Informative Path Planning for AUV-based Underwater Terrain Exploration with a POMDP

Shi Zhang, Rongxin Cui, Weisheng Yan, Yinglin Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Autonomous underwater vehicles (AUVs) in underwater terrain exploration applications represent a topic area, and the interesting problem is planning paths to maximize the vehicle information gathered and combining this information to build a complete map. The Gaussian process (GP) is utilized as a basic environment model and updated using the Bayesian data fusion technique with sensor information. A path planning algorithm, which formulates the terrain exploration problem as the finite-horizon partially observable Markov decision process (POMDP), is proposed to overcome the limitation of the planner converge to locally suboptimal solutions. In addition, a Monte Carlo Tree Search based on the motion primitives tree (MPT-MCTS) solver is developed to solve this POMDP. The effectiveness of the proposed method is explored in the simulation experiment, and its potential is demonstrated by comparing it with other optimization algorithms.

源语言英语
主期刊名Proceeding - 2021 China Automation Congress, CAC 2021
出版商Institute of Electrical and Electronics Engineers Inc.
4756-4761
页数6
ISBN(电子版)9781665426473
DOI
出版状态已出版 - 2021
活动2021 China Automation Congress, CAC 2021 - Beijing, 中国
期限: 22 10月 202124 10月 2021

出版系列

姓名Proceeding - 2021 China Automation Congress, CAC 2021

会议

会议2021 China Automation Congress, CAC 2021
国家/地区中国
Beijing
时期22/10/2124/10/21

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