  We present a novel, decentralized collision avoidance algorithm for
navigating a swarm of quadrotors in dense environments populated with static
and dynamic obstacles. Our algorithm relies on the concept of Optimal
Reciprocal CollisionAvoidance (ORCA) and utilizes a flatness-based Model
Predictive Control (MPC) to generate local collision-free trajectories for each
quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor
and subsequently use this linearized model in our MPC framework. Our method is
downwash conscious and computes safe trajectories that avoid quadrotors from
entering each other's downwash regions during close proximity maneuvers. In
addition, we account for the uncertainty in sensed position and velocity data
using Kalman filtering. We evaluate the performance of our algorithm with other
state-of-the-art methods and demonstrate its superior performance in terms of
smoothness of generated trajectories and lower probability of collision during
high velocity maneuvers.
