面向多目标的多无人机系统主动搜索策略
An Active Search Strategy with Multiple Unmanned Aerial Systems for Multiple Targets
AI总结：
本研究针对广阔自然环境中多无人机高效目标搜索的难题，提出融合全局局部信息、结合轨迹生成与Voronoi划分的去中心化主动搜索策略，经实验验证效率优异，可用于野生鲎搜索，助力生态调查与保护。
摘要：
广阔自然环境中高效目标搜索的挑战，推动了对先进多无人机主动搜索策略的需求。本文提出一种新颖方法，巧妙融合全局与局部信息，以避免短视、冗余往返运动等问题。此外，该方法采用轨迹生成方法，确保连续空间内的搜索模式。为进一步优化多智能体协作，研究采用Voronoi划分（泰森多边形划分）技术，减少重复飞行模式，并实现多智能体的去中心化控制。通过一系列实验，评估与对比结果验证了该方法在多种环境下的效率。这一创新方法的主要应用场景为野生栖息地中的鲎搜索，展现出革新生态调查与保护工作的潜力。
英文摘要：
The challenge of efficient target searching in vast natural environments has driven the need for advanced multi-UAV active search strategies. This paper introduces a novel method in which global and local information is adeptly merged to avoid issues such as myopia and redundant back-and-forth movements. In addition, a trajectory generation method is used to ensure the search pattern within continuous space. To further optimize multi-agent cooperation, the Voronoi partition technique is employed, ensuring a reduction in repetitive flight patterns and making the control of multiple agents in a decentralized way. Through a series of experiments, the evaluation and comparison results demonstrate the efficiency of our approach in various environments. The primary application of this innovative approach is demonstrated in the search for horseshoe crabs within their wild habitats, showcasing its potential to revolutionize ecological survey and conservation efforts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
