Methods for task allocation via agent coalition formation
Artificial Intelligence
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IEEE Transactions on Robotics
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MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
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KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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Information Sciences: an International Journal
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Proceedings of the 2013 Grand Challenges on Modeling and Simulation Conference
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Over the past few years, swarm based systems have emerged as an attractive paradigm for building large scale distributed systems composed of numerous independent but coordinating units. In our previous work, we have developed a protoype system called COMSTAR (Cooperative Multi-agent Systems for automatic TArget Recognition) using a swarm of unmanned aerial vehicles(UAVs) that is capable of identifying targets in software simulations of reconnaissance operations. Experimental results from the simulations of the COMSTAR system show that task selection among the UAVs is a crucial operation that determines the overall efficiency of the system. Previously described techniques for task selection among swarm units use a centralized server such as a ground control station to coordinate the activities of the swarm units. However, such systems are not truly distributed since the behavior of the swarm units is predominantly directed by the centralized server's task allocation algorithm. In this paper we focus on the problem of distributed task selection in a swarmed system where each swarm unit decides on the tasks it will execute by sharing information and coordinating its actions with other swarm units without the intervention of a centralized ground control station supervising its activities. Specifically, we build our task selection algorithm on an auction-based algorithm for task selection in robotic swarms described by Kalra et al. We report experimental results in a simulated environment with 18 robots and 20 tasks and compare the performance of our auction-based algorithm with other heuristic-based task selection strategies in multi-agent swarms. Our simulation results show that the auction-based algorithm improves the task completion times by 30-60% and reduces the communication overhead by as much as 90% with respect to other heuristic-based strategies maintaining similar performance in load balancing.