Improved Human Optimisation Algorithm for Global Path Planning in AGV

Aiming at the problems of slow convergence speed of human learning algorithm for optimum search, low accuracy of the optimization search, low efficiency of the path search, and lack of security in path planning, a diligent human learning optimal algorithm that integrates the particle swarm algorithm and the improved HLO algorithm is proposed. The improved algorithm is applied to global path planning, and simulation experiments prove the feasibility of the improved algorithm in AGV path planning, which has faster convergence speed and shorter planning path length than the traditional algorithm, and can effectively reduce the number of algorithm iterations.