This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages the spatial structure of the coverage problem and presents a decentralized strategy where samples are aggregated locally by establishing communications through the boundaries of a Voronoi partition. We present an algorithm whereby each robot runs a local Gaussian process calculated from its own measurements and those provided by its Voronoi neighbors, which are incorporated into the individual robot's Gaussian process only if they provide sufficiently novel information. The performance of the algorithm is evaluated in simulation and compared with centralized approaches.
|Title of host publication
|IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2022
|2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan
Duration: Oct 23 2022 → Oct 27 2022
|IEEE International Conference on Intelligent Robots and Systems
|2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
|10/23/22 → 10/27/22
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications