Declarative vs Rule-based Control for Flocking Dynamics

TL;DR

This paper introduces a simple declarative flocking control law using a cost function for cohesion and separation, demonstrating superior performance and resilience compared to traditional models.

Contribution

It presents a novel declarative flocking model using model-predictive control, offering easier design and better performance than classic rule-based models.

Findings

DF-MPC achieves better cohesion and less fragmentation.

DF-MPC maintains natural flock shapes similar to Reynolds' model.

DF-MPC shows high resilience to sensor noise.

Abstract

The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as {\textit declarative flocking} (DF). We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our declarative flocking with Reynolds' model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.