## 1 Introduction  
Coal mining is mainly underground mining. The environment of underground mining is complex and dangerous. In order to ensure the safe and efficient mining of coal, it is necessary to inspect the coal mine roadway regularly. The mining inspection robot can effectively prevent the occurrence of underground dangers and reduce the labor intensity of the workers. Compared with manual inspection, the mining inspection robot not only has flexibility and intelligence but also improves the accuracy and efficiency of inspection [1]. The inspection robot needs to have functions such as precise positioning, autonomous movement, and safety detection to replace manual safety inspection. Among them, autonomous movement is one of the key technologies of the mining inspection robot. This is because traditional path-planning methods are often based on structured maps, which are difficult to adapt to the complex and changeable mine environment. The roadway under the mine is a typical unstructured environment. The unstructured environment refers to the environment or scene where the robot works in an uneven surface material performance, irregular structural changes, and irregular scenes. The roadway under the mine is narrow and tortuous, and the environment is complex. Therefore, the mine inspection robot can independently plan the path in this environment, which has become the focus of many scholars [2]. Primary algorithms for path planning are categorized into conventional path-planning methods, sampling-based path-planning methods, and smart bionic algorithms. Conventional algorithms predominantly encompass the A* algorithm [3], Dijkstra algorithm [4],and the artificial potential field method [5]. The path-planning algorithms based on sampling mainly include the rapid-expansion random tree algorithm [6] and probability roadmap algorithm [7]. Intelligent bionic algorithms include the ant colony algorithm [8], lion swarm algorithm [9], etc. Owing to its probabilistic completeness, the RRT algorithm excels in handling path-planning problems within a complex unstructured environment. However, the path generated by the algorithm is often not optimal. Consequently, specialists and academics have extensively studied the path-planning problems of the RRT algorithm in complex environments. [PERSON] et al. [10] proposed an adaptive target bias strategy and a regional sampling adjustment strategy for the problem that the traditional RRT algorithm does not easily converge and the sampling success rate is low in multi-obstacle scenarios. Experimental findings indicate that the improved RRT algorithm has greatly improved the running time, search times, and sampling success rate compared with the original algorithm and the other two target-driven algorithms. [PERSON] et al. [11] proposed an SMRS-RRT algorithm for the problem of low search efficiency and excessive memory occupation of traditional RRT in complex environments with multiple obstacles. Initially, the global grid map is simplified. On this basis, the optimal path point set from the starting point to the target point is found, and the path is used as the guide path to expand through the intelligent sampling factor to obtain the intelligent sampling area. Subsequently, the iterative search is carried out in the intelligent sampling area to obtain a low-cost and collision-free path from the starting point to the target point. The outcomes of simulations indicate that the suggested algorithm significantly enhances the effectiveness of conventional RRT searches, hastens the rate of convergence, and diminishes memory usage. [PERSON] et al. [12] suggested using DVSA-RRT to divide the sampling space using the dynamic variable sampling area formula, then selecting a particular sampling zone. Subsequently, the preliminary planning of the path is finalized through the application of collision detection, focusing on safe distance, a strategy of probabilistic target offset, and the expansion of steps across multiple levels.