To solve the path planning problem for unmanned aerial vehicles(UAVs)in three-dimensional environments,path planning methods through the sparrow search algorithm was investigated.Traditional sparrow search algorithms have issues such as easily falling into local optima and low convergence accuracy when solving this problem.To address these issues,an improved method was proposed.First,dynamic weight factors were added to the discoverers in the population to enhance their local search ability and increase convergence speed,while introducing Gaussian mutation.Followers used a quantum particle swarm approach to generate new solutions,and an additional Cauchy mutation was introduced for perturbation,with a smaller perturbation amplitude to enhance local search ability.Through simulation experiments,the improved algorithm was compared with the sparrow algorithm and other improved sparrow algorithms,showing that the improved algorithm has faster convergence speed and higher solution accuracy,proving the effectiveness and feasibility of the algorithm.This indicates that the algorithm has great potential for UAV three-dimensional path planning.
维普
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