Brain-Actuated Control of Dual-Arm Robot Manipulation With Relative Motion

Li, Zhijun; Wang, Yüan; Zhao, Shuai; Yu, Zhuoping; Kang, Yu; Chen, C. L. Philip

doi:10.1109/tcds.2017.2770168

Cited by 24 publications

(22 citation statements)

References 29 publications

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“…It is worth emphasizing that, unlike nonlinear multiagent systems [8,9] studying the relation between agents, CMMs consider the interaction with the rigid object. In the literature of CMMs' control objectives, they can be classified into two categories, i.e., multiple mobile robot manipulators in cooperation carrying a common object with unknown parameters and disturbances [7,[10][11][12][13][14][15][16][17][18]; one of them tightly holds the object by the end effector, and the remaining mobile manipulators' end effector follows a trajectory on the surface of the object [19,20]. Most of the existing control literature of networked robotics mainly focuses on the implementation of nonlinear controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the motion/force control law was proposed using the computation of constraint force without the optimality problem [15]. An approach of optimization for the dual-arm manipulator with the consideration of relative Jacobian was mentioned in [16] by the QP solver. e extension from dual-arm manipulators to multiarm robots is discussed with the classical backstepping structure and optimal control law being constructed to tackle optimal behaviors involving obstacle avoidance and energy saving [11].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

Nam

Khanh

Nguyen

2021

Mathematical Problems in Engineering

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This paper presents an adaptive reinforcement learning- (ARL-) based motion/force tracking control scheme consisting of the optimal motion dynamic control law and force control scheme for multimanipulator systems. Specifically, a new additional term and appropriate state vector are employed in designing the ARL technique for time-varying dynamical systems with online actor/critic algorithm to be established by minimizing the squared Bellman error. Additionally, the force control law is designed after obtaining the computation of constraint force coefficient by the Moore–Penrose pseudo-inverse matrix. The tracking effectiveness of the ARL-based optimal control is verified in the closed-loop system by theoretical analysis. Finally, simulation studies are conducted on a system of three manipulators to validate the physical realization of the proposed optimal tracking control design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

Nam

Khanh

Nguyen

2021

Mathematical Problems in Engineering

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“…On the other hand, the operator can provide information and transfer skills to the robot, also playing the role of a supervisor of the robot. With the involvement of the human operator, the flexibility of the process increases, which is benefit to accomplish a number of tasks [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Neural Learning Enhanced Variable Admittance Control for Human–Robot Collaboration

et al. 2020

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In this paper, we propose a novel strategy for human-robot impedance mapping to realize an effective execution of human-robot collaboration. The endpoint stiffness of the human arm impedance is estimated according to the configurations of the human arm and the muscle activation levels of the upper arm. Inspired by the human adaptability in collaboration, a smooth stiffness mapping between the human arm endpoint and the robot arm joint is developed to inherit the human arm characteristics. The estimation of stiffness term is generalized to full impedance by additionally considering the damping and mass terms. Once the human arm impedance estimation is completed, a Linear Quadratic Regulator is employed for the calculation of the corresponding robot arm admittance model to match the estimated impedance parameters of the human arm. Under the variable admittance control, robot arm is governed to be complaint to the human arm impedance and the interaction force exerted by the human arm endpoint, thus the relatively optimal collaboration can be achieved. The radial basis function neural network is employed to compensate for the unknown dynamics to guarantee the performance of the controller. Comparative experiments have been conducted to verify the validity of the proposed technique.INDEX TERMS Impedance estimated model, variable admittance control, physical human-robot collaboration, neural networks.

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“…Initially, robots are designed with an idea of developing a mechanism to replace/support human when interacting with the environment [1][2][3][4]. Along with the speedy evolution of control engineering, computer science, and mechanical technologies, robots have an inclination toward possessing the human form [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Super-twisting sliding mode based nonlinear control for planar dual arm robots

Nguyễn¹,

Vu²

2020

Bulletin EEI

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In this paper, a super-twisting algorithm sliding mode controller is proposed for a planar dual arm robot. The control strategy for the manipulator system can effectively counteract chattering phenomenon happened with conventional sliding mode approach. The modeling is implemented in order to provide the capability of maneuvering object in translational and rotational motions. The control is developed for a 2n-link robot and subsequently simulations is carried out for a 4-link system. Comparative numerical study shows that the designed controller performance with good tracking ability and smaller chattering compared with basic sliding mode controller.

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Brain-Actuated Control of Dual-Arm Robot Manipulation With Relative Motion

Cited by 24 publications

References 29 publications

Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

Neural Learning Enhanced Variable Admittance Control for Human–Robot Collaboration

Super-twisting sliding mode based nonlinear control for planar dual arm robots

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