Multi-Robot Task Allocation Based on Swarm Intelligence

Abstract

The task allocation was studied based on the swarm inteeligence for the large-scale multi-robot system with loose-and tight-coupled tasks adopting the hierarchial architecture. In the high level,the ant colony algorithm was employed to find the optimal allocation of the loose-coupled tasks,namely,based on the reverse distribution idea,taking each ant to form a task,an undertaker was chosen for every task. In the low level,the coalition formation algorithms based on the ant colony optimization(ACO) ,the particle swarm and ant colomy optimization(PSACO) ,or the quantum-inspised ant colony optimization(QACO) was proposed respectively for performin a tight-coupled task.Simulations were performed and results showed that PSACO provides the best solution,but its running time is the largest;QACO is a little inferior in solution quality to PSACO,however,it needs only a half time of the 2 other methods. Therefore,QACO appears more suitable for the task allocation of the large-scale multi-robot system.