Reinforcement Learning and Particle Swarm Optimization Supporting Real-Time Rescue Assignments for Multiple Autonomous Underwater Vehicles
Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, the authors propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, the authors present a reward-based real-time rescue assignment algorithm. Secondly, the authors propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, the authors develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
-
Supplemental Notes:
- Copyright © 2022, IEEE.
-
Authors:
- Wu, Jiehong
-
0000-0002-0851-3009
- Song, Chengxin
-
0000-0003-0941-2151
- Ma, Jian
- Wu, Jinsong
- Han, Guangjie
- Publication Date: 2022-7
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 6807-6820
-
Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 7
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Machine learning; Optimization; Search and rescue operations; Unmanned underwater vehicles
- Subject Areas: Marine Transportation; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01853921
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 4 2022 5:17PM