Adaptive path planning using Gaussian process regression: a reinforcement learning approach

Lanqing Zhao

doi:10.1117/12.3012570

21 December 2023 Adaptive path planning using Gaussian process regression: a reinforcement learning approach

Lanqing Zhao

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703J (2023) https://doi.org/10.1117/12.3012570
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In recent years, the development of underwater and sub-ice wireless sensor networks has been promoted by advances in wireless sensor network technology. A key issue to improve the efficiency and performance of underwater robot networks is adaptive trajectory planning. This paper studies adaptive trajectory planning of multiple AUVs in the underwater environment for estimating the water parametric field of interest, using a reinforcement learning system where a fixed access point (AP) on the ice serves as a communication gateway. The water parameter field is modeled as a Gaussian process (GP) with unknown hyperparameters, and a field hyperparameter estimation method based on the maximum likelihood method is employed for more accurate estimation. Compared to the traditional Monte Carlo(MC) algorithm, the proposed algorithm demonstrates superior performance in terms of resource consumption and exploration rate.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lanqing Zhao "Adaptive path planning using Gaussian process regression: a reinforcement learning approach", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703J (21 December 2023); https://doi.org/10.1117/12.3012570

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