Correlation clustering based coalition formation for multi-robot task allocation

Abstract

In this paper, we study the multi-robot task allocation problem where a group\nof robots needs to be allocated to a set of tasks so that the tasks can be\nfinished optimally. One task may need more than one robot to finish it.\nTherefore the robots need to form coalitions to complete these tasks.\nMulti-robot coalition formation for task allocation is a well-known NP-hard\nproblem. To solve this problem, we use a linear-programming based graph\npartitioning approach along with a region growing strategy which allocates\n(near) optimal robot coalitions to tasks in a negligible amount of time. Our\nproposed algorithm is fast (only taking 230 secs. for 100 robots and 10 tasks)\nand it also finds a near-optimal solution (up to 97.66% of the optimal). We\nhave empirically demonstrated that the proposed approach in this paper always\nfinds a solution which is closer (up to 9.1 times) to the optimal solution than\na theoretical worst-case bound proved in an earlier work.\n