# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 1 Introduction  
Unmanned aerial vehicles (UAVs) have been extensively utilized in forestry applications such as forest inspection, rescue, and monitoring due to their high flexibility and real-time inspection capabilities [1, 2]. However, limited payload capacity and hardware technology pose challenges to the endurance of UAVs [3]. With the expansion of plantation forest areas, it has become crucial to enhance the autonomous flight and endurance capabilities of UAVs to efficiently perform continuous inspection and monitoring tasks. Due to the level of industrial development, most researchers have optimized the path planning algorithm to achieve reasonable task path planning according to the actual application needs of UAVs, thereby reducing energy consumption and enhancing obstacle avoidance and safety [4]. Key performance indicators, such as the shortest path length, path search time, number of turning points, total turning angle, and path smoothness, are used to evaluate energy consumption [5]. Obstacle avoidance path planning techniques have been extensively studied in two-dimensional environments [6, 7] to plan safe and collision-free paths in working environments with obstacles. These techniques have gradually been applied to automatic manipulators [8], ground robots [9], and other related fields. The rapidly-exploring random tree (RRT) [10] algorithm, based on uniform random sampling, is among the global path planning algorithms that have probabilistic completeness. However, its expansion efficiency is low, and it tends to fall into optimal local values. To address these limitations, a number of variants have been derived, such as RRT-connect [11], Q-RRT* [12], Informed RRT* [13], BIT* [14], etc. However, these algorithms do not consider pathsmoothness, resulting in a large number of turning points that increase energy consumption. To address this issue, many scholars have attempted to use intelligent algorithms to solve related problems. [PERSON] et al. [15] proposed an improved RRT algorithm that introduces ant colony optimization (ACO) in the process of expanding the random tree to make the planning path asymptotically optimal. [PERSON] et al. [16] introduced the metropolitan criterion into the ACO node screening mechanism to solve the multi-UAV trajectory planning problem in static environments. [PERSON] et al. [17] proposed a 3D spatial path planning algorithm that imitates basic mechanisms of plant growth, including phototropism, negative geotropism, and branching to achieve fast planning speed, few path nodes, and low-latency, real-time planning. [PERSON] et al. [18; 19] proposed the use of a priori maps to pre-plan alternative paths offline and select one of them for execution by online navigation. They later proposed a heuristic navigation method without a priori maps [20] that use an environment-based probabilistic representation to maximize the likelihood of successful navigation to a target. These algorithms improve the efficiency of path search and achieve asymptotic optimality of paths to some extent, but path smoothing is not well covered. Several researchers have applied neural network techniques to the field of path planning. Among them, [PERSON] et al. [21] proposed an efficient and locally connected Hopfield neural network (HNN) that dynamically tracks the numerical potential field of the HNN using a hill-climbing method to find collision-free paths without any unexpected local attraction points. [PERSON] et al. [22] proposed a novel competitive self-organizing neural network algorithm with new adversarial-based learning to generate better neurons in the initial stage. They followed it up with a secondary competitive layer on top of the hidden layer, thus improving the accuracy and convergence speed of the algorithm. However, path planning often needs to deal with complex and changing environments, and the neural network algorithm has poor generalization ability, making it less effective in such scenarios.