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.

翻译：本文针对人机协作（HRC）中整合人类活动预测以提供机器人主动-反应响应能力的挑战。现有研究将当前或预测的人体姿态视为静态障碍物，导致规划过于短视或保守，可能造成机器人路径延迟。反之，时变的人体姿态预测可使机器人路径动态同步避让预期人体姿态。本文提出一种名为STAP的主动路径规划方法，该方法利用时空人体占用图寻找能预判人类运动的机器人轨迹，实现机器人无需停止即可通过。此外，STAP还能预判ISO/TS 15066速度与分离监控（SSM）规定的机器人速度限制导致的延迟。STAP还提出基于RRT*的采样规划算法解决时空运动规划问题，并寻求期望持续时间最短的路径。实验结果显示，STAP生成的路径在全任务周期内持续时间更短、机器人与人的平均分离距离更大。同时，STAP能更精确估计HRC中的机器人轨迹持续时间，这对实现主动-反应式机器人序列规划具有实用价值。