  The number of multi-robot systems deployed in field applications has
increased dramatically over the years. Despite the recent advancement of
navigation algorithms, autonomous robots often encounter challenging situations
where the control policy fails and the human assistance is required to resume
robot tasks. Human-robot collaboration can help achieve high-levels of
autonomy, but monitoring and managing multiple robots at once by a single human
supervisor remains a challenging problem. Our goal is to help a supervisor
decide which robots to assist in which order such that the team performance can
be maximized. We formulate the one-to-many supervision problem in uncertain
environments as a dynamic graph traversal problem. An approximation algorithm
based on the profitable tour problem on a static graph is developed to solve
the original problem, and the approximation error is bounded and analyzed. Our
case study on a simulated autonomous farm demonstrates superior team
performance than baseline methods in task completion time and human working
time, and that our method can be deployed in real-time for robot fleets with
moderate size.
