A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors

Unmanned aerial vehicles (UAVs) are the future technology
for autonomous fast transportation of individual goods.
They have the advantage of being small, fast and not to be
limited to the local infrastructure. This is not only
interesting for delivery of private consumption goods up to
the doorstep, but also particularly for smart factories.
One drawback of autonomous drone technology is the high
development costs, that limit research and development to a
small audience. This work is introducing a position control
with collision avoidance as a first step to make low-cost
drones more accessible to the execution of autonomous
tasks. The paper introduces a semilinear state-space model
for a commercial quadrotor and its adaptation to the
commercially available AR.Drone 2 system. The position
control introduced in this paper is a model predictive
control (MPC) based on a condensed multiple-shooting
continuation generalized minimal residual method
(CMSCGMRES). The collision avoidance is implemented in the
MPC based on a sigmoid function. The real-time
applicability of the proposed methods is demonstrated in
two experiments with a real AR.Drone quadrotor, adressing
position tracking and collision avoidance. The experiments
show the computational efficiency of the proposed control
design with a measured maximum computation time of less
than 2ms.