Online Flocking Control of UAVs with Mean-Field Approximation
It addresses the challenge of cooperative aerial robot control for applications like surveillance or delivery, but is incremental as it builds on existing mean-field approximation techniques.
This work tackled the problem of distributed flocking control for UAV swarms by developing an inference-based method that ensures feasible control inputs and smooth behavior, achieving formation and velocity consensus with real-time collision avoidance in physical and simulation experiments.
This work presents a novel, inference-based approach to the distributed and cooperative flocking control of aerial robot swarms. The proposed method stems from the Unmanned Aerial Vehicle (UAV) dynamics by limiting the latent set to the robots' feasible action space, thus preventing any unattainable control inputs from being produced and leading to smooth flocking behavior. By modeling the inter-agent relationships using a pairwise energy function, we show that interacting robot swarms constitute a Markov Random Field. Our algorithm builds on the Mean-Field Approximation and incorporates the collective behavioral rules: cohesion, separation, and velocity alignment. We follow a distributed control scheme and show that our method can control a swarm of UAVs to a formation and velocity consensus with real-time collision avoidance. We validate the proposed method with physical UAVs and high-fidelity simulation experiments.