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We present a distributed control algorithm simultaneously solving both the stochastic target assignment and optimal motion control for large-scale swarms to achieve complex formation shapes. Our probabilistic swarm guidance using inhomogeneous Markov chains (PSG–IMC) algorithm adopts a Eulerian density-control framework, under which the physical space is partitioned into multiple bins and the swarm's...
We present a novel method for guiding a large-scale swarm of autonomous agents into a desired formation shape in a distributed and scalable manner. Our Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) algorithm adopts an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled. Each agent determines...
Probabilistic swarm guidance enables autonomous agents to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. In contrast with previous homogeneous or inhomogeneous Markov chain based approaches [1], this paper presents an optimal transport based approach which guarantees faster convergence, minimizes a given cost function, and...
In this paper, we integrate, implement, and validate formation flying algorithms for a large number of agents using probabilistic guidance of distributed systems with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each iteration in a statistically independent manner while...
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