## 1 Introduction  
In today's logistics system, the \"last mile\" has become a distribution problem. As the volume of parcels continues to increase globally, so do the expectations of consumers, who want their packages delivered faster and at a lower cost. To meet this demand, logistics companies need to optimize their distribution routes and ensure that each link is executed accurately and efficiently [1]. On the one hand, during the recent coronavirus epidemic, in order to reduce the risk of virus transmission, some catering companies tried to use drones for food delivery, and this non-contact delivery mode was welcomed and recognized by the majority of consumers [2]. In addition, in the aftermath of earthquakes, floods, typhoons, and other disasters [3], the logistics system needs to have the ability to make a rapid response to ensure that emergency supplies are accurately delivered to the affected area in the shortest possible time. In this case, it is especially critical to efficiently plan a distribution path that is both safe and shortest. On the other hand, traditional logistics and distribution are still based on truck transportation, which is not only slow but also requires high labor costs. Meanwhile, with the improvement in social and economic level,the number of vehicles has increased dramatically, and modern cities are facing problems such as traffic congestion, air pollution, and noise [4]. Drones are an important tool for complex transportation problems due to their small size and flexibility [5]. Many logistics majors such as Google, DHL, and Amazon have long launched pilot programs in order to exploit the potential of drones in logistics applications [6]. Drones not only need to avoid obstacles such as urban buildings and mountainous terrain during flight, but must also consider factors such as airborne exclusion zones, maximum turning angles, and air resistance [7]. The need to improve the efficiency of drones due to their lower load capacity compared to ground vehicles [8]. The core of drone path planning is to provide a flight path that is as short and safe as possible, while at the same time needing to reduce the cost of flight, such as battery energy consumption and airframe wear and tear [9]. In general, path planning problems for UAVs can be categorized into three types: start-goal problems, target-coverage problems, and hybrid problems. The start-goal problem is concerned with planning a shortest path between a starting point and an end point; the goal-coverage problem aims to find an optimal path that completely covers a specified area; and the hybrid problem is a combination of both [10]. This paper explores the planning of a shortest flight path for drones that satisfies multiple constraints under the condition that the starting point and the target point are known. The path needs to meet the constraints of maximum yaw angle, maximum pitch angle, and maximum flight speed based on obstacle avoidance to achieve the feasibility and safety of the path. For the UAV path-planning problem, researchers have proposed a variety of algorithms. The classic algorithms include the Artificial Potential Field (APF) method [11], [PERSON]'s algorithm [12], A* algorithm [13], and Random-Number Rapid Search (RRT) algorithm [14]. However, the quality of paths planned by these methods tends to be poor when faced with complex environments. Therefore, some scholars have optimized these algorithms. [PERSON] et al. [15] proposed an improved Artificial Potential Field method, which effectively improves the feasibility and smoothness of the path by introducing risk assessment and virtual subgoal mechanism. [PERSON] et al. [16] used Mixed-Integer Linear Programming (MILP) to improve the A* algorithm, expanding the search range and improving the search success rate of UAVs. [PERSON] et al.