A reinforcement learning-based evolutionary algorithm for the unmanned aerial vehicles maritime search and rescue path planning problem considering multiple rescue centers

• Published in: Memetic computing Vol. 16; no. 3; pp. 373 - 386
• Main Authors: Zhan, Haowen, Zhang, Yue, Huang, Jingbo, Song, Yanjie, Xing, Lining, Wu, Jie, Gao, Zengyun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024; Springer Nature B.V
• Subjects: Adaptive algorithms; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Complex Systems; Control; Cost effectiveness; Emergency plans; Engineering; Evacuations & rescues; Evolutionary algorithms; Genetic algorithms; Heuristic methods; Machine learning; Mathematical and Computational Engineering; Mechatronics; Path planning; Reconnaissance aircraft; Regular Research Paper; Rescue operations; Rescue vehicles; Robotics; Unmanned aerial vehicles; Maritime search and rescue; Genetic; Path planning; UAV; Evolutionary algorithm; Reinforcement learning
• ISSN: 1865-9284; 1865-9292
• DOI: 10.1007/s12293-024-00420-8

In the realm of maritime emergencies, unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations. They help in efficiently rescuing distressed crews, strengthening maritime surveillance, and maintaining national security due to their cost-effectiveness, versatility, and effectiveness. However, the vast expanse of sea territories and the rapid changes in maritime conditions make a single SAR center insufficient for handling complex emergencies. Thus, it is vital to develop strategies for quickly deploying UAV resources from multiple SAR centers for area reconnaissance and supporting maritime rescue operations. This study introduces a graph-structured planning model for the maritime SAR path planning problem, considering multiple rescue centers (MSARPPP-MRC). It incorporates workload distribution among SAR centers and UAV operational constraints. We propose a reinforcement learning-based genetic algorithm (GA-RL) to tackle the MSARPPP-MRC problem. GA-RL uses heuristic rules to initialize the population and employs the Q-learning method to manage the progeny during each generation, including their retention, storage, or disposal. When the elite repository’s capacity is reached, a decision is made on the utilization of these members to refresh the population. Additionally, adaptive crossover and perturbation strategies are applied to develop a more effective SAR scheme. Extensive testing proves that GA-RL surpasses other algorithms in optimization efficacy and efficiency, highlighting the benefits of reinforcement learning in population management.