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FnS - Managing Distributed Situation Awareness in a Team of Agents Tasked to Conduct Search Activity

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This project describes how distributed agents operating under strict constraints can update a system level Situation Awareness (a process of collecting, understanding, and predicting system information). The constraints are limited energy (battery), computational power, dynamic environment, and role variation. The problem is addressed by developing (i) a novel way of coordinating agents’ information collection process using geometrically derived path generation algorithms and (ii) a better information presentation technique using Bayesian Belief Network (BBN). Algorithms and methods were developed to describe an effective BBN update process (in terms of both parametric and structure features), BBN training to support situations prediction and uncertainty handling. The evaluation process was described using a team of Unmanned Aerial vehicles (UAVs), either simulated or physical tasked to search forest fires. Results show better performance over the existing methods in terms of agents’ coordination (compared against popular fixed-pattern and pseudorandom approaches) and SA management (compared against propositional networks, ontology, and concept maps). Finally, the developed agents’ coordination algorithms demonstrate easy application on real UAVs (drones) as tested on a team of UAVs tasked to search one of the Birmingham’s parks. So, even if you are not interested in the algorithms and methods, at least you will learn how to fly and coordinate multiple drones for your summer fun time.

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