Self-Adaptive Distributed Multi-Task Allocation in a Multi-Robot System

Some common issues exist in the bio-inspired algorithms for a multi-robot system include considerable randomness of the robot movement during coordination and unevenly distributed robots in a multi-task environment. To address these issues, a self-adaptive distributed multi-task allocation method in a multi-robot system is proposed in this paper. In this method, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a balance between the exploration and exploitation inspired from particle swarm optimization (PSO) method. To further reduce the random movement, a new task utility function is developed, where not only the current available task weight and the travel cost are considered, but also the potential number of robot redundancy around the task, as well as the task/robot distribution ratio. The proposed algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching task. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.