Position-Based Flocking for Persistent Alignment without Velocity Sensing

2026-02-25

AI Summary

Achieving persistent velocity alignment and coordinated motion in swarm robotics without explicit velocity sensing remains challenging. This work proposes a position-only flocking control method that implicitly estimates relative velocities from changes in current versus initial relative positions. A dynamic alignment gain mechanism is introduced, featuring a non-zero minimum threshold that adaptively adjusts based on time and local agent density. This approach ensures long-term stable swarm alignment without requiring velocity sensors. Simulations with 50 agents demonstrate faster convergence to directional consensus and maintenance of more compact formations. Furthermore, deployment on a physical platform of nine wheeled robots validates the method’s effectiveness and robustness in real-world conditions.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• flocking
• velocity alignment
• position-based control
• swarm robotics
• collective motion

Innovation

• position-based flocking
• velocity-free alignment
• swarm robotics
• collective motion
• density-dependent gain