Abstract
To gain a better understanding of environmental processes we are interested in the problem of deploying multi-robot systems for efficient collection of environmental data. For long-term autonomy, enabling persistent monitoring, it is important to consider the spatio-temporal variations of environmental phenomena. We develop a multi-robot persistent path planning method that reduces uncertainty in the environmental model. Our framework contains two components: the first component computes potential observation points that minimize model prediction uncertainty, and the second component uses this for online planning of multi-robot paths, while also taking into account the efficiency of information collection. We validated our method via simulations, and the results show that it produces multi-robot routing paths that are conflict-free, informative, and adaptive to the environmental dynamics.
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The authors would like to thank Stephanie Kemna and Hordur Heidarsson for their valuable inputs on this paper.
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Ma, KC., Ma, Z., Liu, L., Sukhatme, G.S. (2018). Multi-robot Informative and Adaptive Planning for Persistent Environmental Monitoring. In: Groß, R., et al. Distributed Autonomous Robotic Systems. Springer Proceedings in Advanced Robotics, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-73008-0_20
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DOI: https://doi.org/10.1007/978-3-319-73008-0_20
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