基于人体手臂运动意图反馈的人机顺应协作

Human-robot Compliant Collaboration Based on Feedback of Motion Intention of Human Arm

本文旨在通过人体手臂运动意图反馈来提高人与机器人协作的顺应性.首先通过自编码器和反向传播神经网络融合表面肌电信号和机器视觉信号,对人体手臂肘关节力矩和运动意图进行预测.然后,将人体手臂肘关节力矩反馈给机器人,以此预测手臂的运动意图并让机器人作出顺应响应,从而实现人机协作.最后,结合主观评价指标和客观评价指标,在人机协作锯木头实验中对比了3种不同协作模式的效果.与无运动意图反馈的人机协作相比,有反馈的人机协作下的协作交互力波动幅度减小了153.39 N,任务完成时间减少了19.25 s.实验结果表明,有人体手臂运动意图反馈的人机协作能够提高人与机器人协作的顺应程度,与人-人协作效果类似.

This paper aims to improve the compliance of human-robot collaboration based on the feedback of human arm motion intention. Firstly, the autoencoder and backpropagation neural network(BPNN) are used to fuse the surface electromyography(sEMG) and the machine vision signals for estimating the human elbow joint torque and motion intention.Then, the human elbow joint torque is feedback to the robot, and the motion intention of the arm is estimated to make the robot act adaptively, and thus human-robot collaboration is realized. Finally, 3 different collaboration patterns are compared by combining objective and subjective evaluation indices through the wood sawing experiments in human-robot collaboration.Comparing with the case of the human-robot collaboration without motion intention feedback, the fluctuation amplitude of the interaction force is reduced by 153.39 N and the task completion time is reduced by 19.25 s in the case with feedback.Experimental results show that the human-robot collaboration with the feedback of human arm motion intention can improve the compliance of human-robot collaboration, and the effect is similar to that of the human-human collaboration.