Topology optimization for distributed consensus in multi-agent networks
Abstract: Distributed networks, meaning a network in which several agents work together unanimously to perform some task in order to reach goals has become a field with a wide range of applications. One such applications may exist in the form of drones with a purpose of observing and detecting forest fires. In such applications it can be of paramount importance to be able to agree over some opinions or values between the agents. This value could be something such as event detection or a general direction to fly in. However in such a network there might not exist a central hub and it would not be possible for all drones to communicate directly with each other. In order for such a network to be able to reach consensus or agreement, values have to be exchanged between the agents. This thesis focuses on a subset of this problem known as distributed averaging. In the thesis it is investigated how a networks ability to detect forest fires and communicate both efficiently and quickly can change when the number of agents are adjusted in the network. The results showed that, when operating in a fixed area, for a small network of drones the increasing effective energy cost per drone were higher, than that of a larger network. It was also discovered that the speed at which a network could reach an agreement was not necessarily affected by the size of the network. But as the field area being observed was increased, adverse effects were observed in terms of communication and event detection.
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