at least a 27% increase in average velocity and a 24% reduction in traversal time for the UAV swarm compared to prior methods. The experimental outcomes provide further validation that the proposed DCPS can generate efficient and safe trajectories. For particular cases, the UAV operates at 97% of its maximum possible velocity. The proposed DCPS provides a reliable solution for a group of unmanned aerial vehicles in unknown environments.

In this paper, two technologies are proposed to deal with the problem of flight safty of multiple unmanned aerial vehicles (UAVs) in unknown environments. One technology is to optimize the front-end path generated by traditional path planning methods in order to better match the dynamics of UAVs to obtain the back-end movement trajectories of UAVs. The other technology is to introduce the collision detection adjustment region such that collision avoidance can be realized for multiple UAVs by dynamic replanning of UAV’s trajectory under local neighborhood communication. Finally, according to simulation and real-world experimental results, the effectiveness of the proposed technologies is verified for the flight safty of multiple UAVs in unknown environments.

This article deals with the problem of target tracking and detecting in unknown environments by designing two new algorithms for an autonomous aerial vehicle (AAV). First, an auto-Gaussian-GRU-predictive (AGUP) algorithm is designed to solve the tracking problem of a dynamic target in unknown environments. By integrating Gaussian process regression and gated recurrent unit neural networks, the AGUP algorithm can predict the motion trajectory of a dynamic target. Second, a Tabu search interpolated B-spline (TBL) algorithm is also proposed to solve the problem of optimal path planning for multiple stationary targets. The TBL algorithm can efficiently plan the visiting paths and also can enable the path smooth. Third, both AGUP and TBL algorithms are combined with the model predictive control (MPC) approach in order to guide AAVs to track and detect the targets. Finally, simulation and experimental results show that the AGUP-MPC algorithm exhibits excellent tracking capability. In addition, the TBL-MPC algorithm effectively plans the optimal and smooth detection path and controls AAVs to orderly visit multiple stationary targets.

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.

In this paper, the real-time motion planning framework for unmanned aerial vehicle (UAV) is proposed to solve the dynamic target tracking problem in complex environments. The framework is applicable to 3D path planning and trajectory optimization of UAV, which can effectively reduce the unsmoothness during UAV flight and achieve real-time tracking of dynamic targets in 3D space. The framework is divided into two parts: the front-end uses a graph search-based method to find the shortest path for the UAV to approach the dynamic target, while the back-end uses the GP (Gaussian Potential)-B-Spline soft-constrained trajectory optimization method to optimize the shortest path of the front-end, and design the trajectory that conforms to the motion for the UAV. The results show that the method exhibits excellent tracking performance in complex environments and has a wide potential for practical applications.

This paper mainly considers the navigation problem of unmanned aerial vehicle (UAV) in an unknown environment. Traditional path planning method relies on accurate model parameters and environment maps, which has poor adaptability. Therefore, this paper adopts the deep reinforcement learning algorithm to accomplish the navigation task. The classical proximal policy optimization (PPO) algorithm lacks the perception of the correlation between UAV action and state makes difficult for the UAV to choose the optimal path, thus affecting the success rate and speed of navigation. To solve this problem, this paper adds a long short-term memory (LSTM) network to the policy and evaluation network of the PPO algorithm so that the UAV can refer to the preceding status and action information during path planning. The method is extended to three-dimensional motion space. Simulation results demonstrate that the LSTM-PPO algorithm designed in this paper can complete navigation tasks in unknown environments, and show stability in continuous state space and continuous action space. Meanwhile, compared with the PPO algorithm, the success rate of navigation and average arrival time is significantly improved.

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

This article presents an underactuated gripper with a single actuator to perform a robust pinch capability under various environmental constraints. Its fingertips have the ability not only to slide on sloped surfaces of the tabletop on which the objects to be grasped are located, but also to lift lightweight objects for subsequent tasks, such as vertical in-hand manipulation and simple peg-in-hole tasks. The finger mechanism is constructed with a well-known four-bar linkage, including two phalanges, and then its fingertip is modeled as a slider to realize the fingertip sliding, which is a four-bar driven slider-crank. Kinematic and static analyses are conducted to determine the operation principle of the slider through the input-output relationship on the Plücker coordinates. Especially, the vector of the force, which the fingertip exerts, is analyzed, and then its direction is designed through the dimensional synthesis of the linkage according to several criteria for sliding and lifting. Simulations and experiments are conducted to verify the designed directions and performances of the synthesized linkage. Finally, the gripper equipped on a manipulator is demonstrated under contacts and collisions with various environmental constraints to confirm the feasibility and effectiveness of the gripper design.

