  Scalable multi-robot transition is essential for ubiquitous adoption of
robots. As a step towards it, a computationally efficient decentralized
algorithm for continuous-time trajectory optimization in multi-robot scenarios
based upon model predictive control is introduced. The robots communicate only
their current states and goals rather than sharing their whole trajectory;
using this data each robot predicts a continuous-time trajectory for every
other robot exploiting optimal control based motion primitives that are
corrected for spatial inter-robot interactions using least squares. A non
linear program (NLP) is formulated for collision avoidance with the predicted
trajectories of other robots. The NLP is condensed by using time as a
parametrization resulting in an unconstrained optimization problem and can be
solved in a fast and efficient manner. Additionally, the algorithm resizes the
robot to accommodate it's trajectory tracking error. The algorithm was tested
in simulations on Gazebo with aerial robots. Early results indicate that the
proposed algorithm is efficient for upto forty homogeneous robots and twenty
one heterogeneous robots occupying 20\% of the available space.
