The distributed flocking control of collective aerial vehicles has extraordinary advantages in scalability and reliability, \emph{etc.} However, it is still challenging to design a reliable, efficient, and responsive flocking algorithm. In this paper, a distributed predictive flocking framework is presented based on a Markov random field (MRF). The MRF is used to characterize the optimization problem that is eventually resolved by discretizing the input space. Potential functions are employed to describe the interactions between aerial vehicles and as indicators of flight performance. The dynamic constraints are taken into account in the candidate feasible trajectories which correspond to random variables. Numerical simulation shows that compared with some existing latest methods, the proposed algorithm has better-flocking cohesion and control efficiency performances. Experiments are also conducted to demonstrate the feasibility of the proposed algorithm.

翻译：集群飞行器的分布式集群控制在可扩展性和可靠性等方面具有显著优势。然而，设计可靠、高效且响应迅速的集群算法仍具挑战性。本文提出了一种基于马尔可夫随机场的分布式预测集群框架。该框架利用马尔可夫随机场表征优化问题，最终通过对输入空间进行离散化求解。采用势函数描述飞行器间的相互作用并作为飞行性能的指标。动态约束被纳入对应随机变量的候选可行轨迹中。数值仿真表明，与现有最新方法相比，所提算法具有更好的集群凝聚力和控制效率性能。同时通过实验验证了所提算法的可行性。