Cooperative Task Allocation for Multirobot Systems Based on Multi-objective Ant Colony System

This paper proposes a novel multi-objective ant colony system (MOACS) approach to solve the cooperative task allocation problem of multi-robot systems. The is formulated as multiple traveling salesman (MTSP). objectives are minimize total and maximum cost robotic vehicles so that workload each vehicle could be balanced. time matrices salesmen different asymmetric due flight speeds executing tasks. Based on single-objective (ACS), solution construction method pheromone update rule proposed. At step in phase, with minimum has biggest chance add an unassigned balance vehicle, while also bigger than any other ants find better Pareto front. value limited which helpful avoiding fast convergence local optima. Extensive simulation results suggest proposed MOACS performance effectiveness existing non-dominated sorting genetic algorithm II (NSGA-II) particle swarm optimization (MOPSO). Hardware-in-the-loop experiments unmanned aerial (UAVs) show compared NSGA-II MOPSO, distance UAVs decreased by up 28.46% 26.34%, respectively, used finish all tasks 23.86% 17.94%.