Flatness-based model predictive trajectory optimization for inspection tasks of multirotors

In industrial surface inspection, unmanned mul-tirotors are required to fly over a surface area at a given distance and reference velocity, in consideration of state and input constraints imposed by the measurement system and the multirotor itself. In this paper, the trajectory control task is investigated and split into an offline generation of a suitable path and an online model predictive trajectory optimization, paired with subordinate flatness-based state feedback linearization and stabilizing controllers. With this architecture, only one constrained quadratic programming problem needs to be solved at each time step, since nonlinearities are accounted for by using the inverse terms. Velocity is employed as primary optimization variable to achieve smooth tracking of references, time-optimality within the constraints and implicit integrating behavior. Outdoor experiments with a quadrotor show good tracking performance of both the pre-calculated path and the reference velocity even in the presence of wind disturbances.