Online Multi-Robot Task Assignment with Stochastic Blockages

Conference paper (2022)

Authors

N. Wilde Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 N. Wilde, J. Alonso-Mora
DOI: https://doi.org/10.1109/CDC51059.2022.9993099
More Info
expand_more

Published Date

2022

Language

English

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.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

In this paper we study the multi-robot task assignment problem with tasks that appear online and need to be serviced within a fixed time window in an uncertain environment. For example, when deployed in dynamic, human-centered environments, the team of robots may not have perfect information about the environment. Parts of the environment may temporarily become blocked and blockages may only be observed on location. While numerous variants of the Canadian Traveler Problem describe the path planning aspect of this problem, few work has been done on multi-robot task allocation (MRTA) under this type of uncertainty. In this paper, we introduce and theoretically analyze the problem of MRTA with recoverable online blockages. Based on a stochastic blockage model, we compute offline tours using the expected travel costs for the online routing problem. The cost of the offline tours is used in a greedy task assignment algorithm. In simulation experiments we highlight the performance benefits of the proposed method under various settings.

Files

Online_Multi_Robot_Task_Assign... (pdf)
(pdf | 1.82 Mb)
- Embargo expired in 01-07-2023
Unknown license