Path planning of wheeled coal mine rescue robot based on improved A* and potential field algorithm

Coal mine rescue robots perform search and rescue tasks in unstructured underground tunnel environments. Traditional path planning algorithms may encounter issues such as low efficiency, non-optimal paths, and poor smoothness when applied to search spaces that are large or complex. Additionally, tunnels feature complex environmental characteristics such as intersections, where robots are prone to deviating from preset routes or scraping against tunnel walls. To address these challenges and enhance the navigation accuracy of robots, improvements to the path planning algorithm for wheeled coal mine rescue robots are proposed: ① The heuristic global path planning A* algorithm is enhanced by employing layered neighborhood search and pruning techniques to optimize the search process. The cost function is refined to better balance the influence of actual cost and heuristic cost, thus more accurately assessing the cost of each node, adapting to real situations, reducing computational complexity, and smoothing the path using B-spline methods. ② The Random Sample Consensus (RANSAC) fitting algorithm is utilized to construct a geometric model of coal mine tunnel walls, facilitating the extraction of feature point coordinates of intersections for inclusion in the planning system. The path is optimized using the local support property of B-spline basis functions. When additional path optimization points are added subsequently, only the shape of the curve in the corresponding interval is affected, leaving the rest of the path unaffected. ③ A comprehensive local force field is established based on the constructed environmental geometric model and extracted feature points. Adjustment coefficients are introduced to optimize the distribution of the force field, and motion control is achieved using the Particle Swarm Optimization (PSO) optimized PID (Proportion Integral Differential) algorithm, enhancing the robot's adaptability to complex environments such as tunnel intersections. The feasibility of the algorithm principles and applications is validated through MATLAB and ROS (Robot Operating System) simulations. Experimental results demonstrate that the proposed method can realize functions such as path planning and autonomous driving in complex intersection environments.