无人机搜救路径规划新算法，能减少20%覆盖时间。
Adaptive grid-based decomposition for UAV-based coverage path planning in maritime search and rescue
- 根据搜索区域形状自适应划分网格，减少单元数量
- 结合混合整数规划，使无人机全覆盖耗时最少，最多降20%
- 适合需要快速响应的海上搜救任务
无人机在搜救行动中日益广泛应用，可大幅提升覆盖大范围区域的效率。缩短覆盖时间直接提高发现目标的概率，从而提升搜救成功率。为此，需为无人机规划最优飞行路径以实现完全覆盖。现有方法通常将搜索区域划分为网格，要求无人机遍历所有单元。本文提出自适应网格分解（AGD）算法，能高效将多边形搜索区域划分为更少单元的网格；同时采用与之兼容的混合整数规划（MIP）模型，确定确保全单元覆盖且总耗时最少的飞行路径。实验表明，在多种场景下该算法可将覆盖时间最多降低20%。
原文摘要 · Abstract (English)
Unmanned aerial vehicles (UAVs) are increasingly utilized in search and rescue (SAR) operations to enhance efficiency by enabling rescue teams to cover large search areas in a shorter time. Reducing coverage time directly increases the likelihood of finding the target quickly, thereby improving the chances of a successful SAR operation. In this context, UAVs require path planning to determine the optimal flight path that fully covers the search area in the least amount of time. A common approach involves decomposing the search area into a grid, where the UAV must visit all cells to achieve complete coverage. In this paper, we propose an Adaptive Grid-based Decomposition (AGD) algorithm that efficiently partitions polygonal search areas into grids with fewer cells. Additionally, we utilize a Mixed-Integer Programming (MIP) model, compatible with the AGD algorithm, to determine a flight path that ensures complete cell coverage while minimizing overall coverage time. Experimental results highlight the efficiency of the AGD algorithm in reducing coverage time (by up to 20%) across various scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。