用有限续航无人机高效巡检管道，优化路径缩短检查时间。
Optimizing Line Segment Inspection with Limited-Range Drones
- 设计多无人机协同巡检路径算法，兼顾电池限制与覆盖效率。
- 在两条线段场景下证明问题强NP难，提出近似最优算法。
- 适用于太阳能电站等工业巡检，适合关注效率的工程应用者。
无人机在民用任务中被广泛研究，因其可穿越复杂地形并搭载摄像头与传感器执行监视任务。然而，其有限的电池寿命给实际运营带来挑战。本文解决如下优化问题：需用无人机巡检一组线段（如太阳能电站中的管道），目标是通过人工智能检测破损管道，并高效规划飞行路径。一方面，受限于电池容量，无人机必须周期性返回固定基站；另一方面，需为每架无人机分配一组巡检路径，使所有线段尽快被覆盖，以最小化完成时间（即最大作业时长）。我们证明，即使线段位于一条直线上且仅涉及两架无人机，该问题仍为强NP-hard。随后提出近似算法，计算实验表明该算法在多种场景下均能实现接近最优性能。
原文摘要 · Abstract (English)
Optimization problems with drones are widely studied in a variety of civilian tasks, mainly due to their ability to traverse rough terrains and to carry cameras and other sensors for surveillance tasks. The limited battery life of these aerial robots poses challenges in operational research. In this paper, we address the following optimization problem. We are given a set of line segments (e.g. tubes in a solar plant) to inspect by drones. The objective is to detect broken pipes using artificial intelligence and path planning must be carried out efficiently. On the one hand, the limited capacity of the batteries necessitates periodic visits (tours) to a fixed base station. However, it is desirable to allocate a set of tours for each drone to ensure that the segments are covered as quickly as possible, aiming to minimize the makespan, which is the maximum time spent by any drone. We are able to prove that this optimization problem is strongly NP-hard even when the segments are positioned on a line and the scenario involves only two drones. Then, approximation algorithms are proposed. Our computational experiments demonstrate that the proposed algorithm achieves near-optimal performance across diverse operational scenarios.
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