Research On Path Panning Based On UAV Formation

With the rapid development of artificial intelligence technology,it has become the research focus of how to use the intelligent agent to change the traditional way of life and production.UAV formation is often used for doing rescue and relief work due to its high efficiency,wide range of observations and high reliability.Therefore,UAV formation and related technologies will be more and more popular and will be got applied and concerned increasingly all over the world in the future.As the,The research hotspot has changed from the primary use of the UAV formation to use the UAV formation to perform various tasks better with the development gradually of the UAV formation technology.In this paper,it introduces the related technologies of UAV formation and analyzes several common path planning algorithms and improved algorithms in detail.The A* algorithm is the most direct and effective heuristic search algorithm for solving the shortest path when the task space environment information is known,so it is often used as a static path planning algorithm.The research on A* algorithm shows that the search efficiency is low and the factors are considered relatively simple in the function design when it is used in lager complex space,so the results are not in line with the actual situation.Thus,an improved A* algorithm is proposed for solving the disadvantages of A* algorithm in this paper.At first,the new algorithm improves the heuristic function and the actual cost function,and then it fuses the bi-directional search strategies.Finally it adopts the key point selection strategy to deal with the redundant nodes of the planning path.The improved A* algorithm not only improves the search efficiency of the algorithm,but also improves the smoothness of the planning path to some extent.The artificial potential field method is a kind of virtual force methods.This method is able to plan a smooth path when the environmental information is dynamic.Therefore,it is often used as a dynamic path planning algorithm.However,this method still has some shorts,such as local minimum,unreachable target and local oscillation and so on.At the same time,the paper also proposes some resolutions to solve the above problems.Firstly,the target unreachable problem is solved by changing the gain coefficient of the repulsive field or the gravitational field.Secondly,the gravitational potential function and the repulsion potential function are helpful to improve the local minimum point problem.Thirdly,it is also proposes an improved method of dynamically changing the repulsive force and gravitational force to solve the local oscillation problem.In a word,the improved artificial potential field algorithm effectively solves these inherent problems of traditional algorithms.Finally,the A* algorithm and the artificial potential field method are improved by step-by-step thinking and are combined to form a hybrid path planning algorithm and then to use the MATLAB simulation software simulates and analyzes the improved algorithm.The final results of the simulation show that the new algorithm can obtain a kind of ideal planning path in a complex task environment and the performance is greatly improved compared with the single algorithm.