Three-dimensional path planning of UAV in complex urban environment

Abstract

Aiming at the three-dimensional path planning problem of UAV in complex urban environment, the improved grid method is used to simulate the flight environment, and the safety of path planning is improved by building a safe flight area and introducing a navigation safety cost function. In order to solve the problems of A* (A_Star) algorithm in path planning, such as large number of nodes, large amount of computation and low planning efficiency, we can reduce the redundant checking process in the path search process by expanding the line of sight strategy, improve the algorithm search efficiency and smooth the planned lines. Analyze the characteristics of each stage of path planning, adaptively adjust the weight factor, design the flight cost function, shorten the length of path planning, and improve the speed of path planning. As a result of the experiment, the improved A* algorithm clearly decreased over the time of the path planning, the number of nodes and the total cost of the flight, and it was clarified that the planned route was smoother and more feasible than the traditional algorithm. The performance of the improved algorithm has been fully verified under the complexity of different cities, which provides a reference for the research of UAV path planning in urban environment.

Research on UAV Swarm Network Modeling and Resilience Assessment Methods

“…In recent years, many scholars have carried out systematic research on UAV swarms and other multi-intelligence systems [ 4 , 5 ], and the research direction mainly focuses on UAV swarm topology design [ 6 , 7 , 8 ], task assignment [ 9 , 10 , 11 ], path planning [ 12 , 13 , 14 , 15 , 16 ], cooperative control [ 17 , 18 , 19 ], and planning scheduling [ 20 , 21 ]. However, most of these studies assume that the UAV swarms are in a disturbance-free situation and do not consider the performance of UAV swarms in abnormal flight conditions.…”