1 research outputs found
Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection
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.Comment: 8 pages, accepted for publication at ICAR conferenc