Swarming | Focus processes such as currents and waves, and acoustic propagation factors such as the speed of sound in the water. Processing of all of the required calculations can be completed remotely or onboard the ASVs. For example, ASVs deployed from a ship or flying boat can be controlled and guided remotely from land via a satellite link or from a nearby support ship using a satellite link. With an autonomous support chip, this swarm could map the seabed for weeks or months at a time until maintenance is needed. Sonar-return health checks and coarse seabed mapping would be conducted onboard, while full, high-resolution reconstruction of the seabed requires a supercomputing infrastructure on land. Deploying vehicles in an area and letting them map for extended periods of time without the need for a ship to return home to replenish supplies and rotate crews would simplify logistics and lower operating costs significantly. An 8 m array, containing multiple subarrays equivalent to 25 ASVs locked together, has been tested, generating 3D reconstructions of the seafloor and a shipwreck. Space The first swarm in orbit has been tested using only visual information shared through a wireless network. Robust navigation presents a considerable technological challenge. Current systems rely on the Global Navigation Satellite System (GNSS), requiring frequent contact with terrestrial systems. The Starling Formation-Flying Optical Experiment, or StarFOX, uses four small CubeSat satellites’ onboard star-tracker cameras to calculate their trajectories, so no extra hardware is needed to implement swarm capabilities. The field of known stars in the background is used as a reference to extract bearing angles to the swarming satellites. These angles are processed onboard through accurate, physicsbased force models to estimate the position and velocity of the satellites with respect to the orbited planet – in this case, Earth, but also the moon, Mars or other planetary objects. The swarm control system has three elements. An image-processing algorithm detects and tracks multiple targets in images and computes targetbearing angles – the angles at which objects, including space debris, are moving towards or away from each other. The Batch Orbit Determination algorithm then estimates each satellite’s coarse orbit from these angles. A Sequential Orbit Determination algorithm then refines swarm trajectories with the processing of new images through time to potentially feed autonomous guidance, control and collision avoidance algorithms onboard. Data is shared over an inter-satellite communications link to calculate robust absolute and relative position and velocity to an accuracy of 1.3% without GNSS using a single satellite. Using data from other satellites in the swarm, this accuracy increases to 0.5%. AI Many swarm coordination algorithms are possible with deterministic, rulebased logic, but recent developments in machine learning have enhanced autonomous navigation. AI algorithms help UAVs identify and avoid obstacles, move with autonomy, and make changes in real time based on their environment. A traditional command-and-control structure has agents organised in a hierarchy and detailed, tactical information is fed up the chain of command. While this hierarchical design simplifies data flow, it is not robust, and it is inflexible when dealing with dynamic scenarios that require rapid reactions from agents. Centralised control of a swarm requires a hub-and-spoke communication architecture, which presents several disadvantages: it limits the autonomous behaviour of the swarm, it does not enable communication between agents and it allows for a single point of failure in the design. An alternative is a distributed architecture where swarm decisions are made via collective consensus among agents. This type of architecture is robust and scalable, but requires a communication network that will support potentially greater data traffic. A hybrid of command-and-control architectures can be used to take advantage of the strengths of each one, using a distributed architecture for situational awareness data and an orchestrated architecture for selecting targets. Finite state machines (FSM) (or finite state automata) have been shown to be effective in modelling multivehicle autonomous, uncrewed system architectures. Within an FSM architecture, each agent operates within one of several defined states at a given time. The 47 Uncrewed Systems Technology | February/March 2025 A trial of a swarm of UAVs in a 3 x 3 grid using AI algorithms (Image courtesy of Thales)
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