Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance

TL;DR

This paper introduces an approximate algorithm for optimizing human supervision in multi-robot systems, improving task efficiency and reducing human workload in uncertain environments.

Contribution

It formulates the supervision task as a dynamic graph traversal problem and develops an approximation algorithm with bounded error for effective robot team management.

Findings

Outperforms baseline methods in task completion time

Reduces human working time in simulations

Can be deployed in real-time for moderate-sized robot fleets

Abstract

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.