Fuel Tank Position Localization Based on Machine Vision - Song, Changjiang (International Journal of Computer Science and Information Technology, 2024)
Refueling robots, as an important part of intelligent service systems, greatly enhance the safety and efficiency of the refueling process. This study focuses on the machine vision module in refueling robots, specifically exploring the application of high-resolution cameras and LiDAR in data acquisition. We used Convolutional Neural Networks (CNNs) such as ResNet and MobileNet for feature extraction, which ensured high-precision recognition and classification in a variety of environments. Meanwhile, the target detection module uses YOLOv4 and MobileNet3 with fast and accurate target localization capabilities to effectively identify and calibrate the location of fueling ports. In addition, we introduced Extended Kalman Filter (EKF) and Bayesian Filter algorithms for data fusion and state estimation, which improves the robustness and reliability of the system. By combining these advanced vision techniques and algorithms, the refueling robot realizes efficient and accurate automatic refueling operation. This research provides theoretical and technical support for the further development of intelligent refueling robots so that they can still operate stably in complex environments.