Abstract
This paper proposes an overall control strategy towards cognition-supported pick-and-place tasks of tower cranes, as e.g. used in container handling applications. The presented concept is based on a global path planning algorithm which is combined with a flatness-based feedforward and a model predictive feedback control (MPC). A tower crane serves as an illustrative example for the application of the method. Assuming that the information about possible obstacles within the work space is provided by computer vision the concept is validated for pick-and-place tasks of the crane within the constrained work space.
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Acknowledgements
This work was partially supported by the Austrian FIT–IT project CLIC under project no. 819482 as well as by the Austrian FWF project no. P21253-N22 “Constrained Trajectory Optimization”.
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Egretzberger, M., Graichen, K., Kugi, A. (2012). Flatness-Based MPC and Global Path Planning Towards Cognition-Supported Pick-and-Place Tasks of Tower Cranes. In: Irschik, H., Krommer, M., Belyaev, A. (eds) Advanced Dynamics and Model-Based Control of Structures and Machines. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0797-3_8
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DOI: https://doi.org/10.1007/978-3-7091-0797-3_8
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