  We propose a model predictive control (MPC) based approach to a flock control
problem with obstacle avoidance capability in a leader-follower framework,
utilizing the future trajectory prediction computed by each agent. We employ
the traditional Reynolds' flocking rules (cohesion, separation, and alignment)
as a basis, and tailor the model to fit a navigation (as opposed to formation)
purpose. In particular, we introduce several concepts such as the credibility
and the importance of the gathered information from neighbors, and dynamic
trade-offs between references. They are based on the observations that
near-future predictions are more reliable, agents closer to leaders are
implicit carriers of more educated information, and the predominance of either
cohesion or alignment is dictated by the distance between the agent and its
neighbors. These features are incorporated in the MPC formulation, and their
advantages are discussed through numerical simulations.
