Ant Colony Optimization based Simultaneous Task Allocation and Path Planning of Autonomous Vehicles

This paper applies a meta-heuristic based ant colony optimization (ACO) technique for simultaneous task allocation and path planning of automated guided vehicles (AGV) in material handling. ACO algorithm allocates tasks to AGVs based on collision free path obtained by a proposed path and motion planning algorithm. The validity of this approach is investigated by applying it to different task and AGV combinations which have different initial settings. For small combinations, i.e. small number of tasks and vehicles, the quality of the ACO solution is compared against the optimal results obtained from exhaustive search mechanism. This approach has shown near optimal results. For larger combinations, ACO solutions are compared with simulated annealing algorithm which is another commonly used meta-heuristic approach. The results show that ACO solutions have slightly better performance than that of simulated annealing algorithm