多无人机协同巡检零散区域，提升效率并减少冗余。
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?
- 分阶段优化拍摄位置与飞行路径，兼顾图像质量与耗时。
- 实测与仿真验证：巡检效率显著提升，数据质量保持高水准。
- 适合安防、农业、应急等需快速覆盖分散目标的场景。
无人飞行器（UAV）通过提供更安全、高效和灵活的替代方案，彻底改变了巡检任务。然而，电池续航限制常制约其实际应用，亟需优化飞行路径与数据采集策略。现有覆盖路径规划（CPP）方法虽能确保全面数据采集，但在巡检多个不连通兴趣区域（ROIs）时效率较低。本文提出“快速零散区域巡检”（FISR）问题，并引入多无人机非重叠区域巡检（mUDAI）方法。该方法采用双重优化流程，分别计算最优拍摄位置与最高效无人机航迹，在保证数据分辨率的同时，最小化冗余采集与资源消耗。mUDAI旨在实现对分散ROIs的快速高效巡检，适用于安全设施评估、农业监测及灾后现场勘查等场景。通过模拟评估与真实部署相结合，验证了该方法在提升操作效率的同时维持高质量数据采集的能力，证明其在真实场景中的有效性。mUDAI的开源Python实现已发布于GitHub（https://github.com/soc12/mUDAI），真实实验数据集托管于Zenodo（https://zenodo.org/records/13866483）。此外，官网平台（https://sites.google.com/view/mudai-platform/）支持用户交互式生成多无人机FISR任务。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) have revolutionized inspection tasks by offering a safer, more efficient, and flexible alternative to traditional methods. However, battery limitations often constrain their effectiveness, necessitating the development of optimized flight paths and data collection techniques. While existing approaches like coverage path planning (CPP) ensure comprehensive data collection, they can be inefficient, especially when inspecting multiple non connected Regions of Interest (ROIs). This paper introduces the Fast Inspection of Scattered Regions (FISR) problem and proposes a novel solution, the multi UAV Disjoint Areas Inspection (mUDAI) method. The introduced approach implements a two fold optimization procedure, for calculating the best image capturing positions and the most efficient UAV trajectories, balancing data resolution and operational time, minimizing redundant data collection and resource consumption. The mUDAI method is designed to enable rapid, efficient inspections of scattered ROIs, making it ideal for applications such as security infrastructure assessments, agricultural inspections, and emergency site evaluations. A combination of simulated evaluations and real world deployments is used to validate and quantify the method's ability to improve operational efficiency while preserving high quality data capture, demonstrating its effectiveness in real world operations. An open source Python implementation of the mUDAI method can be found on GitHub (https://github.com/soc12/mUDAI) and the collected and processed data from the real world experiments are all hosted on Zenodo (https://zenodo.org/records/13866483). Finally, this online platform (https://sites.google.com/view/mudai-platform/) allows interested readers to interact with the mUDAI method and generate their own multi UAV FISR missions.
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