Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments

Autonomous navigation in unknown environments
populated by humans and other robots is one of the main
challenges when working with mobile robots. In this paper,
we present a new approach to dynamic collision avoidance for
multi-rotor unmanned aerial vehicles (UAVs). A new nonlinear
model predictive control (NMPC) approach is proposed to
safely navigate in a workspace populated by static and/or
moving obstacles. The uniqueness of our approach lies in
its ability to anticipate the dynamics of multiple obstacles,
avoiding them in real-time. Exploiting active set algorithms,
only the obstacles that affect to the UAV during the prediction
horizon are considered at each sample time. We also improve
the fluency of avoidance maneuvers by reformulating the
obstacles as orientable ellipsoids, being less prone to local
minima and allowing the definition of a preferred avoidance
direction. Finally, we present two real-time implementations
based on simulation. The former demonstrates that our
approach outperforms its analog static formulation in terms of
safety and efficiency. The latter shows its capability to avoid
multiple dynamic obstacles.