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

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.

Automated summary

This paper proposes a real-time planning and tracking control method for wireless charged nonholonomic autonomous vehicles, which addresses the challenges of dynamic obstacle avoidance and precise targeting posture control. By considering vehicle jerk and targeting posture constraints, a kinematic and dynamic model is established as the foundation of planning and tracking control. A real-time layered planner and a fast nonlinear model predictive control algorithm are designed to generate reference trajectory and realize high precision trajectory tracking during dynamic obstacle avoidance.