Visual Motion Tracking and Sensor Fusion for Kite Power Systems

Hesse, Henrik; Polzin, Max; Wood, Tony; Smith, Roy S.

doi:10.1007/978-981-10-1947-0_17

Cited by 5 publications

(14 citation statements)

References 30 publications

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“…We follow the definitions in Hesse et al (2016). The quarter-sphere, {(x, y, z) ∈ R 3 |x > 0, z > 0, x 2 + y 2 + z 2 ≤ r 2 }, with radius, r, and its center at the origin of inertial frame, {G}, is referred to as the wind-window.…”

Section: System Descriptionmentioning

confidence: 99%

“…Line angle measurements at the GS are sampled at 100 Hz. They are always available but known to be biased, Hesse et al (2016). Visually tracked positions are extracted in 30 images per second.…”

Section: System Descriptionmentioning

confidence: 99%

“…Thus, measurements from line angle sensors at the GS are assumed to point towards the wing. Test flights have shown that this assumption is often violated and tether dynamics are not negligible, Hesse et al (2016). As seen from the perspective of the wing, line angles measured at the ground follow the wing with a temporal delay, t d (t), and move slower by a velocity difference, v d (t).…”

Section: Dual Unicycle Modelmentioning

confidence: 99%

“…Position measurements, extracted from a video footage of a camera mounted on the tether lead-out at the GS, have revealed that line angle measurements are affected by a time-varying delay up to two seconds and tether sag, Hesse et al (2016). Consequently, an EKF with an augmented state vector to actively account for dead-times in an AWE system is proposed by Hesse et al (2016). Measurements from an IMU are used to propagate the augmented errorstate vector in an EKF, and line angle measurements are fused, assuming a constant delay.…”

Section: Introductionmentioning

confidence: 99%

“…The state of the wing and model parameters, such as a dynamic tether delay, are estimated simultaneously. The estimator is compared to a state-of-the-art estimator, Hesse et al (2016), using experimental flight data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

State Estimation for Kite Power Systems with Delayed Sensor Measurements

et al. 2017

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We present a novel estimation approach for airborne wind energy systems with ground-based control and energy generation. The estimator fuses measurements from an inertial measurement unit attached to a tethered wing and position measurements from a camera as well as line angle sensors in an unscented Kalman filter. We have developed a novel kinematic description for tethered wings to specifically address tether dynamics. The presented approach simultaneously estimates feedback variables for a flight controller as well as model parameters, such as a time-varying delay. We demonstrate the performance of the estimator for experimental flight data and compare it to a state-of-the-art estimator based on inertial measurements.

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Section: System Descriptionmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: Dual Unicycle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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State Estimation for Kite Power Systems with Delayed Sensor Measurements

et al. 2017

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Identification of kite aerodynamic characteristics using the estimation before modeling technique

et al. 2021

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The aerodynamic characteristics of a leading edge inflatable (LEI) kite and a rigid‐framed delta (RFD) kite were investigated. Flight data were recorded by using an experimental setup that includes an inertial measurement unit, a GPS, a magnetometer, and a multi‐hole Pitot tube onboard the kites, load cells at every tether, and a wind station that measures the velocity and heading angle of the wind. These data were used to feed a flight path reconstruction algorithm that estimated the full state vector of the kite. Since the latter includes the aerodynamic force and moment about the center of mass of the kite, quantitative information about the aerodynamic characteristics of the kites was obtained. Due to limitation of the experimental setup, the LEI kite flew most of the time in post‐stall conditions, which resulted in a poor maneuverability and data acquisition. This assumption was corroborated by a particular maneuver where the lift coefficient decreased from 1 to 0.4, while its angle of attack increased from 35° to 50°. On the contrary, abundant flight data were obtained for the RFD kite during more than 10 figure‐eight maneuvers. Although the angle of attack was high, between 20° and 40°, the kite did not reach its maximum lift coefficient. High tether tensions and a good maneuverability were achieved. Statistical analysis of the behavior of the lift, drag, and pitch moment coefficients as a function of the angle of attack and the sideslip angle allowed to identify some basic aerodynamic parameters of the kite.

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Automatic testbed with a visual motion tracking system for airborne wind energy applications

et al. 2023

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The architecture and a flight test campaign of a small‐scale testbed aimed at aerodynamic and dynamic characterization of airborne wind energy systems are presented. The testbed involves a two‐line rigid‐framed delta kite and an automatic ground station for the lateral control of the kite and reel‐in/reel‐out of the two tethers. The environment, and the states of the kite, the tethers and the actuators are measured by a set of on‐ground and onboard sensors that include, among others, an inertial measurement unit, GNSS receivers, load cells, actuator encoders, a wind station, and a visual motion tracking (VMT) system based on three cameras and an artificial neural network (YOLOv2). The results of a 5‐min flight, including the take‐off, cross‐wind flight, and landing, were used to analyze the capabilities of the testbed. It was shown that the time derivative of the kite course angle exhibits a linear correlation with both the delayed steering input and the delayed differential tether tension, being the dispersion lower for the latter. The intrinsic and extrinsic calibrations proposed for the VMT system led to a good agreement between the estimation of the kite position and course angle provided by the VMT system and the onboard computer. Moreover, although the YOLOv2 algorithm failed in the detection of the kite within around 5% of the images, the simultaneous non‐detection from the three cameras was below 0.1% during the full flight. Such a reliability suggests that a VMT system can be used as a redundant or backup sensor for the GNSS.

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Visual Motion Tracking and Sensor Fusion for Kite Power Systems

Cited by 5 publications

References 30 publications

State Estimation for Kite Power Systems with Delayed Sensor Measurements

State Estimation for Kite Power Systems with Delayed Sensor Measurements

Identification of kite aerodynamic characteristics using the estimation before modeling technique

Automatic testbed with a visual motion tracking system for airborne wind energy applications

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