GRF-based Predictive Flocking Control with Dynamic Pattern Formation
<|reference_start|>GRF-based Predictive Flocking Control with Dynamic Pattern Formation: 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.<|reference_end|>
@article{yu2024grf-based,
    title={GRF-based Predictive Flocking Control with Dynamic Pattern Formation},
    author={Chenghao Yu, Dengyu Zhang, and Qingrui Zhang},
    journal={arXiv preprint arXiv:2403.08434},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08434},
    primaryClass={cs.RO cs.SY eess.SY}
}