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.

翻译：设计一种能够以最优分布式方式自主跟踪变化模式或形状的机器人集群控制方法既充满前景又具有挑战性。因此，本文研究了具有动态模式形成的最优集群控制问题。提出了一种基于吉布斯随机场(GRF)的预测性集群控制算法，其中采用仿生势能来表征"机器人-机器人"和"机器人-环境"相互作用。为优化集群行为，在所提出的设计中引入了与性能相关的专用势能项（如运动平滑性）。最优控制通过最大化吉布斯随机场的后验分布实现。基于均值漂移技术，实现了区域化形状控制以完成模式形成。通过与两种现有先进集群控制方法在障碍环境中的对比实验验证了所提算法的有效性。通过数值仿真和实际实验共同证明了所提设计的高效性。