用无人机自动巡检风力机叶片，精准定位并智能调光保细节
Automated UAV-based Wind Turbine Blade Inspection: Blade Stop Angle Estimation and Blade Detail Prioritized Exposure Adjustment
- 基于费马点算法估算叶片停转角度，提升精度与成功率
- 实测120+次飞行验证，10种机型5个风电场均有效提升检测自主性
- 实时调整曝光参数，关键部位细节不丢失，不可逆修复
无人机在风力机叶片自动化巡检中至关重要。然而现有平台难以满足自动化任务需求，叶片停转角度估计算法易受环境干扰，且图像采集过程中缺乏实时细节优先的曝光调节机制，导致信息丢失无法事后补救。为此，本文提出一套巡检平台及两项新方法：首先构建适配自动化巡检的无人机平台；其次引入基于费马点的叶片停转角度估计方法，显著提升精度与成功率；最后提出叶片细节优先的曝光自适应策略，确保成像亮度合理、关键特征完整保留。在5个运行中的风电场对10种风力机型号开展超过120次飞行测试，结果表明所提方法有效提升了巡检自主性与可靠性。
原文摘要 · Abstract (English)
Unmanned aerial vehicles (UAVs) are critical in the automated inspection of wind turbine blades. Nevertheless, several issues persist in this domain. Firstly, existing inspection platforms encounter challenges in meeting the demands of automated inspection tasks and scenarios. Moreover, current blade stop angle estimation methods are vulnerable to environmental factors, restricting their robustness. Additionally, there is an absence of real-time blade detail prioritized exposure adjustment during capture, where lost details cannot be restored through post-optimization. To address these challenges, we introduce a platform and two approaches. Initially, a UAV inspection platform is presented to meet the automated inspection requirements. Subsequently, a Fermat point based blade stop angle estimation approach is introduced, achieving higher precision and success rates. Finally, we propose a blade detail prioritized exposure adjustment approach to ensure appropriate brightness and preserve details during image capture. Extensive tests, comprising over 120 flights across 10 wind turbine models in 5 operational wind farms, validate the effectiveness of the proposed approaches in enhancing inspection autonomy.
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