AViTRoN: Advanced Vision Track Routing and Navigation for Autonomous Charging of Electric Vehicles

Abstract

The ascent of electric vehicle (EV) technology as a leading solution for green transportation is accompanied by advancements in charging infrastructure and automation. A notable hindrance is the low level of automation in charging procedures. In response to this, Automatic Charging Robots (ACR) have emerged, equipped transitioning from the manual operation to an automated plugging and unplugging operation. However, for this process to be executed flawlessly, these robots necessitate a charging port detection system with a precise navigation system to ensure accurate insertion of the charging gun into the designated charging port. This paper presents a sophisticated system, AViTRoN (Advanced Vision Track Routing and Navigation), which is developed for Automated Charging Robots in the context of Electric Vehicle (EV) charging. AViTRoN integrates advanced technologies to enable efficient charging port detection, navigation, and seamless user interaction. Utilizing the YOLOv8 deep learning model, AViTRoN ensures real-time charging port type detection using the data from a 3D depth sensor and an IR sensor within the Robot Operating System (ROS) framework. The 3D depth sensor provides detailed spatial information, while the IR sensor detects subtle environmental changes, enhancing the system’s accuracy during operation. AViTRoN also incorporates a charging completion notification mechanism, sending instant alerts to users via GSM/GPRS communication upon the conclusion of the charging cycle, thereby enhancing user convenience and experience.

Keywords

• YOLOv8
• ROS
• electric vehicles
• GSM/GPRS communication
• autonomous charging
• socket detection