MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles
- Authors
- Gong, Yue-Jiao; Huang, Ting; Ma, Yi-Ning; Jeon, Sang-Woon; Zhang, Jun
- Issue Date
- Apr-2023
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- Trajectory; Planning; Optimization; Trajectory planning; Indexes; Task analysis; Genetic algorithms; Ant colony system; autonomous underwater vehicles; multiple-trajectory planning; niching
- Citation
- IEEE Transactions on Intelligent Transportation Systems, v.24, no.4, pp 3714 - 3727
- Pages
- 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Intelligent Transportation Systems
- Volume
- 24
- Number
- 4
- Start Page
- 3714
- End Page
- 3727
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111629
- DOI
- 10.1109/TITS.2023.3234937
- ISSN
- 1524-9050
1558-0016
- Abstract
- Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.
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