Multi-robot Learning and Coverage of Unknown Spatial Fields

Maria Santos, Udari Madhushani, Alessia Benevento, Naomi Ehrich Leonard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the problem of optimally covering a domain when the scalar function that describes the relative importance of the points in the domain is initially unknown. We propose an adaptive strategy for a team of cooperative robots that combines estimation and learning methods with optimal spatial coverage. The proposed algorithm leads the team of robots to an optimal solution of the coverage problem by efficiently trading off movement choices for learning the field with movement choices for covering the estimated field. The algorithm exploits the flexibility of Gaussian processes for learning the field and optimization rules based on Voronoi partitions of the environment for covering the field. We propose an exploration strategy that uses the decentralized nature of the coverage problem by allowing each robot to sample the space in its area of dominance. We provide a theoretical guarantee of the algorithm. The performance of the proposed algorithm is evaluated in simulation as well as on a team of mobile robots.

Original languageEnglish (US)
Title of host publication2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-145
Number of pages9
ISBN (Electronic)9781665429269
DOIs
StatePublished - 2021
Event2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021 - Cambridge, United Kingdom
Duration: Nov 4 2021Nov 5 2021

Publication series

Name2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021

Conference

Conference2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021
Country/TerritoryUnited Kingdom
CityCambridge
Period11/4/2111/5/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Control and Optimization
  • Artificial Intelligence

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