Deep learning-based spacecraft relative navigation methods: A survey

Song, Jianing; Rondao, Duarte; Aouf, Nabil

doi:10.1016/j.actaastro.2021.10.025

Cited by 60 publications

(26 citation statements)

References 64 publications

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“…There are no surveys found by combining (ii-a), (ii-b), (iii-a), (iii-b), and the new search command, no surveys found by combining (ii-b), (iii-a), (iii-b), and the new search command, three surveys (refs. [ 101 , 102 , 103 ]) found by combining (ii-a), (iii-a), (iii-b), and the new search command, two surveys (refs. [ 104 , 105 ]) found by combining (ii-a), (ii-b), (iii-b), and the new search command, and no surveys found by combining (ii-a), (ii-b), (iii-a), and the new search command.…”

Section: Table A1mentioning

confidence: 99%

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Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Kuang

Rana

et al. 2022

Sensors

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Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.

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Section: Table A1mentioning

confidence: 99%

“… This survey studied the characterization of planetary soils instead of the terrain for planetary rover navigation. [ 103 ] This survey is not an SLR. This survey studied the autonomous spacecraft relative navigation technology instead of the terrain for planetary rover navigation.…”

Section: Table A1mentioning

confidence: 99%

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Kuang

Rana

et al. 2022

Sensors

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“…In the last few years, Deep Learning (DL) techniques have been proven successful in vision-based space applications such as satellite pose estimation (Dung et al, 2021) (Kisantal et al, 2020) and spacecraft navigation (Song et al, 2022). However, DL models require a vast amount of annotated data to learn data patterns and achieve a high performance.…”

Section: Introductionmentioning

confidence: 99%

Lessons from a Space Lab -- An Image Acquisition Perspective

Pauly¹,

Jamrozik²,

Castillo³

et al. 2022

Preprint

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The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation, when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the 'SnT Zero-G Lab', for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focus on the image acquisition equipment in a space lab: background materials, cameras and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.

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“…Compared to central processing unit (CPU) and graphics processing unit (GPU), FPGAs can provide robustness toward space radiation [71], lower latency [71], connectivity for connecting to any input with a high bandwidth [70], [72], energy efficiency [70], [72], and parallelism [70]. These advantages have resulted in using EI for spacecraft communication systems [73] and spacecraft GNC [74], [75]. To form a charged particle counter in VTXO [12], Amptek A225 and A206 hybrid FPGA chips are used to provide the data to the computer.…”

Section: Introductionmentioning

confidence: 99%

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

Pirayeshshirazinezhad

Biedron

et al. 2022

IEEE Access

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This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

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Deep learning-based spacecraft relative navigation methods: A survey

Cited by 60 publications

References 64 publications

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Lessons from a Space Lab -- An Image Acquisition Perspective

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

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