Real-Time Dynamic Planning and Tracking Control of Auto-Docking for Efficient Wireless Charging

• Published in: IEEE transactions on intelligent vehicles 2023-03, Vol.8 (3), p.2123-2134
• Main Authors: Gong, Liang, Wu, Yingxin, Gao, Bishu, Sun, Yefeng, Le, Xinyi, Liu, Chengliang
• Format: Article
• Language: English
• Subjects: Acceleration; Algorithms; Auto-docking; Automated guided vehicles; Collision avoidance; Control algorithms; Control methods; Deceleration; Docking; Dynamic models; Dynamics; Efficiency; efficiency awareness; Inductive charging; Kinematics; Nonlinear control; Obstacle avoidance; Path planning; Planning; Predictive control; Real time; real-time planning; Real-time systems; Simultaneous localization and mapping; Tracking control; Trajectory planning; trajectory tracking; Vehicle dynamics; Wheels; wireless charging; Wireless power transmission
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2022.3189511

In the open scene of auto-docking for wireless charging, vehicle needs to approach the station with the capability of real-time path planning to achieve the time efficiency, and to stop with a certain posture to guarantee high charging efficiency. The prominent challenge arises from the coupled problem of dynamic obstacle avoidance and precise targeting posture control. To simultaneously address these two issues, this paper takes into considerations of vehicle jerk and targeting posture constrains, and proposes a generic real-time planning and tracking control method for the wireless charged nonholonomic autonomous vehicle. First, a kinematic and dynamic model for nonholonomic differential-drive vehicle is built as the foundation of the planning and tracking control. Then, a real-time layered planner consisting of a path planning and a trajectory planning stage is designed to generate reference trajectory, which encompasses jerk constraints and vehicle dynamics to ensure the global time and energy efficiency in an intermittent acceleration and deceleration scenario. Finally, a fast nonlinear model predictive control (FNMPC) algorithm is established to realize high precision trajectory tracking during the dynamic obstacle avoidance. Numerical experimentation shows that the proposed method achieves a straightforward, less than 3 cm docking error posture under a given autonomous guided vehicle (AGV) configuration, and outperforms general-purpose simultaneous localization and mapping (SLAM) planning and control algorithms for dynamic obstacle avoidance. The results show that the proposed method is feasible for the general efficiency-aware applications of vehicle auto docking in the wireless charging station.