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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.

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Eyekon: Distributed Augmented Reality for Soldier Teams AbstractThe battlefield is a place of violence ruled by uncertainty. Timely knowledge of what's happening around a soldier can mean the difference between life and death. The goals of an enhanced mobile infantry are becoming a reality due, in part, to the U.S. Army's 21st Century Land Warrior (LW) program. However, the current system does not provide a "head up" display capability like that provided by today's avionics. When the soldier employs the weapon, he should see objects easily distinguishable as friendly or not, as well as enemy locations. The Eyekon project is an intelligent agent-based decision support system hosted on the soldier's wearable computer. Eyekon will use the soldier's private network to provide a perspective view in the weapon sight. This will naturally draw the warrior to the most desirable target. There are many performance and human factors issues to address before the concept can be used in lethal situations. If you load a mud foot down with a lot of gadgets that he has to watch, somebody a lot more simply equipped -say with a stone ax -will sneak up and bash his head in while he is trying to read a vernier."--Robert Heinlein, Starship Troopers

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