Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding

Shouvik Roy, Usama Mehmood, Radu Grosu, Scott A. Smolka, Scott D. Stoller, Ashish Tiwari

Abstract: We introduce the concept of Distributed Model Predictive Control (DMPC) with Acceleration-Weighted Neighborhooding (AWN) in order to synthesize a distributed and symmetric controller for high-speed flocking maneuvers (angu-lar turns in general). Acceleration-Weighted Neighborhooding exploits the imbalance in agent accelerations during a turning maneuver to ensure that actively turning agents are prioritized. We show that with our approach, a flocking maneuver can be achieved without it being a global objective. Only a small subset of the agents, called initiators, need to be aware of the maneuver objective. Our AWN-DMPC controller ensures this local information is propagated throughout the flock in a scale-free manner with linear delays. Our experimental evaluation conclusively demonstrates the maneuvering capabilities of a distributed flocking controller based on AWN-DMPC.