Position-Based Flocking for Robust Alignment

TL;DR

This paper introduces a position-based flocking model that enhances robustness and formation stability in collective agent motion by approximating velocities through positions, outperforming traditional velocity-based models in simulations.

Contribution

The paper proposes a novel position-based flocking model that improves alignment and formation stability over existing velocity-based approaches.

Findings

Stronger alignment achieved with the position-based model

More rigid and compact formations in simulations

Enhanced robustness in collective motion behaviors

Abstract

This paper presents a position-based flocking model for interacting agents, balancing cohesion-separation and alignment to achieve stable collective motion. The model modifies a position-velocity-based approach by approximating velocity differences using initial and current positions, introducing a threshold weight to ensure sustained alignment. Simulations with 50 agents in 2D demonstrate that the position-based model produces stronger alignment and more rigid and compact formations compared to the position-velocity-based model. The alignment metric and separation distances highlight the efficacy of the proposed model in achieving robust flocking behavior. The model's use of positions ensures robust alignment, with applications in robotics and collective dynamics.