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.