  Swarms are highly robust systems that offer unique benefits compared to their
alternatives. In this work, we propose a bio-inspired and artificial potential
field-driven robot swarm control method, where the swarm formation dynamics are
modeled on the basis of Markov Random Field (MRF) optimization. We integrate
the internal agent-wise local interactions and external environmental
influences into the MRF. The optimized formation configurations at different
stages of the trajectory can be viewed as formation "shapes" which further
allows us to integrate dynamics-constrained motion control of the robots. We
show that this approach can be used to generate dynamically feasible
trajectories to navigate teams of aerial robots in complex environments.
