Towards Data-Driven Discovery of Governing Swarm Robots Flocking Rules

Extracting local interaction rules that govern the dynamics of a swarm is a central challenge in many swarm robotics application domains. Reverse engineer of such dynamics might be highly beneficial in preventing the serious design handcrafting errors that swarm robotics engineers may implicitly make. Advances in data-driven based systems identification techniques, called SINDy, are currently enabling the tractable identification of the equations governing the dynamics of many systems. However, they have not yet to be applied in swarm robotics systems. In this work, we aim to combine sparsity-promoting techniques with nonlinear swarm dynamical systems to develop a data-driven system identification model capable of discovering governing swarm flocking interaction rules from swarm measurement data. We particularly build and compare two SINDy flocking models: Flock-SINDy-STLSQ and Flock-SINDy-SR3. our findings suggest that the Flock-SINDy-SR3 discover better the underlying flocking dynamics rules than the Flock-SINDy-STLSQ and is expected to be further used as a controller implemented on real drones.