  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.
