Abstract:In the production of multiple types and small batches of small precision devices (diameter 16–40 mm), the main assembly tasks are mainly completed by fixed station robots, with this assembly mode having a large cost. For such small devices, the automatic guided vehicle (AGV) on the market have the problems of poor flexibility and low positioning accuracy. To address these problems, this study designs and develops an omnidirectional AGV autonomous navigation equipped with industrial cameras and dual manipulators to complete the dynamic combination of multiple production lines and realize the orderly auxiliary assembly of various types of devices. To improve the positioning accuracy, the Bayesian rule fuses 2D LiDAR and RGB-D to establish a fused raster map to improve the obstacle detection rate. EKF is used to fuse the data of the wheeled odometer and the IMU to improve the accuracy of the odometer and reduce the motion error. For the sake of improving work efficiency and making innovations in real-time performance, the distance between the precision device to be grasped and the camera optical center is obtained through RGB-D, and the information such as vehicle speed and the pose relationship between radar and camera are fused to calculate the optimal movement time of the vehicle-mounted dual manipulators at the distance S from the precision device to be grasped. Finally, to accurately identify multi-type and small-batch small precision devices, the improved Yolo-Fastest algorithm is used, which improves the recognition accuracy and reduces the computing cost of the AGV. Test results show that the system for small precision devices (e.g., RF connectors) has identification accuracy greater than 95%. In 70×50×100 cm³ space, it can achieve omnidirectional movement, and the maximum motion error is 10 cm. Compared with the existing production mode, the AGV has improved flexibility, reduced production cost, and nearly doubled work efficiency, worthy of practical promotion.