# 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
Because it is based on MINCO trajectory optimization on flat space, which conforms to the robot kinematic model while minimizing the control cost, the trajectory is smoother and the trajectory length is shorter. The relationship between obstacles and the initial position of the robot in the multiobstacle scene is shown in Figure 19, where obstacle 2 is about 5 m in front of the robot. The \(X\)-axis and \(Y\)-axis distances between obstacle 1 and obstacle 2 are about 1.3 m and 0.9 m. The \(X\)-axis and \(Y\)-axis distances between obstacle 3 and obstacle 2 are about 1.4 m and 1.1 m. Obstacle 4 completely blocks the road on the right side, and obstacle 1 and obstacle 3 are at a small distance from the left laneway wall, so the robot cannot pass through them either. The planning experiment was conducted by setting the target point about 10.6 m in front of the robot. The ideal situation is that the robot passes through the middle of the obstacles and reaches the end point without hitting the obstacles. The experiments also compare the planning of the MINCO trajectory method with the Fast-Planner algorithm. The experimental results of the MINCO trajectory method are shown in Figure 20. Figure 20 shows the initial planning result given by the algorithm, Figure 20 shows the final planning result of the algorithm and the point cloud map of the robot moving into the obstacle interval, and Figure 20,d show the map of the actual position of the robot entering the obstacle interval and leaving the obstacle interval, respectively. Figure 19: Multiple obstacle scenarios: (**a**) site environment. (**b**) Rviz 3D point cloud map. Figure 18: Comparison between MINCO trajectory algorithm and Fast-Planner algorithm for single obstacle trajectory. The experimental results of the Fast-Planner planning algorithm are shown in Figure 21. Figure 21a shows the initial planning result given by the algorithm. Obviously, this trajectory does not avoid the obstacles. Figure 21b shows the final planning result of the algorithm and the point cloud of the robot movement regarding the obstacle interval. The final planning is completed only when the robot moves close to the obstacle, where the red curve without points is the actual trajectory of the robot. Figure 21c,d show the actual position maps of the robot entering the obstacle interval and leaving the obstacle interval, respectively. The final trajectories of the MINCO trajectory method and the Fast-Planner algorithm are shown in Figure 15:0 The MINCO trajectory method plans a total of eight times during the robot movement, with a total planning time of 0.18 s, and the length of the robot movement trajectory is 11.04 m. The Fast-Planner algorithm performs a total of 38 planning times during the whole process, with a total time of 0.375 s, and the length of the robot motion trajectory is 11.71 m.  
From the above data as well as the trajectory comparison graphs, it can be seen that the MINCO trajectory method is better than the Fast-Planner algorithm in a number of indexes in the case of multiple static obstacles, and is more suitable for CMRRs. Next, some phenomena in the experiment are analyzed. Since the length and width of the robot used in the experiment are about 1.2 m and 0.8 m, and the \(X\)-axis and \(Y\)-axis distances between obstacle 1 and obstacle 2 are only 1.3 m and 0.9 m, the safety thresholds are very small. So, both algorithms plan several times to adjust them. The Fast-Planner algorithm, because the front-end search does not involve the environment information, needs several adjustments to complete the whole trajectory planning task, resulting in a costly trajectory with multiple turns. The MINCO trajectory method completes the safe  
Figure 15:1 Experimental process of Fast-Planner algorithm in multiobstacle scenarios: (**a**) start planning; (**b**) trajectory execution; (**c**) entering the obstacle zone; (**d**) leaving the obstacle zone.