Research On Path Planning Algorithm Of Mobile Agent In Three-dimensional Environment

With the development of artificial intelligence technology,mobile agent plays an increasingly important role in human life.The path planning algorithm of mobile agent has become the focus of mobile agent research and application.This paper mainly studies the related problems of path planning in the known and unknown threedimensional environment.In the known three-dimensional environment,there are the problems of large amount of calculation and poor real-time performance in traversing the concave obstacle area about the A* algorithm.To solve this problem,this paper first proposes a non optimal path grid point processing method.According to whether the starting point of the planning is in the obstacle or not,the decision strategies of different non optimal path grid points in concave obstacle environment are given.This strategy effectively reduces the search scope,thus reducing the amount of computation.Secondly,the A* algorithm is improved.According to the position relationship between the current planning path point and the end point,the point set of the minimum heuristic value point is obtained,and the search range of the A* algorithm is reduced to the point set,so as to further improve the planning efficiency.The simulation results show that the proposed algorithm not only plans the shortest path in different obstacle environments,but also reduces the running time and improves the efficiency of the algorithm.In unknown three-dimensional environment,due to the complexity and uncertainty of the environment,it is impossible to plan the global shortest path at one time.In order to solve this problem,based on the segmented planning strategy,this paper establishes the perception model of mobile agent in the unknown three-dimensional environment,and uses the heuristic mapping method to calculate the sub-target points of segmented planning,combined with the A* algorithm in the unknown three-dimensional environment to plan the shortest path.The simulation results show that the method is convenient for modeling and less computation,overcomes the defect that the linear mapping method is difficult to determine the sub-target in a certain environment,and obtains the shortest path.