[论文解读] Placement and Routing Optimization for Automated Inspection with UAVs: A Study in Offshore Wind Farm

Hwei-Ming Chung, Sabita Maharjan+3 more|arXiv (Cornell University)|Jun 15, 2020

UAV Applications and Optimization参考文献 23被引用 7

一句话总结

本文提出了一种用于海上风电场无人机（UAV）自主巡检的两阶段优化框架，解决了在风力约束下最小化UAV机队规模和最小化巡检时间的问题。通过启发式算法，该方法实现了UAV的最优部署与路径规划，在Walney风电场数据上得到验证，最大飞行时间为20分钟，能耗为212.82W，显著降低了运营风险与人工强度。

ABSTRACT

Wind power is a clean and widely deployed alternative to reducing our dependence on fossil fuel power generation. Under this trend, more turbines will be installed in wind farms. However, the inspection of the turbines in an offshore wind farm is a challenging task because of the harsh environment (e.g., rough sea, strong wind, and so on) that leads to high risk for workers who need to work at considerable height. Also, inspecting increasing number of turbines requires long man hours. In this regard, unmanned aerial vehicles (UAVs) can play an important role for automated inspection of the turbines for the operator, thus reducing the inspection time, man hours, and correspondingly the risk for the workers. In this case, the optimal number of UAVs enough to inspect all turbines in the wind farm is a crucial parameter. In addition, finding the optimal path for the UAVs' routes for inspection is also important and is equally challenging. In this paper, we formulate a placement optimization problem to minimize the number of UAVs in the wind farm and a routing optimization problem to minimize the inspection time. Wind has an impact on the flying range and the flying speed of UAVs, which is taken into account for both problems. The formulated problems are NP-hard. We therefore design heuristic algorithms to find solutions to both problems, and then analyze the complexity of the proposed algorithms. The data of the Walney wind farm are then utilized to evaluate the performance of the proposed algorithms. Simulation results clearly show that the proposed methods can obtain the optimal routing path for UAVs during the inspection.

研究动机与目标

• 为海上风电场中所有风力涡轮机的完整巡检最小化所需UAV数量，以降低运营成本与风险。
• 通过考虑风力对速度与航程的影响，优化UAV飞行路径，以最小化总巡检时间。
• 为UAV辅助风电场巡检中的NP难性部署与路径规划问题，开发计算高效的启发式解决方案。
• 基于Walney海上风电场的真实数据验证该框架，整合了现实的风力与UAV性能参数。

提出的方法

• 建立部署优化问题以最小化UAV数量，受限于受风力影响的飞行范围与最大飞行时间。
• 开发路径优化问题以最小化总巡检时间，考虑风力引起的地速变化。
• 使用包含型阻、诱导与寄生功率分量的物理基础功率方程建模UAV能耗。
• 从电池容量与功率消耗推导最大飞行时间，基于仿真参数设定实际的20分钟限制。
• 设计UAV部署与路径规划的启发式算法，并在特定收敛条件下证明了部署算法的最优性。
• 利用基于矢量的合速度模型，将风速与风向数据整合到速度与地速计算中。

实验结果

研究问题

• RQ1 在现实风力条件与UAV性能限制下，海上风电场中完整巡检所有风力涡轮机所需的最少UAV数量是多少？
• RQ2 如何优化UAV飞行路径以最小化总巡检时间，同时考虑风力对速度与航程的影响？
• RQ3 风力对UAV地速与能耗有何影响？如何准确建模以支持路径规划？
• RQ4 启发式算法能否为海上风电场中NP难性的UAV部署与路径规划问题提供近似最优解？
• RQ5 所提出的框架在真实环境下的表现如何？是否通过Walney海上风电场的数据得到验证？

主要发现

• 所提出的UAV部署启发式算法实现了最优解收敛，通过反证法证明：若移除任意一架UAV将违反约束条件。
• 基于物理基础功率模型计算出的UAV能耗为212.82瓦，推导出最大飞行时间为20.02分钟，仿真中设定为20分钟。
• 路径优化成功计算出能最小化巡检时间的最优路径，充分考虑了风力引起的地速变化。
• 在Walney风电场的仿真结果表明，所提方法能有效减少巡检时间与UAV机队规模。
• 该框架通过实现自动化、风力感知的UAV作业，显著降低了人工巡检工作量与人员风险，适用于恶劣的海上环境。
• 将风力数据整合到速度与航程建模中，提升了路径规划的准确性与任务可行性。