Scholarlink

<p>The speed and separation monitoring(SSM) mode is an active safety response mechanism with adjustable safety distances to meet safety requirements. However, in intensive human-robot collaboration(HRC) scenarios, the SSM mode may trigger frequent monitoring downtime, which in turn reduces the overall efficiency of the robot operation. Therefore, this paper proposes a dynamic SSM method based on binocular vision. Firstly, the method introduces the human body pose estimation algorithm to obtain the position and speed information of the operator in the scene. Afterwards, when the distance between human and robot during collaboration violates the minimum safe distance(MSD), the Tau-j* algorithm is used to adjust the robot’s motion speed according to the solution result of the critical safe speed, so that the MSD is reduced to meet the safety requirements of the HRC. Finally, experiments results show that the proposed dynamic SSM approach enables the robot to reduce monitoring downtime and improve the smoothness of collaboration while ensuring safety and efficiency by adjusting speed in the face of potentially dangerous situations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic Speed and Separation Monitoring for Human-Robot Collaboration Based on Binocular Vision

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

查看全文
  • Jinzhu Peng,
  • Zhan Yin,
  • Chenyang Zhang,
  • Meng Li,
  • Shuai Ding,
  • Chengzhong Wu

摘要

The speed and separation monitoring(SSM) mode is an active safety response mechanism with adjustable safety distances to meet safety requirements. However, in intensive human-robot collaboration(HRC) scenarios, the SSM mode may trigger frequent monitoring downtime, which in turn reduces the overall efficiency of the robot operation. Therefore, this paper proposes a dynamic SSM method based on binocular vision. Firstly, the method introduces the human body pose estimation algorithm to obtain the position and speed information of the operator in the scene. Afterwards, when the distance between human and robot during collaboration violates the minimum safe distance(MSD), the Tau-j* algorithm is used to adjust the robot’s motion speed according to the solution result of the critical safe speed, so that the MSD is reduced to meet the safety requirements of the HRC. Finally, experiments results show that the proposed dynamic SSM approach enables the robot to reduce monitoring downtime and improve the smoothness of collaboration while ensuring safety and efficiency by adjusting speed in the face of potentially dangerous situations.