Sensor Fusion Applied to Autonomous Aerial Refueling

Williamson, Walton R.; Glenn, Gregory J.; Dang, Vu T.; Speyer, Jason L.; Stecko, Stephen M.; Takacs, John M.

doi:10.2514/1.34589

Cited by 64 publications

(39 citation statements)

References 8 publications

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“…Of particularly note is the work by Darling [6] which demonstrated formation flight at 20 m separation, using a vision-only pose estimation algorithm. Various forms of the Kalman filter have been used to fuse onboard sensor data with vision-only pose estimates [4,14] and with raw marker observations [10,24]. Our work has similarities with the latter but differs by estimating the relative state directly, rather than a concatenated individual vehicle states.…”

Section: A Related Workmentioning

confidence: 99%

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Guidance and Navigation for UAV Airborne Docking

Wilson¹,

Göktoğan²,

Sukkarieh³

2015

Robotics: Science and Systems XI

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Abstract-Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard. Airborne docking, for mid-air refueling, is a viable solution that has been implemented with manned aircraft for decades, but has yet to be achieved with their unmanned counterparts. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is necessary to dock with a small target, in the air, amidst external disturbances. This paper presents a complete solution to airborne docking, which includes vision-aided unscented Kalman filters for leaderrelative navigation and docking appendage motion estimation; and guidance that is suitable for all phases of the mission. The work concludes by demonstrating the proposed algorithms in what is thought to be the first UAV airborne docking.

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Section: A Related Workmentioning

confidence: 99%

“…Airborne docking offers a solution by enabling in-flight refueling [22,24], recharging and payload transfer. In addition to extended range and endurance, fuel savings of 30-40 % have been suggested for haulage flights, through the elimination of refueling stopovers [17].…”

Section: Introductionmentioning

confidence: 99%

Guidance and Navigation for UAV Airborne Docking

Wilson¹,

Göktoğan²,

Sukkarieh³

2015

Robotics: Science and Systems XI

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“…The attempt of this paper is to present a practical vision navigation sensor with a new image processing strategy to detect drogue image position for autonomous probe-and-drogue refueling system. Vision-based navigation method is considered the most feasible solution to the problem of autonomous navigation in deep-space missions, and becomes increasingly popular in autonomous aerial refueling tasks [8][9][10][11][12]. The measurement precision is increased with proximity to the target in vision systems [13,14].…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision Navigation Sensor for Autonomous Aerial Refueling

Huang¹,

Sun²,

Ling³

et al. 2017

Preprint

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Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probeand-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

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“…This is partly due to the fact that it is generally required in many typical applications such as estimating the motion of a robot end-effector 9 , navigation for small unmanned aerial vehicle 10 , spacecraft relative navigation in rendezvous 11 etc. The widely used way of obtaining position and orientation is using an inertial measurement unit (IMU) as the navigation sensor, which consists of a tri-axis gyroscope and a tri-axis accelerometer.…”

Section: Introductionmentioning

confidence: 99%

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors

Sun

Jia

2016

JRNAL

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This paper is concerned with the attitude/position estimation of a rigid-body using inertial and vision sensors. By employing the Newton-Euler method, a kinematic model is developed for the rigid-body by treating the inertial measurements as inputs. Based on the coordinate transformation, a nonlinear visual observation model is proposed by using the image coordinates of feature points as observations. Then the Extended Kalman filter (EKF) is utilized to estimate the attitude/position recursively. The effectiveness of the proposed algorithm is evaluated by simulation.

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Sensor Fusion Applied to Autonomous Aerial Refueling

Cited by 64 publications

References 8 publications

Guidance and Navigation for UAV Airborne Docking

Guidance and Navigation for UAV Airborne Docking

Monocular Vision Navigation Sensor for Autonomous Aerial Refueling

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors

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