Abstract:
Existing coal gangue sorting robots face challenges in mispicking, empty grabs, and collisions during the sorting process. These issues primarily stem from the phenomena of slippage and deviation of coal gangue during belt transportation, making it difficult to accurately acquire real-time positional information of the gangue using belt speed-based tracking methods. This leads to significant errors in the robotic arm’s grabbing actions, adversely affecting the sorting efficiency. To address this issue, a vision-based real-time guidance method for coal gangue tracking is proposed. This method involves capturing the real-time positional information of the coal gangue through cameras, guiding the robotic arm to adjust its actions for tracking and grabbing the gangue. Initially, the vision recognition module acquires the target's initial position and tracking template, with the control system allocating strategies to assign the gangue to the respective robotic arm for grabbing. Once the target gangue enters the robotic arm's grabbing work area, a monocular target tracking model, built on Siamese networks, captures the real-time positional information of the gangue, enabling the arm to adjust its movements dynamically to complete the grab. Subsequently, visual tracking experiments on coal gangue at different belt speeds were conducted, and a coal gangue sorting robot simulation system was developed to perform trajectory planning simulations under various conditions of slippage and deviation. The simulation experiment results demonstrate that the constructed coal gangue tracking model achieves a tracking accuracy of 96.9% and a tracking speed of 39FPS, meeting the needs for real-time guidance. When facing varying degrees of slippage and deviation, the grabbing error of the robotic arm, guided by real-time vision, is reduced to within 1 mm. Compared to the belt speed-based tracking method, this approach effectively eliminates cumulative errors caused by belt slippage and deviation during transportation, enhances the system’s real-time responsiveness, and further improves the efficiency of coal gangue sorting.