AGV path planning based on improved ant colony optimization algorithm
[Objective]Aiming at the problems of low search efficiency,long search path,and multiple inflection points in traditional ant colony algorithm(ACA)for automatic guided vehicle(AGV)path planning,an improved ant colony optimization(ACO)algorithm was proposed.[Method]First,an estimated surrogate value strategy was incorporated into the ant colony algorithm to improve the heuristic function,enhance the guiding effect of the target point,and boost search efficiency;then,the wolf pack algorithm(WPA)allocation mechanism was combined to update pheromones,solving the problem of easily falling into local optima during path planning,and adding inflection point influence factors to reduce path inflection points;finally,a dynamic obstacle avoidance strategy was adopted to solve the deadlock problem.[Result]After applying the improved ant colony optimization algorithm,the optimal path length,the number of iterations and the number of inflections in AGV path planning are reduced by 9.7％,57.8％,and 65.0％,respectively,compared to traditional algorithms,[Conclusion]This study provides important references for AGV to choose paths under complex environmental conditions.
ant colony optimizationsearch efficiencypheromonedeadlockAGV
万方数据
维普
