A Two-Level Q-Learning Approach with Guided Error and Adaptive Step Size for Autonomous EV-Charging System

This paper presents a novel two-level Q-learning algorithm designed to improve path planning and collision avoidance in autonomous electric vehicle (EV)-charging system. The proposed method leverages point cloud data from a single camera to construct a 3D grid-based representation of the environment. Fuzzy logic is used to dynamically determine the number of grid cells, ensuring sufficient resolution to handle minor misalignments during the charging process. The typical Q-learning approach is enhanced with a guided error mechanism and adaptive step size, which adjusts exploration strategies and optimizes step size based on real-time feedback. This adaptation allows the robot to avoid high-risk areas near obstacles, significantly improving obstacle avoidance and boosting the success rate of the charging process. Through extensive experimentation on multiple EV models, the proposed method demonstrated reduced computational time and enhanced robustness, achieving a success rate of 95\% in real-world scenarios. The use of a single camera and cost-effective components positions this approach as a practical solution for real-world autonomous EV charging applications.