An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN.

For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot's motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives.