It is an effective way to execute a complicated mission by cooperating unmanned vehicles. This paper focuses on a search- and-track (SAT) mission for an underwater target, and the mission is implemented by combining an unmanned aerial vehicle (UAV), an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). In the cooperative path planning model, the mission is divided into the search phase and the track phase, and the goals of the two phases are to maximize the search space and minimize the terminal error respectively. The constraints contain the maneuverability of vehicles and communication ranges between vehicles. Strategies based on random simulation experiments and asynchronous planning are developed to design the cooperative path planning algorithm in the two phases, and the paths are generated by an improved particle swarm optimization (IPSO) algorithm in a centralized or a distributed mode. Simulation results demonstrate that the proposed method can deal with different situations. The UAV&USV&AUV system is superior to the USV&AUV system in the SAT mission.

中文翻译：

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针对水下目标的搜索和跟踪任务中异构无人驾驶车辆的协同路径规划

与无人驾驶车辆协同执行复杂任务是一种有效的方式。本文重点研究水下目标的搜索和跟踪（SAT）任务，该任务是通过结合无人机（UAV）、无人水面航行器（USV）和自主水下航行器（AUV）来实现的。在协同路径规划模型中，任务分为搜索阶段和跟踪阶段，两个阶段的目标分别是最大化搜索空间和最小化终端误差。约束包括车辆的机动性和车辆之间的通信范围。开发了基于随机模拟实验和异步规划的策略来设计两个阶段的协同路径规划算法，路径由改进的粒子群优化（IPSO）算法以集中或分布式模式生成。仿真结果表明，所提出的方法可以处理不同的情况。UAV&USV&AUV系统在SAT任务中优于USV&AUV系统。