Path Planning of lawn Mowing Robot Based on Improved Grey Wolf Algorithm
To solve the problems of traditional grey wolf algorithm(GWO)being prone to local optima,slow convergence speed,high iteration times,and low weed removal efficiency when used for lawn trimming operations in full coverage path planning,a heuristic chaos operator grey wolf optimization algorithm(CGWO)is proposed.Based on the tent chaotic mapping,the CGWO is established by an adaptive parameter adjustment strategy in order to adjust the acceleration factor and various control parameters.This strategy enhances randomness in the search process,aiding the algorithm in escaping local optima and improving global search capability.Through simulation analysis,it was found that path cost,iteration times and time consumption of the CGWO algorithm is less than the GWO and particle swarm optimization(PSO)algorithms.Additionally,the generated path is smoother.Real vehicle experiments conducted in three types of lawn environments demonstrate that the CGWO algorithm is more effective than GWO and PSO algorithms.
intelligent lawn trimming robotpath planninggrey wolf optimization algorithmtent chaotic mapping
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