Research On Path Planning Algorithm Of Low Altitude UAV

With the development of aviation technology intelligence as well as information technology,UAVs have been widely popularized in military as well as civil fields.UAV path planning is one of the fundamental technologies that enable UAVs to accomplish their mission requirements.UAV path planning is one or more safe,collision-free paths from the starting position to the end position using one or more fusion algorithms,while meeting the constraints of the UAV’s own performance and environment.The result of path planning is an important factor in whether the mission can be completed effectively.Since the UAV flight environment and the flight scene change at a fast pace,it is necessary to choose a path planning algorithm that is suitable for the current environment,and this paper focuses on intelligent algorithms and non-intelligent path planning algorithms.First,a path planning model is established based on the UAV flight characteristics and the flight environment.This includes the UAV flight environment,performance constraints and path planning evaluation indexes.The environment model mainly includes ground buildings and no-fly zones,and the performance constraints mainly include UAV flight dynamics constraints such as maximum range,minimum turning radius and UAV height constraints.The evaluation indexes in UAV path planning mainly include path length cost,fuel consumption cost,obstacle threat cost and flight height cost.Secondly,to address the problems of too many redundant nodes,slow search efficiency and unsmooth generated paths of the A* algorithm,the variable weight A* algorithm is proposed,which mainly changes the heuristic function and cost function weights in the A*algorithm according to the current position of the UAV to improve the search efficiency of the algorithm.The redundant nodes are judged in the path set generated by the algorithm and the B-spline method is used to optimize the path set twice to generate a smooth and collision-free path.The analysis is carried out in the simulation environment to shorten the search time by74.10% compared with the A* algorithm and the path length by 12.39% compared with the A* algorithm,which proves the feasibility of the variable weight A* algorithm.Then,the A* potential field algorithm(A*-APF)is proposed for the problem that the artificial potential field algorithm easily falls into local optimum.The UAV flies along the improved A* algorithm and switches to the APF algorithm for dynamic obstacle avoidance if the UAV reaches the obstacle repulsive influence range,and then flies along the original path again when the UAV escapes the repulsive influence range.The algorithm aims to plan a safe flight path for the UAV in the presence of dynamic obstacles quickly and in real time.Simulation analysis of static and dynamic environments in a simulation environment shows that the improved APF algorithm reduces the path length by 4.32%,the planning time by25.00%,and the number of iterations by 20.35% compared to the traditional APF algorithm;the simulation experimental results of the fusion algorithm in an environment with dynamic obstacles prove the feasibility of the improved APF as well as the A* potential field algorithm.Finally,the virtual beetle antennae search(VBAS)algorithm is proposed for the problem that the antennae search algorithm is easy to fall into the local optimal solution and the path oscillation is not smooth.The algorithm generates virtual target points to guide the UAV to avoid obstacles when the UAV is about to touch the obstacles,determines whether there is oscillation in the current path during the iteration of the algorithm and removes the oscillating path,and optimizes the path twice using the B-spline method when the entire path set is generated.The simulation experiments show that the virtual aspen whisker search algorithm can effectively improve the path planning ability of UAV in different environments.