Position-Based Flocking for Persistent Alignment without Velocity Sensing

TL;DR

This paper introduces a position-based flocking model that maintains persistent alignment in robotic swarms without relying on velocity sensing, using position changes and adaptive gain for sustained collective motion.

Contribution

The paper proposes a novel position-based flocking algorithm that achieves persistent velocity alignment without velocity measurements, suitable for real-world robotic applications.

Findings

Faster and more sustained directional alignment compared to velocity-based methods

More compact formations achieved in simulations

Successful implementation on real robotic robots

Abstract

Coordinated collective motion in bird flocks and fish schools inspires algorithms for cohesive swarm robotics. This paper presents a position-based flocking model that achieves persistent velocity alignment without velocity sensing. By approximating relative velocity differences from changes between current and initial relative positions and incorporating a time- and density-dependent alignment gain with a non-zero minimum threshold to maintain persistent alignment, the model sustains coherent collective motion over extended periods. Simulations with a collective of 50 agents demonstrate that the position-based flocking model attains faster and more sustained directional alignment and results in more compact formations than a velocity-alignment-based baseline. This position-based flocking model is particularly well-suited for real-world robotic swarms, where velocity measurements are unreliable, noisy, or unavailable. Experimental results using a team of nine real wheeled mobile robots are also presented.