**3. Constraints Formulation**

Autonomously driven underground intelligent vehicles initiate a process of interaction between the underground environment and the vehicle. Before path planning, it is necessary to establish the environment, vehicle features, and interaction constraints.

#### *3.1. Drift Environment Formulation*

Drifts are the main environments for underground intelligent vehicles. These intelligent vehicles start at the stope filled with ore, drive through the drifts, then reach the orepass, and offload the ore. The point cloud is a common method for intelligent mine environmental modeling, which is generated by laser scanners [43]. Figure 3 shows the point cloud data obtained through SLAM, which is a typical drift environment. A typical design profile of a drift is shown in Figure 4 [44]. Where vehicles are required to travel through drifts, the vehicle cross-section will fix the dimensions of the opening. Underground intelligent vehicles do not make vertical movements, so it is possible to process 3D point cloud data into a 2D map by extracting the waistline and then converting the map into a graph for the path planning algorithm.

**Figure 3.** Typical drift environment point cloud data by SLAM.

**Figure 4.** A typical design profile of a drift.

This paper uses the vectorized method instead of the common rasterization method as the map preprocessing method. The drifts are narrow and long with complicated surfaces. In the process of rasterization, the grid size has a great influence. Large-size grids cannot express the small edges and corners of the drifts well, resulting in a lack of detailed map information, and collisions during driving of the vehicles. Small grids lead to large total grids, which result in calculations being carried out in increments and a reduction in

efficiency. Therefore, the rasterization method has certain limitations in processing the drift environment. A vectorized map can effectively improve these shortcomings. It expresses map information such as points, lines, and areas by recording coordinates. The points are represented by the north coordinate and east coordinate. The lines are represented by a series of ordered coordinates. The surfaces are represented by a series of ordered and closed coordinates. We recorded the coordinates of the scattered points on the map boundary through dense interpolation and connected them to form lines. The dataset included coordinate points, lines, and polygons, named as *Polygonmap* in the following. The effect comparison between the rasterized map and vectorized map is shown in Figure 5. The rasterized map used a 22 × 41 matrix, and the dataset was 26.4 kb, as shown in Figure 5a. The vectorized map included 17 points, 17 lines, and 1 polygon. The dataset was 0.9 kb, as shown in Figure 5b. The vectorized map has great advantages in map refinement and data size.

**Figure 5.** Comparison of the rasterized map and vectorized map. (**a**) The rasterized map; (**b**) the vectorized map.
