Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes are open-sourced.

翻译：使用空中机器人对陆上和海上风力发电机进行自动化视觉检测具有多重优势，包括通过规避作业人员高空悬吊需求保障安全作业环境、缩短检测时间、实现预防性维护以及抵达难以接触区域。本文提出一种结合全局风力发电机路径规划器的新型非线性模型预测控制框架，以实现距离最优的风力发电机检测覆盖。与传统MPC公式不同，视觉跟踪NMPC（VT-NMPC）被设计为跟踪检测表面而非位置和航向轨迹，从而避免为无人机提供精确预设轨迹的需求。所提出的VT-NMPC方法另一项能力在于：通过将检测需求转化为待最小化的视觉跟踪代价函数，可在遵循无人机物理约束的同时自然地成功完成检测任务。多组仿真实验与真实世界测试表明，所提出的自动化检测框架在实现目标风力发电机叶片全覆盖方面优于传统MPC设计，且对变化风况具有鲁棒性。实现代码已开源。