Robot path planning based on the improved grey wolf optimization algorithm

Abstract

A modified grey wolf optimization algorithm for the path planning of mobile robots is proposed, for the slow convergence speed and high search path cost. Establish two-dimensional space model of mobile robot obstacle avoidance path planning, convert linear convergence factors in the grey wolf optimization algorithm into non-linear convergence factor; add collaborative quantum optimization of grey wolf population; and use four international test functions to prove that the improved algorithm is better in terms of convergence accuracy and stability. The improved algorithm was applied to the path planning of the mobile robot and compared with the original algorithm to verify the effectiveness of the algorithm.