This paper presents a robot teaching system based on hand-robot contact state detection and human motion intent recognition. The system can detect the contact state of the hand-robot joint and extracts motion intention information from the human surface electromyography (sEMG) signals to control the robot's motion. First, a hand-robot contact state detection method is proposed based on the fusion of the virtual robot environment with the physical environment. With the use of a target detection algorithm, the position of the human hand in the color image of the physical environment can be identified and its pixel coordinates can be calculated. Meanwhile, the synthetic images of the virtual robot environment are combined with those of the physical robot scene to determine whether the human hand is in contact with the robot. Besides, a human motion intention recognition model based on deep learning is designed to recognize human motion intention with the input of sEMG signals. Moreover, a robot motion mode selection module is built to control the robot for single-axis motion, linear motion, or repositioning motion by combining the hand-robot contact state and human motion intention. The experimental results indicate that the proposed system can perform online robot teaching for the three motion modes.

本文提出了一种基于手-机器人接触状态检测和人体动作意图识别的机器人教学系统。该系统可以检测手-机器人关节的接触状态，并从人体表面肌电图（sEMG）信号中提取运动意图信息来控制机器人的运动。首先，提出了一种基于虚拟机器人环境与物理环境融合的手机器人接触状态检测方法。利用目标检测算法，可以识别人手在物理环境彩色图像中的位置，并计算出其像素坐标。同时，将虚拟机器人环境的合成图像与物理机器人场景的合成图像相结合，以确定人手是否与机器人接触。除了，设计了一种基于深度学习的人体运动意图识别模型，通过输入sEMG信号识别人体运动意图。此外，还构建了机器人运动模式选择模块，通过结合手-机器人接触状态和人体运动意图来控制机器人进行单轴运动、直线运动或重新定位运动。实验结果表明，所提出的系统可以对三种运动模式进行在线机器人示教。