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In this paper, we study the multi-robot task allocation problem where a groupof robots needs to be allocated to a set of tasks so that the tasks can befinished optimally. One task may need more than one robot to finish it.Therefore the robots need to form coalitions to complete these tasks.Multi-robot coalition formation for task allocation is a well-known NP-hardproblem. To solve this problem, we use a linear-programming based graphpartitioning approach along with a region growing strategy which allocates(near) optimal robot coalitions to tasks in a negligible amount of time. Ourproposed algorithm is fast (only taking 230 secs. for 100 robots and 10 tasks)and it also finds a near-optimal solution (up to 97.66% of the optimal). Wehave empirically demonstrated that the proposed approach in this paper alwaysfinds a solution which is closer (up to 9.1 times) to the optimal solution thana theoretical worst-case bound proved in an earlier work.

Correlation Clustering Based Coalition Formation For Multi-Robot Task Allocation