Abstract
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.
Highlights
Robot is expected to compliantly perform a lot of complicated tasks
We propose a stiffness mapping strategy between the human arm and the robot arm in order to achieve a more coordinated human-robot collaboration according to the estimated stiffness
The main contribution of this paper can be summarized as follows: 1)This paper proposes a novel framework for human-robot interaction, i.e, a human-robot impedance mapping strategy and variable admittance control based on the estimated human arm impedance parameters. 2)An NN-based controller is adopted to enhance the tracking performance
Summary
Robot is expected to compliantly perform a lot of complicated tasks. Robots liberate humans from the mindnumbingly repetitive work routines. Robots have become a significant part of factory production, assisting in repetitive and dangerous operations. It is difficult to address this problem by humans working alone or by automated robots, which raises the demand for human-robot collaboration. Robots gradually participate in the daily life of humans and the collaboration between human and robot has attracted more and more attention in recent works, for the purpose of improving the safety and the reliability of the robot systems. Paper [1] presents a novel approach to estimate the motion intention of the operator and the unknown robot dynamics
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