LEARNING-BASED POSE ESTIMATION IN AUTONOMOUS ELECTRIC VEHICLE CHARGING SYSTEM USING ROBOT ARM MANIPULATION
DOI:
https://doi.org/10.11113/jtse.v13.261Keywords:
Autonomous Electric Vehicle, Machine Learning-Based Detection, Motion Planning, Peg-in-holeAbstract
Autonomous Electric Vehicles (AEVs) require fully automated charging systems to achieve complete autonomy. However, existing charging methods still depend on manual plug insertion by users. This study presents a learning-based control algorithm for autonomous robotic charging using a four-degrees-of-freedom robot arm. The robotic arm was designed in SOLIDWORKS, simulated in Gazebo, and controlled through the Robot Operating System (ROS). An object detection model was trained using a dataset of Type 2 EV charging sockets to identify the charging port, while a depth camera estimated its three-dimensional coordinates. The detected position was then processed by a motion planner to generate a collision-free trajectory for accurate plug alignment. Simulation results demonstrated reliable charging socket detection and robotic manipulation under various operating conditions. The proposed approach achieved a precision of 0.99 and a recall of 0.98, indicating robust detection performance. The highest detection confidence score of 0.87 was obtained at a charging socket distance of 1.1 m, while the lowest score of 0.73 was recorded at 1.0 m. These findings demonstrate the potential of the proposed framework for enabling reliable and autonomous EV charging operations.
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