TY - GEN
T1 - Informative Path Planning for AUV-based Underwater Terrain Exploration with a POMDP
AU - Zhang, Shi
AU - Cui, Rongxin
AU - Yan, Weisheng
AU - Li, Yinglin
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Autonomous Underwater Vehicle
KW - Gaussian process
KW - Informative path planning
KW - Monte Carlo Tree Search
KW - Partially observable Markov decision process
UR - http://www.scopus.com/inward/record.url?scp=85128076094&partnerID=8YFLogxK
U2 - 10.1109/CAC53003.2021.9728147
DO - 10.1109/CAC53003.2021.9728147
M3 - 会议稿件
AN - SCOPUS:85128076094
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 4756
EP - 4761
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -