Spatio-Temporal Avoidance of Predicted Occupancy in Human-Robot Collaboration

TL;DR

This paper introduces STAP, a spatio-temporal path planning method for human-robot collaboration that predicts human movements to enable robots to plan proactive, efficient, and safe trajectories without stopping.

Contribution

The paper presents a novel spatio-temporal planning approach, STAP, that anticipates human movements and robot delays, improving path efficiency and safety in human-robot collaboration.

Findings

STAP generates shorter robot paths with increased safety distance.

STAP accurately estimates robot trajectory durations in HRC.

Experimental results demonstrate improved path efficiency and safety.

Abstract

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.