*2.2. Path Planning Methods*

Based on high-precision positioning and unmanned driving technology, many unmanned equipment path planning algorithms have been studied, which are mainly divided into artificial potential field methods, graph search algorithms, and sampling-based algorithms. The artificial potential field, proposed by Khatib, is a virtual force method, which makes the equipment subject to the repulsive forces from obstacles and gravity and from the target at the same time [15–18]. This method is simple to calculate, and the obtained path is safe and smooth, but it easily falls into a local optimal solution. The graph search algorithm converts the map for path planning into a graph and obtains the optimal path through graph theory, including the Dijkstra algorithm, A\* algorithm, etc. [19–24]. This method takes into account both efficiency and completeness, but the map needs to be rasterized to complete the graph conversion, resulting in poor path smoothness. The sampling-based algorithm narrows the search space by discrete sampling in a continuous space. It is a Monte Carlo method with uniform space, including the Probabilistic Road Map Method (PRM), Rapid Random Extended Tree Method (RRT), etc. [25–29]. It has the advantages of fast search speed and simple environment modeling, but it cannot obtain a global optimal solution, and its efficiency is greatly affected by its step size and sampling mode.

The RRT algorithm [30] was proposed by Lavalle et al. in 1998. It is a random sampling algorithm that uses incremental growth to achieve rapid search in non-convex high-dimensional spaces. The RRT algorithm does not need to rasterize the search space and has the advantage of high search space coverage. It is suitable for dealing with scenes containing obstacles and motion constraints. Therefore, it is widely used in path planning for intelligent devices. The RRT algorithm is a Monte Carlo method. It usually takes the starting point as the root node and generates a random extended tree through random sampling. When the child node reaches the target area, the sampling is completed, and a feasible path is obtained.

The sampling of the RRT algorithm is random, and the generated path is a feasible path rather than an optimal path. Therefore, a variety of improved methods are derived. The RRT\* algorithm [31] was improved based on the RRT algorithm, and the goal is to improve the performance of the RRT algorithm in order to ascertain the optimal path. The RRT\* algorithm continuously optimizes the path during the search process by reselecting the parent node and rerouting. With the increase in iterations, the obtained path gradually approaches the optimal path.

There is relatively little research on path planning in underground mining, and currently it is mainly focused on underground disaster relief, surveying, and mapping. Ma et al. [32] proposed a path planning method considering gas concentration distributions. The global working path for a coal mine robot was planned based on the Dijkstra algorithm and the ant colony algorithm, then local path adjustments were carried out. The research object was coal mine robots, and the scene was disaster relief. Mauricio [33] proposed a strategy of exploration and mapping for multi-robot systems in underground mines where toxic gas concentrations are unknown. The principal algorithm was behavior control. Papachristos et al. [34] considered the challenge of autonomous navigation, exploration, and mapping in underground mines using aerial robots, and proposed an optimized multimodal sensor fusion approach combined with a local environment morphology-aware exploration path planning strategy. The research objects were four-rotor drones, and the scene was underground surveying and mapping. Gamache et al. [35] set up a shortest-path algorithm for solving the fleet management problem in underground mines with consideration for dispatching, routing, and scheduling vehicles. The solution approach was based on a shortest-path algorithm. They considered all single-lane bi-directional road segments of the haulage network. The research focused more on mining scheduling than vehicle path planning. The solution provided the direction of the vehicle at an intersection, rather than the trajectory of a single device. It can be considered as a form of cooperative scheduling, which relates to the upper-level control of intelligent vehicles. Larsson [36] developed a new flexible infrastructure-less guidance system for autonomous tramming of center-articulated underground mining vehicles. The results showed that it was capable of autonomous navigation in tunnel-like environments. However, the process of path planning was not described. Tian [37] presented a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Yuan [38] focused more on path planning and an obstacle avoidance mechanism under the complex geological conditions of a coal mine. Dang [39] presented a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Song [40] considered both the distance of the path and some hybrid costs to obtain a global path. Bai [41] proposed a multisensor data fusion algorithm based on genetic algorithm optimization of the variably structured fuzzy neural network. Ma [42] improved both the distance function and the selection of child nodes. The feature of this paper is the full consideration of the environment with a vectorized map and the articulated kinematics of underground mines. A comparison between some typical underground mine path planning studies is shown in Table 1.


**Table 1.** Comparation of typical underground mine path planning research.
