TY - GEN
T1 - Multi-robot Learning and Coverage of Unknown Spatial Fields
AU - Santos, Maria
AU - Madhushani, Udari
AU - Benevento, Alessia
AU - Leonard, Naomi Ehrich
N1 - Funding Information:
This work was supported by the Office of Naval Research grant 25105-G0001-10012165 and Army Research Office grant W911NF-18-1-0325.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/MRS50823.2021.9620688
DO - 10.1109/MRS50823.2021.9620688
M3 - Conference contribution
AN - SCOPUS:85123994644
T3 - 2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021
SP - 137
EP - 145
BT - 2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2021
Y2 - 4 November 2021 through 5 November 2021
ER -