Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

Zeng, Chao; Yang, Chenguang; Chen, Zhaopeng

doi:10.1007/s11432-019-2748-x

Cited by 49 publications

(18 citation statements)

References 30 publications

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“…From the aforementioned study, it is notable that the impedance control algorithm in the Cartesian space is a better compliant control strategy in that it can transform force error signals into position error signals [ 27 ]. Impedance control can achieve better force tracking results [ 28 , 29 ]. However, pure impedance control has a general trajectory tracking accuracy and cannot get a high-precision trajectory/force tracking effect for dual-arm cooperative manipulators.…”

Section: Introductionmentioning

confidence: 99%

Dual-Arm Coordinated Control Strategy Based on Modified Sliding Mode Impedance Controller

Liu

Zhu

et al. 2021

Sensors

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To meet the high-accuracy position/force control requirements of dual-arm robots for handling a target object, a control algorithm for dual-arm robots based on the modified sliding mode impedance controller MSMIC(tanh) is proposed. First, the combinative kinematics equation of the dual-arm robots and the unified dynamics model combining the manipulated object is established. Second, according to the impedance control motion model for the object, the desired joint angular accelerations of the manipulators are obtained, and the sliding mode controller based on the hyperbolic tangent function as the switch function is introduced to design the coordinated control strategy for dual-arm robots. The stability and convergence of the designed controller are proved according to the Lyapunov function theory. Finally, the operation tasks of the coordinated transport the target object for dual-arm robots are carried out in the simulated experiment environment. Simulation results show that the proposed control scheme can stably output the required internal force and achieve a high-precision trajectory tracking effect while reducing the periodic torque and joint chattering amplitude generated in the conventional sliding mode control algorithm.

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Section: Introductionmentioning

confidence: 99%

Dual-Arm Coordinated Control Strategy Based on Modified Sliding Mode Impedance Controller

Liu

Zhu

et al. 2021

Sensors

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“…Recently, collaborative robots have been deemed crucial where robots and humans work in proximity [1,2]. Human–robot collaboration poses many challenges where humans and robots work inside a shared workspace [3]. This close collaboration enables the creation of a human–robot synergy that merges complementary advantages of both humans and robots: the high precision and repetitiveness of robots and complex skills of humans [4].…”

Section: Introductionmentioning

confidence: 99%

Co-carrying an object by robot in cooperation with humans using visual and force sensing

Zhang

Liu

et al. 2021

Phil. Trans. R. Soc. A.

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Human–robot collaboration poses many challenges where humans and robots work inside a shared workspace. Robots collaborating with humans indirectly bring difficulties for accomplishing co-carrying tasks. In our work, we focus on co-carrying an object by robots in cooperation with humans using visual and force sensing. A framework using visual and force sensing is proposed for human–robot co-carrying tasks, enabling robots to actively cooperate with humans and reduce human efforts. Visual sensing for perceiving human motion is involved in admittance-based force control, and a hybrid controller combining visual servoing with force feedback is proposed which generates refined robot motion. The proposed framework is validated by a co-carrying task in experiments. There exist two phases in experimental processes: in Phase 1 , the human hand holds one side of the box object, and the robot gripper of the Baxter robot automatically approaches to the other side of the box object and finally holds it; in Phase 2 , the human and the Baxter robot co-carry the box object over a distance to different target positions. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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“…In this human-like control method, an iterative adaptive law was designed to minimize the tracking error and the stiffness term (the difference of the desired stiffness and the controller stiffness). Furthermore, a robot controller adapting the stiffness and reference trajectory simultaneously and a bio-inspired controller imitating human motor learning properties were provided for the interaction with the timevarying environment [24,25]. However, in a rehabilitation training scenario, iterative learning control only focuses on how to adjust the controller to ensure the convergence of the system's state as well as the interaction force.…”

Section: Introductionmentioning

confidence: 99%

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Cao

Cheng

Yang

et al. 2021

Sci. China Technol. Sci.

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Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. Control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system's stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient's motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

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Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

Cited by 49 publications

References 30 publications

Dual-Arm Coordinated Control Strategy Based on Modified Sliding Mode Impedance Controller

Dual-Arm Coordinated Control Strategy Based on Modified Sliding Mode Impedance Controller

Co-carrying an object by robot in cooperation with humans using visual and force sensing

Iterative assist-as-needed control with interaction factor for rehabilitation robots

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