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.

翻译：无人机通过提供比传统方法更安全、高效和灵活的替代方案，彻底改变了巡检任务。然而，电池续航限制常常制约其效能，因此需要开发优化的飞行路径和数据采集技术。虽然现有方法如覆盖路径规划能确保全面的数据采集，但其效率可能不高，尤其是在巡检多个互不连通的感兴趣区域时。本文提出了分散区域快速巡检问题，并引入了一种新颖的解决方案——多无人机非连通区域巡检方法。该方法采用双重优化流程，分别计算最佳图像采集位置和最高效的无人机轨迹，在数据分辨率和作业时间之间取得平衡，同时最小化冗余数据采集和资源消耗。mUDAI方法旨在实现对分散ROI的快速高效巡检，使其成为安全基础设施评估、农业巡检和应急现场评估等应用的理想选择。通过模拟评估与实地部署相结合的方式，我们验证并量化了该方法在保持高质量数据采集的同时提升作业效率的能力，证明了其在真实场景中的有效性。mUDAI方法的开源Python实现可在GitHub上获取，实地实验采集和处理的数据均托管于Zenodo平台。最后，该在线平台允许感兴趣的读者与mUDAI方法进行交互，并生成自己的多无人机FISR任务。