Abstract
This study focuses on efficiently deploying a multi-robot system (MRS) to achieve optimal area coverage in the presence of an unknown event that requires monitoring. We introduce a control algorithm based on coverage to enable a team of robots to learn and estimate the spatial process of this region while ensuring good coverage. The robots’ observations are influenced by environmental noise. We use Gaussian processes (GP) to model the spatial process, and the multi-robot team optimally covers the estimated process. We evaluated the algorithm through simulations and real platform experiments.
This work was supported by the AI-DROW Project through the Italian Ministry for University and Research under the PRIN 2022 program, funded by the European Union – Next Generation EU.
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References
Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom. 20(2), 243–255 (2004)
Jakkala, K.: Deep gaussian processes: a survey. arXiv preprint arXiv:2106.12135 (2021)
Luo, W., Nam, C., Kantor, G., Sycara, K.: Distributed environmental modeling and adaptive sampling for multi-robot sensor coverage. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1488–1496 (2019)
Nakamura, K., Santos, M., Leonard, N.E.: Decentralized learning with limited communications for multi-robot coverage of unknown spatial fields. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9980–9986. IEEE (2022)
Paley, D.A., Wolek, A.: Mobile sensor networks and control: adaptive sampling of spatiotemporal processes. Ann. Rev. Control, Robot. Auton. Syst. 3, 91–114 (2020)
Park, C., Huang, J.Z.: Efficient computation of gaussian process regression for large spatial data sets by patching local gaussian processes. J. Mach. Learn. Res.-JMLR (2016)
Pratissoli, F., Capelli, B., Sabattini, L.: On coverage control for limited range multi-robot systems. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9957–9963 (2022)
Santos, M., Madhushani, U., Benevento, A., Leonard, N.E.: Multi-robot learning and coverage of unknown spatial fields. In: 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), pp. 137–145. IEEE (2021)
Schulz, E., Speekenbrink, M., Krause, A.: A tutorial on gaussian process regression: modelling, exploring, and exploiting functions. J. Math. Psychol. 85, 1–16 (2018)
Schwager, M., Vitus, M.P., Rus, D., Tomlin, C.J.: Robust adaptive coverage for robotic sensor networks. In: Robotics Research, pp. 437–454. Springer (2017)
Viseras, A., Shutin, D., Merino, L.: Robotic active information gathering for spatial field reconstruction with rapidly-exploring random trees and online learning of gaussian processes. Sensors 19(5), 1016 (2019)
Wei, L., McDonald, A., Srivastava, V.: Multi-robot gaussian process estimation and coverage: Deterministic sequencing algorithm and regret analysis. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 9080–9085. IEEE (2021)
Zuo, L., Shi, Y., Yan, W.: Dynamic coverage control in a time-varying environment using Bayesian prediction. IEEE Trans. Cybern. 49(1), 354–362 (2017)
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Mantovani, M., Pratissoli, F., Sabattini, L. (2024). Adaptive Distributed Coverage Control for Learning Spatial Phenomena in Unknown Environments. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_28
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DOI: https://doi.org/10.1007/978-3-031-76424-0_28
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