Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV Path Planning (PP) system plans the best path to per- form the mission and then it uploads this path on the Flight Management System (FMS) providing reference to the aircraft navigation. Tracking the path is the way to link kine- matic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control sys- tem solves on-line (i.e., at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory.


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    Title :

    NMPC and genetic algorithm based approach for trajectory tracking and collision avoidance of UAVs



    Publication date :

    2013-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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