## 2 Related Work  
The path planning of underground unmanned mine vehicles refers to the fact that, in the underground tunnel environment, the vehicle finds a collision-free path from the start point to the target point according to certain criteria [6]. Path planning methods are generally divided into global path planning and local path planning [7; 8]. Global path planning is a process of quickly and accurately planning the optimal path on a known environmental map, taking into account the principles of shortest path length, lowest vehicle energy consumption, and highest vehicle driving safety based on traffic tasks, using appropriate global path planning algorithms [9]. Local path planning is based on the global path, utilizing sensors to dynamically collect environmental information and generate real-time vehicle paths. [10; 11]. At present, global path planning algorithms mainly include A* [12], Dijkstra [13], and rapidly-exploring random trees (RRTs) [14]. Local path planning algorithms include the artificial potential field algorithm (APF) [15], DWA [16], and deep learning method (DL) [17]. Dijkstra [18] uses a breadth-first search, which has the advantages of simple principles and a small amount of computation, but the computational efficiency is low. RRT [19] is a sampling-based algorithm with probabilistic integrity, but the algorithm search is blind, and the convergence speed is slow. The A* algorithm, as a heuristic search algorithm, has the characteristics of simple and easy implementation, a small amount of required computation, and high efficiency. However, when the complexity of the environment increases, the amount of computation requiredincreases sharply, so it is difficult to find the optimal path. At the same time, the path planned by the A* algorithm has many inflection points, which is not conducive to vehicle tracking [20]. APF is an error control strategy with real-time obstacle avoidance, but, when multiple obstacles appear, zero potential energy points are prone to occur, resulting in vehicles being unable to reach the target point [21]. Path planning methods based on DL generally have feedback mechanisms, strong robustness, and good adaptability, but they have a slow convergence speed, high temporal and spatial complexity, and are prone to local convergence [22]. The DWA algorithm transforms the obstacle avoidance problem of the vehicle into the optimal speed execution problem by simulating the path of the vehicle at different speeds and scoring them [23]. The DWA algorithm has significant advantages in dynamic obstacle avoidance, but it is prone to local minima in complex environments, thereby reducing algorithm performance. In [24], an improved A* algorithm that uses different coefficient ratios to find the optimal path by changing the weight of the heuristic function in real-time and improving the smoothness of the path is proposed, and the path length is significantly reduced. In [25], a bidirectional A* algorithm is proposed by considering the factors affecting road escape. The experiment shows that fewer nodes are generated, and the planned path is shorter. In [26], a joint algorithm based on improved A* and APF is proposed. A* is improved by using exponential function weighting and spline interpolation methods. APF is improved by adding correction and escape factors, respectively, making the generated path smoother. In [27], the concepts of the relative velocity potential field and acceleration potential field are proposed in the artificial potential field method to achieve optimal path selection. In [28], A* and DWA are used based on a grid diagram to achieve autonomous path planning for underground vehicles in coal mines. The above examples are all explorations and studies of autonomous path planning in underground mines, but most of the algorithms are still in the simulation and testing stage, and their adaptability to the auxiliary tunnel environment of coal mines needs to be verified.