Graph Neural Networks (GNN) for UxS Collaborative Agent Control

The purpose of this topic is to create an AI GNN framework for collaboration between swarming agents. Currently, collaboration is done using Laplacian matrices which can be used to find useful properties of a graph but it has to be hard coded and thus will be not be robust, since many lines of codes are need to program the behavior of each agent/graph. Current methods limit the behavior changes if a team member is added or lost. Since the hard coded method is used, when a new member is added, the user must account for it, which makes it harder to add new member, the same goes for losing a member, thus a new matrix will be needed to be added or the formation will not be robust. Having AI for each member will make the collaboration faster, more robust and will take the need for pre-determined behaviors. Communication, control and collaboration must be dynamic for large numbers of graph. Adjusting intelligently for unforeseen circumstances i.e adding/loosing members. Swarming for defensive and offensive fires will be able to utilize such Artificial Intelligence. This technology can also be utilized by Ballistic Low Drone Engagement (BLADE) and other C-UAS systems where they can communicate with each other and give suggestions to the user. C-swarming will also benefit from this research as it will give a testbed as to how the swarms of the future will look like and what it takes to counter them. If successful, the GNN will be easier to implement thus will make scaling up very easy and efficient thus will reduce the time it takes for the user to pre-program each new agent. It will also reduce the communication time between agents thus making it faster. If successful, having this assessed will also help in defending against future swarms.