[1]ZHAO Yuxin,DU Denghui,CHENG Xiaohui,et al.Path planning for mobile ocean observation network based on reinforcement learning[J].CAAI Transactions on Intelligent Systems,2022,17(1):192-200.[doi:10.11992/tis.202106004]
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Path planning for mobile ocean observation network based on reinforcement learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 192-200 Column: 人工智能院长论坛 Public date: 2022-01-05
Title:
Path planning for mobile ocean observation network based on reinforcement learning
Author(s):
ZHAO Yuxin1;  DU Denghui1;  CHENG Xiaohui1;  ZHOU Di2;  DENG Xiong1;  LIU Yanlong1
1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001 , China;
2. China Ship Development and Design Center, Wuhan 430064, China
Keywords:
deep reinforcement learning; marine environmental observation; path planning; USV; Q learning; multi-agent; DDPG; RankGauss
CLC:
TP242.6
DOI:
10.11992/tis.202106004
Abstract:
Reasonable and effective planning method of mobile vehicles for marine environmental observation is beneficial to the design of marine environmental observation network and the collection efficiency of marine environmental information. In view of the vast marine environment and limited observation resources, the deep reinforcement learning algorithm is used to plan the marine environmental observation network. In order to solve the problems in the design of discrete and continuous motion during the path planning, two algorithms, DQN and DDPG, are designed to solve the problem of single platform and multi-platform experiments. The experimental results show that the reward curve of DQN algorithm using discrete motion is better than DDPG algorithm using continuous motion. This paper further analyzes the sampling path results of the mobile vehicles for marine environmental observation, and the results show that the sampling result of DQN algorithm with discrete action is better. The experimental results show that the DQN algorithm using discrete motion can maximize the effective data information collection, which demonstrates effectiveness and feasibility of the method.
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