TY - GEN
T1 - Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection
AU - Amer, Abdel Hakim Khaled Saad Amin
AU - Mehndiratta, Mohit
AU - Sejersen, Jonas le Fevre
AU - Pham, Xuan Huy
AU - Kayacan, Erdal
PY - 2024/2
Y1 - 2024/2
N2 - Automated visual inspection of on- and off-shorewind 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
1
1https://www.github.com/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced.
AB - Automated visual inspection of on- and off-shorewind 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
1
1https://www.github.com/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced.
KW - model predictive control
KW - unmanned aerial vehicle
KW - robotics
KW - autonomous systems
UR - http://www.scopus.com/inward/record.url?scp=85185829240&partnerID=8YFLogxK
U2 - 10.1109/ICAR58858.2023.10406329
DO - 10.1109/ICAR58858.2023.10406329
M3 - Article in proceedings
SN - 979-8-3503-4230-7
T3 - International Conference on Advanced Robotics (ICAR)
SP - 431
EP - 438
BT - 2023 21st International Conference on Advanced Robotics (ICAR)
PB - IEEE
ER -