  This paper presents an implementation of robust model predictive control
(MPC) for collision-free reference trajectory tracking for mobile robots. The
presented approach considers the robot motion to be subject to process noise
bounded by ellipsoidal sets. In order to efficiently handle the evolution of
the disturbance ellipsoids within the MPC, the zero-order robust optimization
(zoRO) scheme is applied. The idea is to fix the disturbance ellipsoids within
one optimization iteration and solve the problem repeatedly with updated
disturbance ellipsoid trajectories. The zero-order approach is suboptimal in
general. However, we show that it does not impair convergence to the reference
trajectory in the absence of obstacles. The experiments on an industrial mobile
robot prototype demonstrate the performance of the controller.
