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This paper presents a novel trajectory planning approach for nonlinear dynamical systems; a multi-rotor drone, built on an optimization-based framework proposed by the authors named the Nonlinear Model Predictive Horizon. In the present work, this method is integrated with a Backstepping Control technique. The goal is to remove the non-convexity of the optimization problem in order to provide real-time computation of reference trajectories for the vehicle which respects its dynamics while avoiding sensed static and dynamic obstacles in the environment. Our method is applied to two models of multi-rotor drones to demonstrate its flexibility. Several simulation and hardware flight experiments are presented to validate the proposed design and demonstrate its performance improvement over earlier work. [ABSTRACT FROM AUTHOR]

The invariant observer is a recently introduced constructive nonlinear design method for symmetry-possessing systems such as the magnetometer-plus-global positioning system (GPS)-aided inertial navigation system (INS) example considered in this paper. The resulting observer guarantees a simplified form of the nonlinear estimation error dynamics, which can be stabilized by a proper choice of observer gains using a nonlinear analysis. A systematic approach to this step is the invariant Extended Kalman Filter (EKF), which is modified from its originally proposed form and applied to the aided INS example to obtain the observer gains. The resulting invariant observer is implemented onboard an outdoor helicopter unmanned aerial vehicle platform and successfully validated in experiment and demonstrates an improvement in performance over a conventional (non-invariant) EKF design. [ABSTRACT FROM PUBLISHER]