Conventional dexterous grippers are not only heavy and complex but also task dependent. As a result, they cannot provide power, intermediate and precision grasping together. We propose usability evaluation of a novel gripper mechanism which can provide 1-Degree of Freedom (DOF) parallel grasping and 2-DOF independent pinching by using a closed chain architecture. The usability of our design and one commercial product has been evaluated based on various grasping tasks: adaptive grasping, parallel pinching grasping, independent pinching grasping and contact grasping. The success rate has been evaluated to validate the excellent usability of the proposed 3-DOF gripper.

The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the global asymptotic stability during free motion when the external force is absent. The proposed controller is simple to be implemented for a complex tendon-driven mechanism because it requires only gravity compensation. In addition, it brings a robustness to the entire control system. And finally, the control strategy can be treated as one of the impedance control schemes so as to achieve the desired performance efficiently.

The paper presents design, fabrication and characteristics of two kinds of conductive Knit-covered Pneumatic Artificial Muscle (it is called as k-PAM in the paper) actuators, in which two different knits are made by braiding silver-coated (conductive) yarn and spandex (non-conductive) yarn with different stitch methods. The k-PAM is able to measure the change in length of the actuator body according to the applied air pressures as well as the strain due to external force. A complete fabrication method is presented to make the actuator work for higher pressure (≥ 300[kPa]). Since the force generated by the actuator is decoupled from the external force, ultimately, it can be directly used to measure not only the length but also the force. Experimental validations are performed describing the characteristics of two different types of k-PAMs. It is expected that the k-PAM can be used directly for robotic applications in higher pressure condition, while the semi-permanent conductive knit provides the actuator with durability in high repetitive operation environment.

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.

All the people have their own individual force ranges to feel the sense of touch and its minimum value is called light touch threshold (in short LTT). For instance, if a touching force is very weak, people do not know whether the object is contacted to their skin, even though it is pushing the skin already. This paper presents a novel apparatus to measure the LTT felt by each subject, which is referred to as Active von Frey (AvF) in this paper. As far as the authors know, the LTT measurement device is for the first time proposed in this paper. It is possible for the AvF to provide the touching force ranges from 1[mgf] to 400[mgf]. In order to provide an accurate touching force for the subject, D'Arsonval movement is chosen as an actuator to rotate a touching AvF pin. Both an electric current applied to the D'Arsonval movement and a rotational angle of AvF pin are utilized to calculate the touching force by the electro-mechanical statics. In addition, the image processing technique is used to measure the rotational angle of AvF pin. Finally we show that the LTTs are very different from individual to individual through the experimental results of 10 participants. We hope that the personalized parameters is used to design or control haptic or tele-operation devices for various applications.

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.

This paper presents a continuous control method of electric wheelchair based upon surface electromyographic signals (EMG), ultimately, for quadriplegics. The proposed method utilizes two EMG signals as inputs for the muscle-computer interfaces (MCI). Since Zygomaticus major muscles located in the right and left sides of human face are able to excise individually and to control contractile forces voluntarily, the surface EMG signals of both muscles satisfy core requirements for the development of EMG-based electric wheelchair control system, such as independent and continuous speed control of two wheels. For this, the envelopes of the signal waveforms are first extracted to reflect the moving average activities by using RMS (root mean squares) operations. Also, in order to obtain the desired linear and angular velocities of the electric wheelchair, the RMS signals are processed sequentially as follows; normalizing the RMS signals and then determining the control inputs of the electric wheelchair. Finally, the effectiveness of the proposed control scheme is verified through several experiments.

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.

Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%.

This paper proposes a new actuator system using a movable pulley for a bio-mimetic system and wearable robot applications, which is able to store energy in advance and to release it in such a way to satisfy desired speed and force control performances, simultaneously. Since new actuator system consists of two motors (dual-motors), a movable pulley, and series spring connection for high speed/force operation characteristics, it is referred to as Dual-motor system using a Movable Pulley with a Series Elastic Actuator (DuMP-SEA) in this paper. Also, the proposed actuator system not only combines individual speed/force operation region of each motor, but also extends the combined operation region by using the energy stored in advance. Finally, we suggest the extended operation region (high speed and high force characteristics) obtained by using the DuMP-SEA through simulations and experiments.

This paper proposes a new planar robotic manipulator using stackable 4-BAR mechanisms for various applications such as a manipulator of surgical robot and a manipulator of field robot. The proposed manipulator has an advantage that all driving actuators can be separated from the mechanical mechanism, in other words, we are able to separate all electrical components such as electrical actuators and wirings from mechanical linkage/joint components in the robotic manipulator. Thus the robotic manipulator including working joints and linkages can be manufactured using one or a few material(s) with light-weight and slim-size. Also, the proposed mechanism does not require the actuators to attach directly to driving joints and it can be independently controlled. In addition, we suggest the kinematic analysis of the proposed manipulator composed of input mechanisms, multiple 4-BAR mechanisms and output mechanisms. Finally, a variety of simulation results and a prototype are suggested to show the effectiveness of the stackable 4-BAR mechanisms.

