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Article

Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning †

1
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2
School of Information Engineering, Qingdao Binhai University, Qingdao 266555, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Dong, P.; Liu, J.; Tao, H.; Ruby, R.; Jian, M.; Luo, H. An optimized scheduling scheme for uav-usv cooperative search via multi-agent reinforcement learning approach. In Proceedings of the 20th International Conference on Mobility, Sensing and Networking (MSN 2024), Harbin, China, 20–22 December 2024; pp. 172–179.
Sensors 2025, 25(13), 4025; https://doi.org/10.3390/s25134025 (registering DOI)
Submission received: 20 May 2025 / Revised: 19 June 2025 / Accepted: 26 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Underwater Vision Sensing System: 2nd Edition)

Abstract

Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities.
Keywords: maritime search and rescue; maritime unmanned systems; vision sensing; cooperative search; reinforcement learning maritime search and rescue; maritime unmanned systems; vision sensing; cooperative search; reinforcement learning

Share and Cite

MDPI and ACS Style

Dong, P.; Liu, J.; Tao, H.; Zhao, Y.; Feng, Z.; Luo, H. Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning. Sensors 2025, 25, 4025. https://doi.org/10.3390/s25134025

AMA Style

Dong P, Liu J, Tao H, Zhao Y, Feng Z, Luo H. Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning. Sensors. 2025; 25(13):4025. https://doi.org/10.3390/s25134025

Chicago/Turabian Style

Dong, Pengyan, Jiahong Liu, Hang Tao, Yang Zhao, Zhijie Feng, and Hanjiang Luo. 2025. "Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning" Sensors 25, no. 13: 4025. https://doi.org/10.3390/s25134025

APA Style

Dong, P., Liu, J., Tao, H., Zhao, Y., Feng, Z., & Luo, H. (2025). Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning. Sensors, 25(13), 4025. https://doi.org/10.3390/s25134025

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