Proximity operations and docking sensor development

Howard, Richard T.; Bryan, Thomas C.; Brewster, Linda L.; Lee, James E.

doi:10.1109/aero.2009.4839574

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Moreover, a distance of 5 km can be considered within the range for proximity operations. 13 The orbit considered for describing the GPS orbit is related to the PRN-06 satellite, and its orbital parameters are accurate with respect to the Yu- On the contrary, the same is not true for the three-dimensional orbits of both the GPS and SSO cases. That is, in the GPS and SSO cases, one cannot understand if the orbit is Keplerian or non-Keplerian.…”

Section: Arbitrary-orientation Modelmentioning

confidence: 99%

Osculating Keplerian Elements for Highly Non-Keplerian Orbits

Peloni

McInnes

Ceriotti

2018

Journal of Guidance, Control, and Dynamics

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This paper presents a mapping between the elements of highly non-Keplerian orbits and classical orbital elements. Three sets of elements are discussed and mappings are derived in closed, analytical form for both the direct and inverse problem. Advantages and drawbacks of the use of each set of elements are discussed. The spacecraft thrust-induced acceleration used to generate families of highly non-Keplerian orbits is extracted from the inverse mapping from the osculating orbital elements. The key signatures of highly non-Keplerian orbits in Keplerian elements tracking data are determined through a set of representative test cases.

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Section: Arbitrary-orientation Modelmentioning

confidence: 99%

Osculating Keplerian Elements for Highly Non-Keplerian Orbits

Peloni

McInnes

Ceriotti

2018

Journal of Guidance, Control, and Dynamics

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“…The visual-perception camera is a fairly common piece of aerospace pose-measurement equipment, and is characterized by high accuracy, noncontact use, and low power consumption. It is widely used in tasks such as spacecraft rendezvous, care and maintenance of on-orbit load of space manipulators, and cleanup of space debris or abandoned satellites [ 1 , 2 , 3 , 4 ]. The visual-measurement camera uses the visual-positioning marker as the observed target, the relationship of the target feature in the image as a reference, and image preprocessing, target recognition, and attitude calculation to determine the position and attitude of the target.…”

Section: Introductionmentioning

confidence: 99%

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification

Ren

Wang

Dong

2022

Sensors

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Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.

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“…NASA's rendezvous docking mission has produced and utilized sensors suitable for the sections, but it is inevitable to consume a lot of resources such as manpower, development period, and development cost. [8][9] Typical RV&D missions, such as the removal of space debris, are noncooperative and difficult to apply if their shape changes due to external collisions and arbitrary rotation. In addition, the space environment is heavily influenced by factors such as movement and light condition.…”

Section: Introductionmentioning

confidence: 99%

Strategy for on-orbit space object classification using deep learning

Lim

Kim

2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Since nanosatellites are spotlighted as a verification platform for space technology, new studies on on-orbit satellite servicing using nanosatellites are being conducted. This servicing is based on space robotics using vision-based sensors in the rendezvous state with a target satellite. The space environment, such as sunlight and Earth albedo, affects the mission. Simulation of the space environment on the ground is difficult, but the development of robust algorithms which reflect the effect is essential. In particular, missions such as active debris removal require a method for overcoming changes in any known information due to external factors such as collisions. This study proposes a new strategy on nanosatellite for on-orbit space object classification by applying deep learning to sensor-based orbit satellite service activity. When previously known information is changed, a method of online learning on orbit after obtaining additional data at a short relative distance can help determine the final service part. Using the images and point cloud data that simulate the space environment, we apply a convolutional neural network and PointNet to classify the objects. The learning environment is studied using a general desktop and a micro-graphics processing unit (GPU) board that can be mounted on a nanosatellite. For the training, we used self-produced data using 3D models of nanosatellites and asteroids with similar shapes, which are difficult to distinguish with existing algorithms. Consequently, the proposed strategy by the author shows feasibility of using nanosatellite’s micro GPU for on-orbit space object classification, and it is verified that point cloud–based methods are more suitable by utilizing deep learning for nanosatellites. The proposed method with processor of nanosatellite is applicable to satellite service missions in orbit, such as capturing of robotic parts for extending life span or removing space debris.

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Proximity operations and docking sensor development

Abstract: Abstract-The Next Generation Advanced Video Guidance Sensor (NGAVGS) has been under development for the last three years as a long-range proximity operations and docking sensor for use in an Automated Rendezvous and Docking (AR&D) system.

Cited by 7 publications

References 5 publications

Osculating Keplerian Elements for Highly Non-Keplerian Orbits

Osculating Keplerian Elements for Highly Non-Keplerian Orbits

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification

Strategy for on-orbit space object classification using deep learning

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