Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding

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 imb...

Full description

Saved in:
Bibliographic Details
Main Authors: Roy, Shouvik, Mehmood, Usama, Grosu, Radu, Smolka, Scott A., Stoller, Scott D., Tiwari, Ashish
Format: Conference Proceeding
Language:English
Subjects:
Accelerated aging
Collision avoidance
Decentralized control
Delays
Model checking
Scalability
Turning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2378-5861
DOI:10.23919/ACC50511.2021.9483155