UAV 3D path planning based on improved particle swarm optimization algorithm
In order to solve the problem that the traditional particle swarm optimization algorithm has strong dependence on parameters and is easy to fall into local optimization,chaotic mapping,improved iteration formula and Cauchy mutation operator are introduced to improve the performance of the algorithm.Firstly,establish the three-dimensional operating space of UAV.Secondly,considering ground and air suspension obstacles factors,the fitness function is constructed.In the process of algorithm iteration,Logistic chaotic mapping with interval constraint is introduced to enhance the randomness of the initial particle distribution,and the global search ability and local search ability of the nonlinear iterative formula balance algorithm are improved.Then Cauchy mutation operator is introduced to avoid the algorithm falling into local optimality.Finally,a large number of experimental results show that compared with the three typical path planning algorithms,the improved particle swarm optimization algorithm has a strong ability to jump out of the local optimal and has a stable optimization ability.
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