@inproceedings{9e89ef949d7a40a0a4960bbde39a8ce3,
title = "Multi-robot Learning and Coverage of Unknown Spatial Fields",
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.",
author = "Maria Santos and Udari Madhushani and Alessia Benevento and Leonard, {Naomi Ehrich}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021 ; Conference date: 04-11-2021 Through 05-11-2021",
year = "2021",
doi = "10.1109/MRS50823.2021.9620688",
language = "English (US)",
series = "2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "137--145",
booktitle = "2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021",
address = "United States",
}