Spatio-Temporal Avoidance of Predicted Occupancy in Human-Robot Collaboration
Spatio-Temporal Avoidance of Predicted Occupancy in Human-Robot Collaboration
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DOI：
10.1109/ro-man57019.2023.10309469
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Jared Flowers;M. Faroni;G. Wiens;N. Pedrocchi
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文献类型：
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作者：
Jared Flowers;M. Faroni;G. Wiens;N. Pedrocchi
本文讨论了人-机器人协作（HRC）的挑战，整合人类活动的预测，为机器人提供一个积极的反应能力。将当前或预测的人类姿势视为静态障碍物的先前工作在规划中过于短视或过于保守，可能导致延迟的机器人路径。或者，人类姿势的时变预测将使机器人路径能够避免预期的人类姿势，在时间和空间上动态同步。在这里，主动路径规划方法，表示STAP，提出了使用时空人类占用地图，以找到机器人轨迹，预期人类的运动，允许机器人通过而不停止。此外，STAP预计ISO/TS 15066速度和分离监控（SSM）要求的机器人速度限制会导致延迟。STAP还提出了一种基于RRT* 的采样规划算法来解决时空运动规划问题，并找到最小预期持续时间的路径。实验结果表明，STAP生成的路径的持续时间较短，更大的平均机器人-人分离距离在整个任务。此外，STAP更准确地估计机器人的轨迹持续时间在HRC，这是有用的，在到达前摄-反应式机器人排序。
This paper addresses human-robot collaboration (HRC) challenges of integrating predictions of human activity to provide a proactive-n-reactive response capability for the robot. Prior works that consider current or predicted human poses as static obstacles are too nearsighted or too conservative in planning, potentially causing delayed robot paths. Alternatively, time-varying prediction of human poses would enable robot paths that avoid anticipated human poses, synchronized dynamically in time and space. Herein, a proactive path planning method, denoted STAP, is presented that uses spatiotemporal human occupancy maps to find robot trajectories that anticipate human movements, allowing robot passage without stopping. In addition, STAP anticipates delays from robot speed restrictions required by ISO/TS 15066 speed and separation monitoring (SSM). STAP also proposes a sampling-based planning algorithm based on RRT* to solve the spatio-temporal motion planning problem and find paths of minimum expected duration. Experimental results show STAP generates paths of shorter duration and greater average robot-human separation distance throughout tasks. Additionally, STAP more accurately estimates robot trajectory durations in HRC, which are useful in arriving at proactive-n-reactive robot sequencing.