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Multi-Robot Task Allocation is a crucial issue before performing a certain task. This paper deals with a distributed task allocation method based on some special relation defined according to the performance of history cooperation between two robots. The algorithm we propose here is named TARARC—a Task Allocation algorithm based on Robot Ability and Relevance with group Collaboration, where robot ability is weighed by reliability, relevance represents a fresh concept of "history relevance" between every two robots to establish reasonable groups for better collaboration, and the group collaboration includes inter and inner group help strategy that are adopted when different nodes failures happen in unknown environment. TARARC emphasizes the role of "agent node" in each group that is responsible for task competition, group leadership, formation maintenance as well as task execution with changing agents. Simulation on Player/Stage shows that our mechanism is feasible and valid.

A Multi-Robots Task Allocation Algorithm Based on Relevance and Ability With Group Collaboration