# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
###### Abstract  
This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics--including path length, average curvature, fluctuation degree, and direction change rate--an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot's kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA's evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. A* algorithm; dynamic window method; smooth path; adaptive B-spline curve; anti-diagonal collision  
The path planning of mobile robots is a complex but crucial task. Its core goal is to find an optimal and collision-free path from the starting point to the target point for the robot in two-dimensional or three-dimensional space [1; 2]. In this process, the robot needs to make motion decisions based on objective functions (such as the shortest path, the shortest time, the least resource cost, etc.). According to the perception of environmental information, path planning is divided into two categories: global path planning and local path planning [3]. Local path planning algorithms include the artificial potential field method [4], simulated annealing method [5], dynamic window method [6], neural network [7], and reinforcement learning [8]. Global path planning algorithms include the Dijkstra algorithm [9], RRT algorithm [10], D* algorithm [11], A* algorithm [12], and so on. The A* algorithm is widely used because of its simple calculation and efficient generation of the shortest path. However, the A* algorithm has problems such as long search time, low efficiency, and uneven path. Many scholars, both domestically and internationally, have proposed various improvement strategies to address the limitations of the A* algorithm. [PERSON] [13] effectively reduced the turning frequency of the path by introducing the angle evaluation mechanism into the heuristic function, but also significantly increased the burden of node calculation. A bidirectional A* algorithm combining four-domain and eight-neighborhood search mechanisms was developed by [PERSON] [14]. This algorithm significantly shortens the search time, but it is easy to fall into a local optimal solution. [PERSON] [15] optimized the cost function of the bidirectional A* algorithm by introducing a weight coefficient, which can generate the shortest path, but there are too many inflection points in the path, which is not conducive to the smooth driving of the mobile robot. [PERSON] [16] developed an A* algorithm integrated with jump point search, achieving memory reduction through long-distance jumps. However, excessive unnecessary jump point searches could potentially degrade search efficiency.