Robot path planning based on improved sparrow algorithm

Abstract

Abstract Robot path planning is currently a research hotspot. To overcome the shortcomings of the traditional raster method, an improved sparrow search algorithm is proposed for path planning in the raster. On the basis of the new sparrow search algorithm, the K-medoids clustering method is introduced to classify the population dynamically and accelerate the information exchange among the population. Then, an adaptive weight factor is introduced to make the algorithm search more detailed and extensive, which enhances the optimization ability of the algorithm. The optimized routes of each algorithm on the raster graph show that the improved sparrow search algorithm can plan a simpler route with the least cost.