Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

TL;DR

This paper addresses the challenge of scheduling human operator assistance in multi-robot systems, proposing an optimal formulation and a scalable heuristic to improve efficiency and reduce system makespan.

Contribution

It introduces a mixed integer linear programming model and an anytime algorithm for operator scheduling in multi-robot teams, handling NP-hard complexity.

Findings

The proposed algorithm outperforms greedy methods in simulations.

The MILP formulation provides optimal solutions for small to medium problems.

The anytime algorithm offers high-quality solutions for larger instances.

Abstract

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at a faster rate. We show that the problem of creating a teleoperation schedule that minimizes makespan of the system is NP-Hard. We formulate our problem as a Mixed Integer Linear Program, which can be used to optimally solve small to moderate sized problem instances. We also develop an anytime algorithm that makes use of the problem structure to provide a fast and high-quality solution of the operator scheduling problem, even for larger problem instances. Our key insight is to identify blocking tasks in greedily-created schedules and iteratively remove those blocks to improve the quality of the solution. Through numerical simulations, we demonstrate the benefits of the proposed algorithm as an efficient and scalable approach that outperforms other greedy methods.