Research On UAV Path Planning Algorithm

With the advent of the intelligent age,drones are gradually becoming popular,and more and more industries are beginning to use drones for operations.How the UAV completes the task efficiently depends on the path planning system.UAV path planning refers to the design to ensure that the UAV completes a specific flight task and avoids various obstacles and threatened areas in the process of completing the task.The process of optimal trajectory route.Studying the problem of UAV trajectory planning can shorten the flight distance of the UAV as much as possible on the premise of ensuring safety in flight.Therefore,the research prospects of UAV path trajectory are broad and of great significance.In view of the research content,this article mainly carried out the following work content:(1)Model the trajectory planning environment and obstacles,and explain the threat model when the UAV performs missions.Introduces the algorithms commonly used in UAV trajectory planning,including the basic principles and algorithm steps of genetic algorithm,neural network algorithm,particle swarm algorithm,and artificial potential field method,and analyzes and summarizes their respective advantages and disadvantages.(2)According to the principle of the heuristic A~* algorithm,the A~* algorithm is simulated on Matlab.First,the environment is modeled according to the known information such as the starting point,target point and obstacles,and then the A~*algorithm is based on the algorithm principle.The nodes that meet the constraints are placed in the OPEN list,otherwise,they are placed in the CLOSE list.Finally,the nodes in the OPEN list are connected in sequence to obtain the planned path.In view of the long path length at the turning corners of the obtained planned path,the path does not reach the global optimum,so the algorithm is improved,and the tangent arc is introduced under the condition of the kinematics constraints of the UAV,and the experimental simulation is performed.The simulation results show that the improvement The length of the back path is relatively shortened.Prove the effectiveness of the improved algorithm.(3)The principle and process of the RRT algorithm are simulated,and the RRT algorithm is time-consuming and inefficient to find the path.The RRT-Connect idea is introduced to obtain the planned path,and then the algorithm RRT-Connect is improved,and the improved algorithm is carried out.The simulation results show that the path length after the improved algorithm is shorter than that obtained by the RRT-Connect algorithm.The basic ideas and key parameters of the ant colony algorithm planning path are explained,verifying the effectiveness of the ant colony algorithm in the search path,and the traditional ant colony algorithm is easy to fall into the local optimal problem early in the path planning.Drawing lessons from the "pawn" rule in chess,the improved track is obtained by setting the state parameter equation,and the improved track is re-planned,so that the global optimal track planning path can be achieved.The simulation results show that the improved and re-planned ant colony algorithm can achieve the purpose of shortening the track length.