弹道
启发式
计算机科学
水下
蚁群优化算法
集合(抽象数据类型)
度量(数据仓库)
运动规划
警报
任务(项目管理)
人工智能
数学优化
机器学习
数据挖掘
工程类
机器人
数学
系统工程
物理
地质学
航空航天工程
海洋学
程序设计语言
天文
作者
Yue-Jiao Gong,Ting Huang,Yining Ma,Sang-Woon Jeon,Mengjie Zhang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:24 (4): 3714-3727
被引量:5
标识
DOI:10.1109/tits.2023.3234937
摘要
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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