48 Focus | Swarming trigger events that cause the agent to transition between states are precipitated by environmental conditions it senses or events it encounters. This type of structure is applicable in developing swarm systems as the states and triggers can be defined deterministically, which is necessary for high-risk mission events. There may be other events, such as searching, where some bounded degree of unpredictability is desired. In those cases, probabilistic FSM can be used by allowing for different behaviour within a state. AI has been key to the first flight-test swarms of UAVs with different levels of autonomy. Using AI and intelligent agents reduces the cognitive load on operators yet ensures they remain in control, particularly during critical mission phases. The control logic for the swarm can use a rule-based system with predictability and determinism, or with an AI framework. If the AI framework is not able to be deterministic, the autopilot has to be able to sidestep the mission-control computer when it has an issue. This requires additional logic that monitors the MCS and on encountering a problem decides whether to go into safe mode to segregate the AI. Generative AI, such as large language models (LLMs), are used for the human-to-machine interface, giving an operator easier ways to define what they want to do with large numbers of UAVs in a swarm. Flight tests with nine UAVs connected via 4G cellular have shown that AI can support swarms by allowing operators to adapt the level of autonomy of the vehicles to the operational requirements of each phase of the mission, which also reduces the load on the wireless network. The UAVs are capable of perceiving and analysing their local environment, sharing target information, analysing enemy intent and prioritising missions. They can also use collaborative tactics and optimise their trajectories to increase resilience and boost force effectiveness. These use specialised machine-learning frameworks developed specifically for swarm applications. These are optimised for analysis of the data generated by sensors and decision-support pathways for mixed UAV swarms, while addressing the specific cybersecurity, embeddability and system optimisations for smaller aircraft with constrained resources. All this is driving development of swarm operation in the air, on land and at sea to implement more resilient operations with different types of uncrewed systems. Acknowledgements With thanks to Iker Camiruaga and Ignacio Calomarde at UAV Navigation, Andrew March at MIT and Nils Thorjussen and Tony Samaritano at Verge Aero. February/March 2025 | Uncrewed Systems Technology FRANCE Thales +1 646 664 0659 www.thales.com SPAIN UAV Navigation +34 91 657 2723 www.uavnavigation.com UK Hadean +44 203 514 1170 www.hadean.com Saab/Blue Bear Systems Research +44 1234 826 620 www.saab.com US Applied Aeronautics +1 512 961 1771 www.appliedaeronautics.com Honeywell +1 480 353 3020 www.honeywell.com Saronic Technologies - www.saronic.com Swarm Aero - www.swarmaero.com Verge Aero +1 425 556 2900 www.vergeaero.com Some examples of swarm suppliers Using AI and intelligent agents [cuts] the cognitive load on operators yet ensures they remain in control, particularly during critical mission phases
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