Correlation Clustering Based Coalition Formation For Multi-Robot Task Allocation

TL;DR

This paper presents a fast, near-optimal coalition formation algorithm for multi-robot task allocation using correlation clustering and graph partitioning, significantly improving solution quality and computational efficiency.

Contribution

It introduces a linear-programming based graph partitioning method combined with region growing for efficient coalition formation in multi-robot systems.

Findings

Algorithm finds solutions within 97.66% of optimal.

It is significantly faster than existing methods, taking only 230 seconds for 100 robots.

Solutions are closer to optimal by up to 9.1 times compared to worst-case bounds.

Abstract

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.