Abstract
We present a novel approach for informative path planning in multi-robot systems for mapping under time and communication constraints. The approach is based on modeling the mapping task as a regression problem using Gaussian processes (GP) and adaptively directing the robots towards the regions that are most informative for GP learning. The methodology is based on a multi-stage process where either a robot or a group of robots search for the best convex region where to sample new data, identify the most informative sampling locations in the region, and compute an optimized path through them. The process is iterated over time adapting to newly gathered evidence and is performed collaboratively. Techniques from Monte Carlo, sequential Bayesian inference, and orienteering optimization models are combined in an integrated strategy. Fully distributed and leader-follower architectures are designed to implement the multi-stage strategy and have been evaluated in simulation, showing up to 69% of improvement over a baseline strategy.
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This work was made possible by NPRP Grant 10-0213-170458 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein are solely the responsibility of the authors.
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Di Caro, G.A., Ziaullah Yousaf, A.W. (2022). Map Learning via Adaptive Region-Based Sampling in Multi-robot Systems. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_26
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DOI: https://doi.org/10.1007/978-3-030-92790-5_26
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