Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

Conference Paper (2022)
Author(s)

Yifan Cai (University of Waterloo)

Abhinav Dahiya (University of Waterloo)

Nils Wilde (TU Delft - Learning & Autonomous Control)

Stephen L. Smith (University of Waterloo)

Research Group
Learning & Autonomous Control
Copyright
© 2022 Yifan Cai, Abhinav Dahiya, N. Wilde, Stephen L. Smith
DOI related publication
https://doi.org/10.1109/CDC51059.2022.9993014
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Yifan Cai, Abhinav Dahiya, N. Wilde, Stephen L. Smith
Research Group
Learning & Autonomous Control
Pages (from-to)
3997-4003
ISBN (print)
978-1-6654-6761-2
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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 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.

Files

Scheduling_Operator_Assistance... (pdf)
(pdf | 1.89 Mb)
- Embargo expired in 10-07-2023
License info not available