# GRF-based Predictive Flocking Control with Dynamic Pattern Formation

arXiv: https://arxiv.org/abs/2403.08434 · HTML: https://mlsanity.com/paper/2403.08434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Learning Efficient Flocking Control based on Gibbs Random Fields](https://mlsanity.com/paper/2502.02984/) (2025)

## Abstract (arXiv)

It is promising but challenging to design flocking control for a robot swarm to autonomously follow changing patterns or shapes in a optimal distributed manner. The optimal flocking control with dynamic pattern formation is, therefore, investigated in this paper. A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where bio-inspired potential energies are used to charaterize ``robot-robot'' and ``robot-environment'' interactions. Specialized performance-related energies, e.g., motion smoothness, are introduced in the proposed design to improve the flocking behaviors. The optimal control is obtained by maximizing a posterior distribution of a GRF. A region-based shape control is accomplished for pattern formation in light of a mean shift technique. The proposed algorithm is evaluated via the comparison with two state-of-the-art flocking control methods in an environment with obstacles. Both numerical simulations and real-world experiments are conducted to demonstrate the efficiency of the proposed design.