# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Experiments on Trajectory Planning of CMRRs
#### 4.1.1 Static Scene Planning Experiment  
Single Obstacle Scene Planning Experiment  
The first is a simple single obstacle scenario. A bucket is artificially set as a static obstacle at about 4.5 m in front of the robot start point. As shown in Figure 15a, the target point is set about 12 m in front of the start point to examine the robot's trajectory planning effect. Figure 15b shows the corresponding RVIZ 3D point cloud, in which \"world\" represents the world coordinate, the red axis is the \(X\)-axis, the green axis is the \(Y\)-axis, the blue axis is the \(Z\)-axis, and the coordinate \"position\" indicates the robot position. The initial robot position coincides with the world coordinates. Compare the MINCO trajectory-based planning method with the Fast-Planner algorithm [32]. In the experiment, the robot was remotely controlled to the same initial point position; the two algorithms were run separately. Then, the same target point was given for comparison. The experimental results of the MINCO trajectory method are shown in Figure 16. The blue curve is the target trajectory planned by the algorithm, and the light-blue polyhedron is the safety corridor. Figure 16a shows the planning results, and Figure 16b shows the actual positional relationship when the robot moves next to an obstacle. Figure 15: Single obstacle scenario: (**a**) site environment map; (**b**) RVIZ 3D point cloud map. “world” represents the world coordinate, the red axis is the \(X\)-axis, the green axis is the \(Y\)-axis, the blue axis is the \(Z\)-axis, and the coordinate “position” indicates the robot position. The experimental results of the Fast-Planner planning algorithm are shown in Figure 17. The red curve is the target trajectory planned by the algorithm. Figure 17a shows the initial planning results, and Figure 17b shows the results after the algorithm replanning. Replanning is performed with the current position of the robot as the starting point, and the target point remains unchanged. Figure 17c,d show the 3D point cloud map and the actual position relationship when the robot moves next to an obstacle, respectively. The MINCO trajectory method performs only one planning in the initial stage of the robot movement, and the planning time is 0.029 s. The trajectory is sampled at the \(X\)-axis interval of 0.05 m to obtain the sampled trajectory points (all the points below are sampled at the \(X\)-axis interval of 0.05 if not pointed out), and the length of the planned trajectory is calculated to be 12.54 m. The Fast-Planner algorithm performs three planning times in the whole process, with a total time of 0.034s, and the length of the robot trajectory was 12.75 m.  
The localization information in the LOAM algorithm was used to obtain the two real running trajectories of the robot in the laneway, respectively. The algorithm trajectory comparison is shown in Figure 18. It can be seen that compared with the Fast-Planner algorithm, in the case of single obstacle planning, the MINCO trajectory method is better than the Fast-Planner algorithm both in the planning time, trajectory length, and in the trajectory smoothing, which is more suitable for underground mobile robots. The reason is because the algorithm in this paper plans within the safety corridor, the safety threshold is large enough in this simple scenario, and only one planning is performed, so the total  
Figure 16: Experimental process of MINCO trajectory algorithm in single obstacle scenarios: (**a**) planning result; The blue curve is the target trajectory planned by the algorithm, and the light-blue polyhedron is the safety corridor. (**b**) actual pose. Figure 17: Test process of Fast-Planner algorithm in single obstacle scenario: (**a**) start planning; (**b**) replanning; (**c**) trajectory execution; (**d**) actual pose relationship. The red curve is the target trajectory planned by the algorithm. planning time is less.