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<p>This study investigates the exploration of accident-prone maritime regions, including incidents of illegal driving, unauthorized boarding, illegal fishing, and smuggling, through a collaborative system integrating an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV). The primary objective is to optimize travel time and energy efficiency of this system while ensuring comprehensive coverage of all identified accident-prone points. Key limitations include the USV’s inability to access certain target locations independently and the UAV’s energy constraints, which preclude solo exploration of the entire mission area. To overcome these challenges, we introduce the collaborative accident searching routing optimization (CASRO) algorithm, designed to coordinate a cooperative USV–UAV framework. The CASRO algorithm leverages the USV as a mobile charging station to alleviate UAV energy limitations, while the UAV serves as the primary platform for hazardous area exploration. We propose a novel optimization approach that integrates an enhanced ant colony optimization (ACO) algorithm with the combinatorial Lazy Theta* path planning technique to effectively minimize travel time. Extensive simulations validate that the CASRO algorithm yields a highly coordinated routing strategy, significantly enhancing the hybrid system’s exploration efficiency and energy utilization. These findings underscore the potential of cooperative robotic systems in maritime surveillance, with implications for future research into dynamic environmental adaptations and real-time operational constraints.</p>

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CASRO: a path optimization strategy for USV–UAV collaborative exploration of maritime target

  • 期刊论文

  • 发表日期：

    2026年3月22日

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  • Yue Han,
  • Wei Yue,
  • Bin Yang

摘要

This study investigates the exploration of accident-prone maritime regions, including incidents of illegal driving, unauthorized boarding, illegal fishing, and smuggling, through a collaborative system integrating an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV). The primary objective is to optimize travel time and energy efficiency of this system while ensuring comprehensive coverage of all identified accident-prone points. Key limitations include the USV’s inability to access certain target locations independently and the UAV’s energy constraints, which preclude solo exploration of the entire mission area. To overcome these challenges, we introduce the collaborative accident searching routing optimization (CASRO) algorithm, designed to coordinate a cooperative USV–UAV framework. The CASRO algorithm leverages the USV as a mobile charging station to alleviate UAV energy limitations, while the UAV serves as the primary platform for hazardous area exploration. We propose a novel optimization approach that integrates an enhanced ant colony optimization (ACO) algorithm with the combinatorial Lazy Theta* path planning technique to effectively minimize travel time. Extensive simulations validate that the CASRO algorithm yields a highly coordinated routing strategy, significantly enhancing the hybrid system’s exploration efficiency and energy utilization. These findings underscore the potential of cooperative robotic systems in maritime surveillance, with implications for future research into dynamic environmental adaptations and real-time operational constraints.