## 4 Analysis and Verification
### Path Planning Experiment  
#### 4.3.1 Simulation Experiment Verification and Analysis  
In order to verify the effectiveness and robustness of the fusion algorithm in this paper, the proposed algorithm was simulated and verified in the MATLAB environment. The improved ant colony algorithm in this paper was compared with the basic ant colony algorithm, RRT algorithm, and the algorithm in reference [24]. The simulation results are shown in Figure 8.  
Figure 7: Build diagram: (**a**) experimental environment; (**b**) LiDAR builds maps; (**c**) depth camera build maps; (**d**) map after data fusion. As can be seen from Figure 8, all four algorithms were able to find drivable paths during the process of running the search. The path planning curve of the RRT path planning algorithm is shown in Figure 8c, and the path length was 34.9690 m. It can be seen from the figure that compared with the traditional RRT path planning algorithm, the ant colony algorithm was more efficient in exploring path planning with short and straight paths, as shown in Figure 8a,c. The path planned by the ant colony algorithm will form a certain peak at the turning point, which increases the turning angle of the intelligent robot to a certain extent, affecting the passing efficiency of the intelligent robot. In this paper, by comparing the polynomial fitting curve and the cubic B-spline curve optimization path experiment, we found that the polynomial method only deals with the peak of the path. However, the cubic B-spline curve flattened the overall path, smoothed the turning angle of the path, and accelerated the convergence iteration speed, as shown in Figure 9. Figure 8d is the iterative convergence trend diagram of the three ant colony algorithms. It can be seen from the figure that the ant colony algorithm proposed in [20] had a faster convergence speed, and the optimal path length obtained in the 13 th iteration was 29.1848 m. In the 11 th iteration of the improved ant colony algorithm proposed in this paper, the shortest path was 28.0416 m. In the 11 th iteration of the improved ant colony algorithm proposed in this paper, the shortest path was 28.0416 m. The algorithm had less fluctuation in the initial stage, which improved the convergence speed. Therefore, the improved ant colony algorithm in this paper had better iterative convergence, which increased the smooth and safe passage of the intelligent robot. Figure 8: Comparison of path algorithms: (**a**) the optimal path of the basic ant colony algorithm; (**b**) the optimal path of the ant colony algorithm in the literature [24]; (**c**) the optimal path of the RRT algorithm; (**d**) convergence trend of three ant colony algorithms. The starting point \(S\), the target point \(T\), and the unknown obstacle were set in the raster map, and the fusion algorithm in this paper was used to complete the simulation experiment of different positions of the two unknown obstacles. The experimental results are shown in the Figure 10.  
As can be seen from Figure 10, the improved ant colony algorithm first set the global optimal path, while the dynamic window algorithm detected the position of obstacles according to the on-board sensors and set local target points on the global path. If an unknown obstacle is detected, the dynamic window method will set local target points combined with the evaluation function of global path information and will track the local target points until the target point is reached. When avoiding local obstacles, the dynamic window method will ensure that the intelligent robot will not collide with the obstacles, and the direction of real-time tracking of local target points will be consistent with the direction of the target points. During the operation of the intelligent robot, the control parameters will be output in real time according to the number of control nodes, which is convenient to realize the automatic feedback control of the intelligent robot. When encountering unknown obstacles, the intelligent robot starts to slow down for local obstacle avoidance.