Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation
In this paper, we study the problem of allocating multiple heterogeneous robots to tasks. Due to the limited capabilities of the robots, a task might need more than one robot to complete it. The fundamental problem of optimally partitioning the set of n robots into m disjoint coalitions for allocating to m tasks is proven to be NP-hard. To solve this computationally intractable problem, we propose a distributed hedonic game formulation, where each robot decides to join or not join a team based on the other robots allocated to that particular task. It uses a bipartite matching technique to establish an initial set of coalitions before letting the robots coordinate asynchronously and change teams if desired. Our proposed solution is proved to converge to a Nash-stable solution. Results show that our proposed approach is fast and handles asynchronous robot-to-robot communication while earning more utility (up to 23%) than an existing technique in the majority of the test cases.
IEEE International Conference on Automation Science and Engineering
Digital Object Identifier (DOI)
A. Dutta, V. Ufimtsev, T. Said, I. Jang and R. Eggen, "Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation," 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021, pp. 639-644, doi: 10.1109/CASE49439.2021.9551582.