Unmanned aerial vehicles (UAV) have the potential to be used for search and surveillance missions, and as munitions in the battlefield. The UAVs are deployed in swarms as they may not have sufficient computational, sensor, and operational capability to complete the task single-handedly. A desirable feature for these UAV swarms is the capability of intelligent autonomous decision making and coordination, with minimal or no centralized control. In this chapter, we present decentralized and distributed task allocation schemes based on concepts from team theory, game theory, and from negotiation techniques used in decision-making problems arising in economics, and apply these to design intelligent decision-making strategies for multiple UAV systems performing a wide area search and surveillance mission. We also address the task of searching an unknown environment, which is a major component in such missions, separately using game theoretical concepts.
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Sujit, P.B., Sinha, A., Ghose, D. (2007). Team, Game, and Negotiation based Intelligent Autonomous UAV Task Allocation for Wide Area Applications. In: Chahl, J.S., Jain, L.C., Mizutani, A., Sato-Ilic, M. (eds) Innovations in Intelligent Machines - 1. Studies in Computational Intelligence, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72696-8_3
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