Grasping objects using an unmanned aerial vehicle (UAV) equipped with an onboard manipulator is far more flexible compared to ground mobile robots, since the UAV has a larger operation space. However, it still faces a great challenge, particularly when the dynamics of the UAV is not known exactly or the system is impacted by unknown external disturbances. To maintain a stable flight and grasp an object in the air precisely, a reliable control strategy is necessary. In this paper, we present a control framework based on nonlinear model predictive control (NMPC) combined with an augmented dynamics model employing Gaussian processes (GP) as a nonparametric regression model. Throughout the real-world experimental results, our proposed control framework eliminates at least 38% more of the trajectory tracking error in comparison to the NMPC with the nominal dynamics model alone, and it ensures a stable and dependable flight performance to grasp an object near the ground.


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    Title :

    Gaussian Process Regression-augmented Nonlinear Model Predictive Control for Quadrotor Object Grasping*


    Contributors:


    Publication date :

    2022-06-21


    Size :

    4219506 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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