# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 3 Results
### Comparison Experiments Results Based on 3D Point Cloud Map of Plantation Forest  
To validate the effectiveness of the improved A* algorithm in practical plantation forest environments, it was compared with the traditional A* algorithm, an improved A* algorithm proposed in the literature [38], the RRT algorithm, and the APF algorithm. The evaluation criteria were based on the path search time, path length, and total turning angle of the generated path. Figure 8 depicts the trajectories planned by the four algorithms under different starting and ending points, where the blue five-pointed star indicates the starting point, and the green circle represents the target position. Moreover, the coordinates presented in the figure are the grid coordinates of the map after re-rasterization, and each grid was set to \(1\times 1\) in size. The results of evaluation metrics corresponding to different algorithms are shown in Table 3.  
Figure 8: Path planning trajectory with different starting and target point positions. (**a**) start point coordinates (10, 182), end point coordinates (240, 8); (**b**) start point coordinates (38, 35), end point coordinates (220, 162); (**c**) start point coordinates (8, 112), end point coordinates (206, 138); (**d**) start point coordinates (72, 194), end point coordinates (125, 8); Note: Where blue path trajectory represents traditional A* algorithm, green represents the improved A* algorithm in this paper, purple represents the improved A* algorithm in literature [38], red represents the APF algorithm, and black represents the RRT algorithm.  
The results in Table 3 indicate that the traditional A* algorithm generates a path with a short path length and spends less time in the pathfinding process due to its inherent heuristic capabilities.  
However, it generates more turning points and has a larger total turning angle, and the trajectory is often close to the edge of obstacles, which increases the risk of collision. Due to the lack of heuristic guidance, the RRT algorithm based on random sampling generates paths with a large number of nodes, which appear more tortuous, resulting in more time consumption during the search process, and the obtained path length and total turning angle are the largest. The APF algorithm generates paths with shorter lengths but takes the longest time in the pathfinding process. The improved A* algorithm proposed in the literature [38] reduces the path search time and path length to some extent and can generally achieve a reduction in the total turning angle. However, the trajectory is sometimes close to the edge of obstacles. In contrast, the improved A* algorithm proposed in this paper has the shortest path search time and generates paths with no significant turning points, the shortest length, and the smallest total turning angle. Importantly, it avoids the situation where the algorithm searches for a path close to the edge of an obstacle to obtain the shortest path. Specifically, compared with the traditional A* algorithm, the improved A* algorithm reduces the path search time by an average of 33.53%, the path length by an average of 6.00%, and the total turning angle by an average of 92.17%. Compared with the improved A* algorithm proposed in the literature [38], the improved A* algorithm reduces the path search time by an average of 20.89%, the path length by an average of 3.98%, and the total turning angle by an average of 90.90%. Compared with the RRT algorithm, the improved A* algorithm reduces the path search time by an average of 95.34%, the path length by an average of 30.69%, and the total turning angle by an average of 98.95%. Compared with the APF algorithm, the improved A* algorithm reduces the path search time by an average of 97.07%, the path length by an average of 1.58%, and the total turning angle by an average of 94.05%.