Research On Autonomous Path Planning Technology For Uav To Air Target

The autonomous task planning technology of the Unmanned aerial vehicle(UAV)in air combat is very important to avoid danger and complete the tasks.Autonomous mission planning technology runs through the entire process of drone operations.The path planning technology is one of the core technologies in the autonomous task planning of UAVs.It belongs to complex multiple objective optimization problems.It is necessary to consider various constraints and constraints,and there may be conflicts between different targets.The basis for realizing the simultaneous coordination of multiple UAVs in time and the prevention of collisions in space of multiple UAVs.This thesis analyzes and summarizes the research status of UAV path planning and the analysis of key issues of path planning technology.According to the future development trend and current problems,the following research has been done on the rapid autonomous path planning of an UAV and the cooperative route planning of multiple UAVs in a 3D environment in air combat:The existing path planning algorithm converges for a long time and low efficiency of the shortcomings in the implementation of complex engineering application scenarios.Based on the original rapidly exploring random-tree algorithm,modified rapidly exploring random-tree algorithm using real-time local extended node optimization and bilateral parallel computing is formed.In addition,based on the traditional particle swarm optimization algorithm,the particle swarm optimization algorithm is improved,retaining the individual's self-knowledge learning ability and maintaining individual diversity,while improving the accuracy of the solution and the search efficiency of the algorithm.Simulation experiments were performed on the real-time route planning of an UAV and multiple UAVs through two improved route planning algorithms.It is found that the new algorithm is better and faster in the case of multiple threats and multiple constrained optimization targets in a large rang 3D environment.And it has good stability in the solution process,and can quickly and reliably search and avoid all threat area.At the same time,the improved algorithm can have better adaptability and completeness to the threat of different random probabilities,and the overall engineering practicability has been significantly improved.