Large Robotic Swarm Control via Random Finite Set Theory
Decentralized Robotic Swarm Control PolicyThis technique for controlling swarms of robotic units uses technical control algorithms based on Random Finite Set (RFS) theory. Using this method, each robotic device has a computing platform (i.e., an embedded processor) configured to apply the techniques to estimate local formation configuration and design a decentralized control policy, thus achieving a local, individualized configuration of each robotic device of the swarm. Using RFS theory for localizing and controlling swarming units and solving the control problem employs behavioral distribution and model predictive control to generalize the swarm state. The RFS-based approach localizes swarms through a Gaussian mixture Probability Hypothesis Density (PHD) filter, and then information divergence defines the distance between swarm RFS and a desired distribution. A stochastic optimal control problem is formulated using the information divergence, and it is solved through behavioral distribution and model predictive control.Low Cost Approach for Swarm Satellites, UAVs and RoversControlling large swarms of robotic units has many challenges, such as computational complexity due to the number of units, uncertainty in the functionality of each unit in the swarm, and limited knowledge of information of each unit. This random finite set-based approach addresses these challenges, and through optimal control, the swarm can optimally track a target density to a cost function. Decentralized approaches to controlling swarms through optimal transport and sequential convex programming create efficient trajectories for swarms including uncertainty, but they require computational power. This new approach considers the swarm units as a distribution through random finite sets and provides optimal trajectories—including uncertainty—without requiring computational power. This approach works best for low cost swarms swarm satellites, UAVs and rovers.BENEFITS AND FEATURES:Controls swarms of robotic unitsBased on Random Finite Set (RFS) theoryEstimates local formation configurationDesigns a decentralized control policyAchieves a local, individualized configuration of each robotic device of a swarmEmploys behavioral distribution and model predictive controlApplies a Gaussian mixture Probability Hypothesis Density filterInformation divergence defines the distance between swarm RFS and a desired distributionModified L22 distance formulates a stochastic optimal control problemAPPLICATIONS:Low cost swarm satellites, UAVS and roversPhase of Development - Proof of concept
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