Path Planning of Wheeled Robot with Improved Sparrow Search Algorithm
Aiming at the problems such as the traditional sparrow search algorithm is easy to fall into the local optimal in the path planning application of wheeled robot,which leads to the long planned path and the algorithm is easy to fall into the precocity in the later stage,an improved sparrow search algorithm is proposed for the path planning of wheeled robot.Firstly,Logistic chaos is used to improve the diversity of initial population when initializing the initial population.Secondly,the linear dynamic inertial weight adjust-ment method is introduced into the discoverer position update,which improves the global search ability and convergence speed of the algorithm.Then,the midvertical algorithm is combined with the follower position updating method to make the follower get closer to the individual with the highest fitness of population quickly and accurately.Finally,in the late stage of the algorithm,the optimal explosive particle strategy and reverse learning strategy are combined to generate disturbances near the optimal solution,so as to prevent the algorithm from falling into the local optimal solution.In the application of robot path planning,the glob-al optimal solution is searched locally again to improve the ability of robot path planning.The simulation re-sults show that ISSA application in path planning has significantly improved the path length,optimization speed and iteration times.
path planningLogistic chaosmidvertical algorithmexplosive particlesreverse learningglob-al optimal solution local
万方数据
维普
