# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 1 Introduction
[PERSON] et al. [45] proposed a dynamic multi-heuristic A* (DMHA*) algorithm to solve the problem of wasting a lot of search time around local minima, while [PERSON] et al. [46] proposed an improved A* algorithm that judges the presence of obstacles between the current path point and the target point to shorten the path. However, the computational cost of [PERSON]'s method is high, and it cannot smooth the turning path. [PERSON] et al. [47] adopted a hybrid-state A* kinodynamic path searching method to find a safe and minimum-time initial trajectory in the discretized control space and to improve the smoothness and clearance of the trajectory by a B-spline optimization. [PERSON] et al. [48] introduced a rewiring process to remove redundant points in the path, which in turn re-corrected the path and optimized the path length, but there were still invalid turning points. [PERSON] et al. [49] assigned weights to each pair of vertices in the path generated by the A* algorithm to develop a pruning strategy, which removed invalid waypoints from the path and achieved path smoothing, creating a better navigation experience for the user. [PERSON] et al. [50] designed a dynamic fusion pathfinding algorithm (DFPA) based on Delaunay triangulation and improved A* algorithm to implement path planning for mobile robots. In summary, the A* algorithm, which incorporates heuristic intelligent search, exhibits good path optimality and search efficiency and is more suitable for static map scenarios. However, due to the special characteristics of plantation forests (such as the unstructured and complex inter-forest environment and the high density of tree trunks and branches obstruction), the path planned by A* algorithms may increase the UAV's flight energy consumption and affect flight safety. In addition, current improvements to the A* algorithm often sacrifice the performance of other indicators to achieve the desired indicator performance in specific scenarios. To address these challenges, this paper proposes an improved algorithm that enhances the autonomous flying capability of UAVs in forest environments and reduces flight energy consumption by developing cost evaluation functions, heuristic functions, turning point removal strategies, and smooth optimization. In addition, the innovation and main contributions of this paper are as follows:  
1. This paper proposes a segmented cost evaluation function with parameterized weight factors (\(a\) and \(b\)) assigned to actual cost and heuristic cost in each stage, where the weight factor of the heuristic cost function is adaptively and dynamically adjusted. The optimal parameter combinations under different environmental conditions are obtained to enhance the global search capability for the fast and accurate acquisition of optimal paths. 2. The algorithm incorporates turn cost with UAV heading angle constraints into the heuristic function to enhance heuristic search, reducing unnecessary turning pointsand sharp turns, making the planning path smoother, and guiding the UAV towards the target direction. 3. The paper proposes a strategy for removing redundant turning points by reselecting path nodes on path segments composed of turning points, avoiding the process of turning point removal from falling into local optima while achieving a quadratic programming process on the path, allowing for a more significant reduction of turning points in the shortest path. The rest of the paper is organized as follows. Section 2 presents a comprehensive account of the proposed enhancements to the traditional A* algorithm, along with the experimental design details. The comparative simulation results of various algorithms are illustrated in Section 3. The experimental results are discussed in Section 4. Finally, the conclusions are drawn in Section 5.