评估风对接触式电力线巡检半自主无人机降落的影响
Assessing Wind Impact on Semi-Autonomous Drone Landings for In-Contact Power Line Inspection
AI总结：
本研究提出一种基于 NADILE 无人机的半自主降落控制方案，通过蒙特卡洛方法分析不同风况与策略下的降落成功率，发现两阶段策略最优，并经实验验证了其有效性。
摘要：
近年来，由于高效、高性价比以及能够到达难以接近区域的能力，巡检无人机在高压电缆巡检中的应用日益普及。然而，将无人机安全降落在电力线上，特别是在有风条件下，仍然是一项重大挑战。本研究提出了一种半自主控制方案，利用 NADILE 无人机（一种基于原始 LineDrone 电力线巡检核心特征的实验型无人机）降落在电力线上，并评估了在各种风况下的运行包线。采用蒙特卡洛方法分析给定初始无人机状态下降落的成功概率。系统性能针对两种降落策略、多种控制器参数和四个等级的风强度进行了评估。结果表明，两阶段降落策略提供了更高的降落成功概率，并深入揭示了最佳控制器参数以及系统保持鲁棒性的最大风力等级。最后，展示了系统在电力线上自主降落的实验演示。
英文摘要：
In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The performance of the system is evaluated for two landing strategies, variously controllers parameters and four level of wind intensities. The results show that a two-stage landing strategies offers higher probabilities of landing success and give insight regarding the best controller parameters and the maximum wind level for which the system is robust. Lastly, an experimental demonstration of the system landing autonomously on a power line is presented.
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。
