关键词: 人机协作装配, 场景认知, 目标检测, 人物交互图

Abstract: The rapid understanding of Human-Robot Collaborative (HRC) assembly scene is of great practical significance to improve the cognitive ability of collaborative robot and realize HRC assembly.Aiming at the problems of large difference of object scale and the lack of unified scene description framework in the cognitive process of unstructured HRC assembly scene,a Lightweight Multi-Scale object detection Network (LMS-Net) was constructed,and the anchor clustering mechanism was introduced in the network training process to improve the accuracy of multi-scale object detection.Then,the LMS-Net detection results were converted into human-object interaction graph,and a meta-description model of HRC assembly scene was established.A cognitive method of HRC assembly scene based on multi-scale object detection was proposed.Experimental results on the self-built dataset HRC-Action showed that the proposed multi-scale object detection network had high accurate (average 89%) and faster speed (average 58.7FPS on deep learning workstation,average 25FPS on Jetson Nano B01),and the proposed HRC assembly scene cognitive method had good feasibility and practicability.

Key words: human-robot collaborative assembly, scene cognitive, object detection, human-object interaction graph