Correlation Clustering Based Coalition Formation For Multi-Robot Task Allocation
This addresses efficient task allocation for multi-robot systems, offering a fast and near-optimal solution, though it appears incremental as it builds on existing graph partitioning methods.
The paper tackles the NP-hard multi-robot coalition formation problem for task allocation by proposing a linear-programming based graph partitioning approach with region growing, achieving near-optimal solutions (up to 97.66% of optimal) in fast times (e.g., 230 seconds for 100 robots and 10 tasks).
In this paper, we study the multi-robot task allocation problem where a group of robots needs to be allocated to a set of tasks so that the tasks can be finished 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-hard problem. To solve this problem, we use a linear-programming based graph partitioning approach along with a region growing strategy which allocates (near) optimal robot coalitions to tasks in a negligible amount of time. Our proposed 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). We have empirically demonstrated that the proposed approach in this paper always finds a solution which is closer (up to 9.1 times) to the optimal solution than a theoretical worst-case bound proved in an earlier work.