# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
###### Abstract
[PERSON] [17] introduced a dynamic weighting factor in the heuristic function, adjusted the search neighborhood range, and reduced the number of search nodes, thereby improving the efficiency of the algorithm. [PERSON] [18] extended the search direction of eight neighborhoods to twelve neighborhoods to reduce the number of nodes in the search process of the bidirectional A* algorithm. [PERSON] [19] improved the traditional eight-neighborhood search to a five-neighborhood search, which effectively reduced the traversal of irrelevant points and significantly improved the path search efficiency. [PERSON] [20] proposed an improved bidirectional A* algorithm, adding local path constraints to the search path, and introducing a cubic B-spline curve to smooth the inflection point, which successfully solved the problem of diagonal collision and non-smooth path. Implementing map inflation preprocessing, [PERSON] [21] developed an obstacle-avoidance strategy that effectively eliminates path-obstacle intersections through geometric constraint transformation of environmental configuration space. [PERSON] [22] proposed a path-smoothing strategy to reduce the redundant points in the path planned by the bidirectional A* algorithm (B-A*). However, the above algorithm cannot achieve local obstacle avoidance. Therefore, in recent years, scholars have often combined global path planning with local path planning. A novel path planning method combining improved A* algorithm and dynamic window approach (DWA) was developed by [PERSON] [23] for legged robots operating in coal mine environments. On the premise of ensuring the safety and stability of robot walking, compared with the traditional A* algorithm, this method not only shortens the calculation time, but also reduces the path length and makes the path smoother. By combining these two algorithms, the global path planning ability is further enhanced, so that the robot can effectively avoid dynamic and static obstacles in a multi-obstacle environment. [PERSON] [24] developed an improved A*-DWA fusion algorithm, which optimizes the path of mobile robots through real-time dynamic obstacle avoidance to ensure that the planned path is safe and efficient, and has good smoothness. [PERSON] [25] introduced a path planning method combining an improved Dijkstra algorithm and DWA. This method can maintain the smooth speed and smooth trajectory of the robot when facing static and dynamic obstacles. At the same time, the angular velocity fluctuation is small, which proves the stability of the robot motion and the ability to adjust the direction in real time, and has a good obstacle avoidance effect. To address the issues in the A* algorithm for underground roadway path planning, such as diagonal movement collisions at corner points, proximity to roadway edges, and unsmooth paths, this paper proposes the following improvements. First, a crossing point detection mechanism is introduced during diagonal movements to prevent collisions when the path crosses obstacle corners. Second, roadway width information is integrated into the heuristic function, enabling the planned path to avoid narrow areas, thereby improving the path quality and ensuring safe navigation. Additionally, based on the global path features planned by the A* algorithm, an appropriate B-spline curve order and smoothing factor are selected to achieve a smooth transition of the path. For local obstacle avoidance, the smoothed global path is divided into multiple sub-goals, with the DWA algorithm using these sub-goals as local targets for obstacle avoidance. To prevent excessive deviation from the global path during obstacle avoidance, a distance cost function is added to the evaluation function of the DWA algorithm. Finally, experiments demonstrate that the improved adaptive A* algorithm is able to plan a path closely following the center of the roadway, avoid collisions during diagonal movement, generate smooth paths without sharp turns, and effectively avoid newly introduced static and dynamic obstacles.