Autonomous navigation and collaborative control of coal mine roadway cleaning robot

To address localization degradation of coal mine roadway cleaning robots in long, straight roadways where satellite signals are unavailable, difficulties in multi-process coordination, and reliance on manual intervention for attachment replacement, a comprehensive control method integrating autonomous navigation, multi-tool collaborative control, and automatic attachment replacement of fixture is proposed. For autonomous navigation, a fusion localization method combining angle-clustering-based ground segmentation with the Unscented Kalman Filter (UKF) was employed to suppress axial drift, and an improved pose-aware A^* global planning algorithm was designed to eliminate the need for final pose adjustment. For collaborative control, a layered architecture based on a hierarchical finite-state machine was constructed to enable autonomous scheduling of multiple processes. For attachment replacement, a distance-triggered heterogeneous perception fusion strategy with coarse-to-fine staging was proposed; joint-state feedback and visual localization information were continuously weighted and fused to complete the entire attachment replacement process autonomously. An underground industrial test involving a cumulative operating time of 160 h and an advance of 60 m was conducted. Results showed that the coal mine roadway cleaning robot had a mean trajectory error of 0.01 m, a final heading deviation of 3.8°, and a dynamic obstacle avoidance success rate of 92%. The full-process success rate was 80%, and the manipulator end-effector tracking error was 0.12 m. The success rate of automatic attachment replacement was 93.3%, and the mean duration per replacement was 4.8 min. The number of operators per shift was reduced from 5 to 2, a reduction of 60%, while per-capita efficiency increased from 1.8 m³/shift to 2.6 m³/shift.