Path planning of coal mine underground robot based on improved artificial potential field algorithm

Path planning is one of the key technologies that urgently need to be solved in the application of coal mine robots in narrow underground roadways. A path planning method for coal mine robots based on improved APF algorithm is proposed to address the issues of traditional artificial potential field (APF) algorithms that planning paths in narrow roadway environments may be too close to the roadway boundary, as well as the possibility of unreachable targets and path oscillations near obstacles. Referring to the relevant provisions of the Coal Mine Safety Regulations, the boundary potential field between the two sides of the roadway is established. The robot's path is planned as much as possible in the middle of the roadway to improve the safety of robot travel. The method introduces regulatory factors into the repulsive potential field of obstacles to solve the problem of unreachable targets. The method introduces corner constraint coefficients to smooth the planned path, reduce oscillations, improve planning efficiency, and ensure the safety of the planned path. The simulation results show that when the target point is very close to the obstacle, the improved APF algorithm can successfully plan a path that can reach the target point. The improved APF algorithm reduces the planning cycle by an average of 14.48% compared to traditional algorithms. The cumulative value of steering angle reduces by an average of 87.41%, and the sum of absolute curvature values is reduced by an average of 78.09%. The results indicate that the improved APF algorithm plans smoother paths, shorter path lengths, and has higher planning efficiency and safety.