In tightly coupled cooperative manipulation of two arms, an interaction force control is one of the important issues. In this paper, a sensor-less interaction force control in a de-burring task using a dual-arm is introduced. The dual arm manipulation increases the motion dexterity in the de-burring task as compared to the single arm manipulation. A closed form interaction force control algorithm is proposed, which does not require any force sensor. In the motion planning, the 12 Lagrangian coordinates of the system are decomposed into 9 motion degrees and 3 constrained degrees. Then, the whole dual arm can be modeled as a kinematically redundant robot. The primary task is to control the 9 motion degrees and the secondary task is to control the interaction force and the relative motion between the two arms. The concept of virtual joints is employed to represent the constrained degrees, which include the interaction force and the relative motion between the two arms. Through the simulation results, we show that the smooth de-burring task can be accomplished by using the suggested interaction force model without force sensor.

In molecular modeling, it is quite useful to exercise real-time rendering of solvent-accessible surfaces for three dimensional models. The real-time rendering helps researchers in analyzing three dimensional structures and behaviors of molecular simulations. The researchers can determine whether critical parts of molecular models are correctly visualized and properly combined at right locations. However, it is quite difficult to render solvent-accessible surfaces in real-time using conventional molecular modeling tools. It is because the surfaces are visualized as isosurfaces which express chemical information and three dimensional positions. In this paper, we propose a method which facilitates real-time rendering of solvent-accessible surfaces for three dimensional molecular models. We evaluated real-time interactivity of our method with molecular models. Therefore, researchers can observe and manipulate solvent-accessible surfaces of three dimensional molecular models in real-time.

In this paper, we present a novel approach to glycomics: collaborative molecular simulation. Glyco-MGrid is a collaborative grid environment for glycobiology which supports simulation, databases and trajectory analyses in an integrated way. It allows the global glycomics community to share simulation results and to continue further research from previous results by other individual scientists or research groups. Currently, over five hundreds of simulation results for glycans and glycoproteins are available from the Glyco-MGrid system (http://glyco.mgrid.or.kr) running on the grid testbed at the Konkuk University Applied Grid Computing Center. In addition, we have been extending the Glyco-MGrid system to build a shared simulation data repository for the Avian flu grid project at PRAGMA (http://avianflugrid.pragma-grid.net).

This paper suggests the real time tracking algorithm for human arm motion by processing the signals of surface EMG (ElectroMyoGram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the desired flexion/extension motions of both the elbow and shoulder of human. Also, we show the validity of the suggested algorithms through the experimental results.

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 proposes the walking pattern generation method, the kinematic resolution method of CoM (center of mass) Jacobian with an embedded motion, and the walking controller design method 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 motion makes a humanoid robot balanced automatically during the movement of the all other limbs. Actually, it offers the ability of whole body coordination to the humanoid robot. Third, the walking controller is composed of the CoM controller minus the ZMP (zero momentum position) controller. Also, we show that the proposed walking controller brings the disturbance input-to-state stability (ISS) for the simplified bipedal walking robot model. Finally, the effectiveness of the proposed kinematic resolution method and walking controller is shown through experiments in regard to humanoid robot dancing and walking

This paper proposes the indirect zero momentum position (ZMP) controller for biped robot systems and proves its disturbance input-to-state stability (ISS). The ZMP control has been used as a standard method for stable walking control of biped robot systems. Since the ZMP information consists of position and acceleration of the center of gravity (COG) for a biped robot system, the ZMP can be indirectly controlled by the motion of COG. In this paper, the reference COG planner is developed by solving the reference ZMP differential equation. The indirect ZMP controller is proposed to derive the desired motion of COG from the reference ZMP trajectory and the COG error (the difference between the reference and real COG). The ISS of the proposed indirect ZMP controller is proved for the simplified biped robot model. The robustness of the proposed indirect ZMP controller is shown in simulation.

In this paper, we present a simple method for visual localization of indoor mobile robots based on a natural landmark model. Only two natural lines and an intersection point are used for localization to take the advantage of fast detection. To track the landmark model, Lucas-Kanade optical flow algorithm is applied by using gradient descent. Then, by adopting reasonable assumption for indoor environments, a quick localization method for mobile robots from correspondent landmark is proposed in a linear technique. Experimental results that demonstrate the robustness of the method with respect to image illumination and noises are also presented. The performance in indoor environments shows the feasibility of the proposed localization algorithm in real-time.

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.

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.

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.

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.

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.