Position-Based Flocking for Persistent Alignment without Velocity Sensing
2026-02-25 • Robotics
Robotics
AI summaryⓘ
The authors created a new way for robot groups to move together like bird flocks or fish schools without needing to directly measure each other's speed. Instead, their method uses the changes in positions over time to guess how fast others are moving and adjusts accordingly. Their tests with 50 simulated robots showed this approach keeps the group moving in the same direction longer and in tighter formations than older methods that rely on velocity data. They also tried it on nine real robots to show it works in practice, especially when speed sensors are unreliable or missing.
collective motionflockingswarm roboticsvelocity alignmentposition-based controlalignment gainmobile robotsvelocity sensingswarm algorithmscohesion
Authors
Hossein B. Jond, Veli Bakırcıoğlu, Logan E. Beaver, Nejat Tükenmez, Adel Akbarimajd, Martin Saska
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.