GRFに基づく動的パターン形成を伴う予測型群制御
GRF-based Predictive Flocking Control with Dynamic Pattern Formation
ギブス確率場(GRF)を用いてロボット群の予測型群制御を設計し、平均シフト法による領域形状制御で動的にパターンを形成する手法を提案した。シミュレーションと実機実験で有効性を示した。
著者: Chenghao Yu, Dengyu Zhang, Qingrui Zhang
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
