Home /Research /Coalition Formation for Multi-Robot Task Allocation via Correlation Clustering
SWARM

Coalition Formation for Multi-Robot Task Allocation via Correlation Clustering

Ayan Dutta, Vladimir Ufimtsev, Asai Asaithambi, Emily Czarnecki

Year
2019
Citations
17

Abstract

The complexity of a vast number of real world tasks provides a great challenge for the currently available robots due to their limited capabilities. Thus, multiple robots would need to form coalitions for the completion of such tasks. In this paper, we examine the multi-robot coalition formation problem for task allocation where a group of robots needs to be allocated to a set of tasks. Our approach for this problem is to use a correlation clustering technique enabling similar robots to form coalitions. The algorithm presented in this paper is fast and scales better in comparison to two existing algorithms.

Keywords

Computer scienceTask (project management)Cluster analysisRobotSet (abstract data type)Artificial intelligenceEngineering

Related papers

Browse all SWARM papers