The Analysis of Concurrent-Task Operation Ability: Peripheral-Visual-Guided Grasp Performance Under the Gaze

Liu, Yuan; Zhang, Wenxuan; Zeng, Bo; Zhang, Kuo; Qian, Cheng; Ming, Dong

doi:10.1007/978-3-030-89098-8_47

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An optical motion capture system, VICON, was used for recording the 3D coordinates data and kinematic data. Five retro-reflex landmarks were placed on the WRT (the styloid process of the radius), the WRP (the styloid process of the ulna), the RFIN (third right finger), the TB (thumbnail), and the IDX (index fingernail) of the hand, and one retroreflective marker was placed on the RC (object center) [28]. This labeling method was used in our previous work and has proven to be able to obtain upper-limb movement data.…”

Section: Data Acquisitionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%