A Push‐Based Gangue‐Sorting Robotic System: Design, Development, Integration, and Field Evaluation

ABSTRACT

During coal mining, coal and gangue with varying shapes, sizes, and masses are transporte together on conveyor belts, posing significant challenges for gangue sorting. Traditional manual sorting is increasingly limited by safety risks, labor costs, and the shortage of skilled workers. To address this problem, this paper presents a push‐based gangue‐sorting robotic system for industrial dynamic conveyor scenarios. The system is organized as a task‐specific closed‐loop robotic sorting framework that couples gangue perception, identity‐consistent tracking, contour‐level pose estimation, time‐aware push planning, coal‐carryover avoidance, and multimodal execution‐outcome verification for dynamic mine‐conveyor operation. In contrast to conventional grasp‐lift‐place sorting systems, the proposed system exploits conveyor support and uses planar pushing as the manipulation primitive, which is better suited to large, heavy, and irregular gangue pieces in constrained mine environments. Long‐term tests show that the proposed system can stably complete gangue separation under isolated deployment conditions without manual intervention. In industrial online tests, the recognition accuracy for dynamic gangue exceeds 92%, the average planning time of a single robotic arm is about 0.4 s, the average execution time is about 2.8 s, and the highest single‐arm gangue removal rate reaches 61.52%. A two‐stage cascade estimate based on the highest single‐arm result suggests the potential for line‐level removal above 80%. The proposed execution‐outcome verification module achieves verification accuracy exceeding 95%.