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Visual Motion Tracking and Sensor Fusion for Kite Power Systems

Part of the Green Energy and Technology book series (GREEN)

Abstract

An estimation approach is presented for kite power systems with groundbased actuation and generation. Line-based estimation of the kite state, including position and heading, limits the achievable cycle efficiency of such airborne wind energy systems due to significant estimation delay and line sag. We propose a filtering scheme to fuse onboard inertial measurements with ground-based line data for ground-based systems in pumping operation. Estimates are computed using an extended Kalman filtering scheme with a sensor-driven kinematic process model which propagates and corrects for inertial sensor biases. We further propose a visual motion tracking approach to extract estimates of the kite position from ground-based video streams. The approach combines accurate object detection with fast motion tracking to ensure long-term object tracking in real time. We present experimental results of the visual motion tracking and inertial sensor fusion on a ground-based kite power system in pumping operation and compare both methods to an existing estimation scheme based on line measurements.

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Acknowledgements

This research was support by the Swiss National Science Foundation (Synergia) No. 141836 and the Swiss Commission for Technology and Innovation (CTI). We further acknowledge Corey Houle, Damian Aregger, and Jannis Heilmann from the Fachhochschule Nordwestschweiz (FHNW) for their test support and providing all the hardware used in the experiments. Development of the hardware architecture enabling onboard measurements was done by Martin Rudin and Alexander Millane (ETH Zurich). We are grateful for their support. The authors acknowledge the SpeedGoat Greengoat program.

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Hesse, H., Polzin, M., Wood, T.A., Smith, R.S. (2018). Visual Motion Tracking and Sensor Fusion for Kite Power Systems. In: Schmehl, R. (eds) Airborne Wind Energy. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-1947-0_17

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  • DOI: https://doi.org/10.1007/978-981-10-1947-0_17

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