基于LiDAR的四旋翼无人机在杂乱环境中的自主巡检系统
LiDAR-based Quadrotor Autonomous Inspection System in Cluttered Environments
AI总结：
提出基于LiDAR的四旋翼无人机双阶段巡检系统，结合人在环与自主巡检，利用TSP优化任务序列，在复杂GNSS拒止环境中显著提升巡检效率与安全性。
摘要：
近年来，自主无人机（UAV）技术取得了快速进展，显著提高了操作效率并减轻了工业巡检、农业监测以及搜救任务等领域中人工操作的风险。尽管取得了这些进展，现有的无人机巡检系统仍面临两个关键挑战：在复杂、非结构化和GNSS拒止环境中可靠性有限，以及对熟练操作员的明显依赖。为了克服这些限制，本研究提出了一种基于LiDAR的无人机巡检系统，采用双阶段工作流程：人在环巡检和自主巡检。在人在环阶段，未经训练的飞行员通过自主避障获得支持，使他们能够生成3D地图、指定巡检点并安排任务。然后使用旅行商问题（TSP）对巡检点进行优化，以创建高效的任务序列。在自主阶段，四旋翼自主执行计划的任务，确保安全高效的数据采集。在包括斜坡、滑坡、农田、工厂和森林等多种环境中进行的全面实地实验证实了系统的可靠性和灵活性。结果显示，巡检效率得到显著提升，与人在环操作相比，自主操作将轨迹长度减少了高达40%，飞行时间减少了57%。这些发现强调了所提出系统在安全关键和资源受限场景中增强基于无人机巡检的潜力。
英文摘要：
In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encounter two critical challenges: limited reliability in complex, unstructured, and GNSS-denied environments, and a pronounced dependency on skilled operators. To overcome these limitations, this study presents a LiDAR-based UAV inspection system employing a dual-phase workflow: human-in-the-loop inspection and autonomous inspection. During the human-in-the-loop phase, untrained pilots are supported by autonomous obstacle avoidance, enabling them to generate 3D maps, specify inspection points, and schedule tasks. Inspection points are then optimized using the Traveling Salesman Problem (TSP) to create efficient task sequences. In the autonomous phase, the quadrotor autonomously executes the planned tasks, ensuring safe and efficient data acquisition. Comprehensive field experiments conducted in various environments, including slopes, landslides, agricultural fields, factories, and forests, confirm the system's reliability and flexibility. Results reveal significant enhancements in inspection efficiency, with autonomous operations reducing trajectory length by up to 40\% and flight time by 57\% compared to human-in-the-loop operations. These findings underscore the potential of the proposed system to enhance UAV-based inspections in safety-critical and resource-constrained scenarios.
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