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By Kruglyakov I.Z.

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Under the highest autonomy for mission management, predictions estimate operators could theoretically control (as an upper limit) four times as many, ∼17 vehicles. Previous air traffic control (ATC) studies have indicated that 16-17 aircraft are the upper limit for en route air traffic controllers [17]. Since controllers are only providing navigation assistance and not interacting with flight controls and mission sensors (such as imagery), the agreement between ATC en route controller capacities and low workload for UAV operators is not surprising.

The approach defines revenues and cost functions across the possible plans for executing a specified task. The task is accomplished by decomposing it into sub-tasks and allowing the robots to bid and negotiate to carry out these sub-tasks. Gerkey and Mataric [19] use an auction mechanism for multi-robot coordination while they analyze the communication and computational complexity involved in multi-robot task allocation in [20]. The authors categorize the task allocation methods based on the capability of the robots and the kind of tasks involved.

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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2-families of lines in P 3 and pairs of congruent lines in P 3 by Kruglyakov I.Z.


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