Human-machine Collaboration Method Based on Key Nodes of Human Posture

Abstract

Aiming at the personal safety problems in the complex working conditions and high-risk working environments such as high temperature and high radiation in the industrial field, a human-machine collaboration method based on the key nodes of human posture is proposed. First, compare the human pose detection performance of AlphaPose, OpenPose, and HRNet models in complex environments such as different lighting, different human poses, and occlusions, and select AlphaPose, which is suitable for real-time human-machine collaboration, as the basic model of human-machine collaboration; then, formulate standard human actions, establish the mapping relationship between human actions and robot actions; then, use the spatial position data of key human nodes to establish a human action recognition algorithm to calculate the similarity between the action to be detected and the standard human action to identify the human action; finally, real-time control according to the mapping relationship the movement of the robot realizes the collaboration between human and robot. Experimental results show that the average action recognition accuracy of the method proposed in this paper can reach 91.2%, the average action recognition speed can reach 67ms, the average action recognition accuracy under 0-60 Lux illumination intensity can reach 90.4%, and the average recognition accuracy under partial occlusion conditions up to 87.6%.