Efficient Reachable Workspace Division under Concurrent Task for Human-Robot Collaboration Systems

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Abstract
Division of the reachable workspace of upper limbs under different visual and physical conditions, finding the efficient reachable area under concurrent task conditions, and using it as a basis to divide the incorporation boundaries that require robot assistance are the focus of this paper. These could be used to rationalize the allocation of human and robot workspaces to maximize the efficiency of multitask completion, which has significant applications in the enhancement of human–robot collaboration (HRC) capabilities. However, research on this has rarely been conducted due to the complexity and diversity of arm movements. In this paper, we considered the physical and visual restrictions of the human operator, extracted the movement data of 10 participants while completing the reaching task, and divided the workspace into five areas (their angles are 0°~44.761°, 44.761°~67.578°, 67.578°~81.108°, 81.108°~153.173°, and 153.173°~180°). Measuring the concurrent task completion times when the target object is in each area, respectively, we demonstrated that areas I~II are efficient, reachable workspaces for the human. In the non-efficient reachable workspaces, the average completion times for HRC were 86.7% for human operators (in area III) and 70.1% (in area IV), with the average number of warnings reduced from 2.5 to 0.4. The average completion time for HRC in area V was 59.3% for the human operator, and the average number of warnings was reduced from 3.5 to 0.5. Adding robotic assistance in this area could improve the efficiency of the HRC systems. This study provided a quantitative evaluation of human concurrent task completion capabilities and the incorporation boundaries of robots, which is a useful reference for achieving efficient HRC.
Funding Information
  • National Key Research and Development Program of China (2022YFF1202500, 2022YFF1201205, 62273251, 51905375, 21JCYBJC00520)
  • Tianjin University (2022YFF1202500, 2022YFF1201205, 62273251, 51905375, 21JCYBJC00520)
  • Natural Science Foundation of Tianjin (2022YFF1202500, 2022YFF1201205, 62273251, 51905375, 21JCYBJC00520)

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