An overview of the autonomous navigation for a gravity-assist interplanetary spacecraft

Ma, Xin; Fang, Jiancheng; Ning, Xiaolin

doi:10.1016/j.paerosci.2013.06.003

Cited by 26 publications

(20 citation statements)

References 49 publications

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“…One key enabler for autonomy is on-board optical navigation (OPNAV) that uses imaging data to aid in spacecraft navigation. It has the advantages of independence, low cost, high reliability, high accuracy, and real-time performance, and has become a key technology in deep space exploration [1], [2]. In the typical case, OPNAV uses the optical sensor on-board to take pictures of some target body, extracts the optical observables from the picture, and then, measures the image coordinates of the target as navigation information [3].…”

Section: Introductionmentioning

confidence: 99%

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Xiao

Fan

Feng

et al. 2020

IEICE Trans. Inf. & Syst.

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Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, lightweight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a lightweight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

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Section: Introductionmentioning

confidence: 99%

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Xiao

Fan

Feng

et al. 2020

IEICE Trans. Inf. & Syst.

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“…Future deep-space exploration is expected to rely more and more on autonomous spacecraft [ 1 , 2 , 3 , 4 ]. Indeed, traditional ground-based radio navigation poses limitations in terms of data transmission latency, channel availability, and robustness to communication failures.…”

Section: Introductionmentioning

confidence: 99%

A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation

Gioia

Meoni

Giuffrida

et al. 2020

Sensors

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Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.

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“…The INS alone can hardly provide accurate navigation information over a long period of time. To compensate this deficiency of the INS, various integrated navigation systems centered on inertial navigation have been developed, and an inertial/gravity integrated navigation system based on characteristics of the geophysical field has become an effective approach for navigation by underwater submarines under conditions of long-range underwater navigation over a long period [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The geophysical field-aided navigation method primarily uses gravity and magnetic fields and sea floor terrain data measured by an underwater submarine to perform matching and comparison with graphical data stored in advance, thus obtaining accurate estimates of locations and correcting the INS errors.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation

Wang

Chai

et al. 2017

Sensors

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The variation of a marine gravity anomaly reference map is one of the important factors that affect the location accuracy of INS/Gravity integrated navigation systems in underwater navigation. In this study, based on marine gravity anomaly reference maps, new characteristic parameters of the gravity anomaly were constructed. Those characteristic values were calculated for 13 zones (105°–145° E, 0°–40° N) in the Western Pacific area, and simulation experiments of gravity matching-aided navigation were run. The influence of gravity variations on the accuracy of gravity matching-aided navigation was analyzed, and location accuracy of gravity matching in different zones was determined. Studies indicate that the new parameters may better characterize the marine gravity anomaly. Given the precision of current gravimeters and the resolution and accuracy of reference maps, the location accuracy of gravity matching in China’s Western Pacific area is ~1.0–4.0 nautical miles (n miles). In particular, accuracy in regions around the South China Sea and Sulu Sea was the highest, better than 1.5 n miles. The gravity characteristic parameters identified herein and characteristic values calculated in various zones provide a reference for the selection of navigation area and planning of sailing routes under conditions requiring certain navigational accuracy.

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An overview of the autonomous navigation for a gravity-assist interplanetary spacecraft

Cited by 26 publications

References 49 publications

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation

Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation

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