  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.
