超越覆盖路径规划：无人机群能否完美检查散落区域？
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?
AI总结：
本文提出多无人机不连通区域检查方法，用于高效检查散落区域，提升操作效率并保持高质量数据收集。
摘要：
无人机（UAVs）通过提供更安全、更高效和更灵活的替代方案，彻底改变了检查任务。然而，电池限制往往限制了其效果，需要开发优化的飞行路径和数据收集技术。尽管现有的覆盖路径规划（CPP）方法可以确保全面的数据收集，但在检查多个不连通的兴趣区域（ROIs）时可能效率低下。本文提出了快速检查散落区域（FISR）问题，并提出了一种新的解决方案，即多无人机不连通区域检查（mUDAI）方法。该方法实施了双重优化过程，用于计算最佳图像捕捉位置和最有效的无人机轨迹，平衡数据分辨率和操作时间，最小化冗余数据收集和资源消耗。mUDAI方法旨在实现快速、高效的散落ROIs检查，使其适用于安全基础设施评估、农业检查和紧急现场评估等应用。通过模拟评估和现实世界部署的结合，验证并量化了该方法在提高操作效率的同时保持高质量数据收集的能力，证明了其在现实操作中的有效性。mUDAI方法的开源Python实现可在GitHub上找到（https://github.com/soc12/mUDAI），现实世界实验中收集和处理的数据均托管在Zenodo（https://zenodo.org/records/13866483）。最后，这个在线平台（https://sites.google.com/view/mudai-platform/）允许感兴趣的读者与mUDAI方法互动并生成自己的多无人机FISR任务。
英文摘要：
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.
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。
