TY - GEN
T1 - DR-MPC: Disturbance-Resilient Model Predictive Visual Servoing Control for Quadrotor UAV Pipeline Inspect
AU - Li, Wen
AU - Su, Jinya
AU - Liu, Cunjia
AU - Chen, Wen Hua
AU - Li, Shihua
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/11
Y1 - 2025/11
N2 - Unmanned Aerial Vehicles (UAVs) are gaining attention for inspections due to their improved safety, efficiency, and accuracy, alongside reduced costs and environmental risks. Visual servoing is crucial for autonomous UAV flight in GPS-degraded environments, guiding the UAV by minimizing errors between observed and desired visual features. This study focuses on Image-Based Visual Servoing (IBVS) control for quadrotor UAVs under complex dynamics and environmental disturbances. A nonlinear model predictive control (MPC) framework is first integrated with visual servoing to handle dynamics nonlinearity, control optimality, and constraints. To address uncertainties and disturbances, a Generalized Extended State Observer (GESO) is incorporated into the MPC, forming the Disturbance-Resilient (DR-) MPC. The GESO estimates the lumped disturbance to improve model predictions within the MPC horizon. The proposed algorithm is validated in a realistic Gazebo environment for UAV pipeline inspection in 3D scenarios, showing better control accuracy and reduced inspection time compared to three baseline methods: IBVS, IBVS-MPC(K) with kinematics, and IBVS-MPC(D) with dynamics.
AB - Unmanned Aerial Vehicles (UAVs) are gaining attention for inspections due to their improved safety, efficiency, and accuracy, alongside reduced costs and environmental risks. Visual servoing is crucial for autonomous UAV flight in GPS-degraded environments, guiding the UAV by minimizing errors between observed and desired visual features. This study focuses on Image-Based Visual Servoing (IBVS) control for quadrotor UAVs under complex dynamics and environmental disturbances. A nonlinear model predictive control (MPC) framework is first integrated with visual servoing to handle dynamics nonlinearity, control optimality, and constraints. To address uncertainties and disturbances, a Generalized Extended State Observer (GESO) is incorporated into the MPC, forming the Disturbance-Resilient (DR-) MPC. The GESO estimates the lumped disturbance to improve model predictions within the MPC horizon. The proposed algorithm is validated in a realistic Gazebo environment for UAV pipeline inspection in 3D scenarios, showing better control accuracy and reduced inspection time compared to three baseline methods: IBVS, IBVS-MPC(K) with kinematics, and IBVS-MPC(D) with dynamics.
KW - Disturbance rejection
KW - Model predictive control
KW - UAV inspection
KW - Visual servoing
UR - https://www.scopus.com/pages/publications/105029936831
U2 - 10.1109/IROS60139.2025.11247550
DO - 10.1109/IROS60139.2025.11247550
M3 - Conference article published in proceeding or book
AN - SCOPUS:105029936831
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 20738
EP - 20745
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
Y2 - 19 October 2025 through 25 October 2025
ER -