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First, through in-depth analysis of the diversified collective behaviors in wolf pack, this study summarizes four remarkable features of wolf pack’s labor division. Second, the wolf pack’s role-task matching labor division mechanism is investigated, namely the individual wolves perform specific tasks that match their respective roles, and then a novel role matching labor division model is proposed. Finally, the performances of RMM are tested and evaluated with two swarm robotics task allocation scenarios. It is proved that RMM has higher solving efficiency and faster calculation speed for the concerned problem than the compared approach. Moreover, the proposed model shows advantages in the task allocation balance, robustness, and real time, especially in the dynamic response capability to the complex and changing environments.

Wolf Pack’s Role Matching Labor Division Model for Dynamic Task Allocation of Swarm Robotics