This study proposes an automatic recognition and location system of electric vehicle charging port with application to automatic charging. The system obtains the charging port posture through image processing, and performs the insertion motion in combination with the robot arm to complete the charging gun insertion of the automatic charging link. The framework of the system is mainly divided into three parts, recognition, location and insertion. In the charging port recognition, the convolutional neural network-based method is used, and the recognition success rate is up to 98.9% under the light intensity of 4000 lux; in the location of the charging port, the method of solving the pose based on the circle feature is adopted. The average value of the position error is within 1.4 mm, and the average value of the attitude angle error is within 1.6°, which meets the accuracy requirement of the insertion experiment; in the charging gun insertion, the motion of the robot is planned by interpolation algorithm. The lower limit of the successful insertion is about 135 lux and the upper limit is about 9350 lux.

中文翻译：

---

复杂环境下电动汽车充电口自动识别定位系统

提出了一种电动汽车充电口自动识别定位系统，并将其应用于自动充电中。该系统通过图像处理获得充电口姿势，并结合机械臂执行插入动作，以完成自动充电链接的充电枪插入。该系统的框架主要分为三个部分，识别，定位和插入。在充电口识别中，采用了基于卷积神经网络的方法，在光通量为4000 lux的情况下，识别成功率高达98.9％。在充电口位置，采用基于圆形特征的姿态求解方法。位置误差的平均值在1.4 mm以内，姿态角误差的平均值在1.6°以内，符合插入实验的准确性要求；在充电枪插入时，通过插值算法来计划机器人的运动。成功插入的下限约为135 lux，上限约为9350 lux。