Autonomous Optical Navigation Research Articles

Spacecraft autonomous navigation using line-of-sight directions of non-cooperative targets by improved Q-learning based extended Kalman filter

Autonomous optical navigation is one of the most promising techniques to estimate the position and velocity of a spacecraft in Earth orbit without the supports of Earth-based tracking stations. To improve the navigation performance, this paper presents a novel autonomous optical navigation method, where a star camera on the spacecraft is utilized to measure the line-of-sight (LOS) directions of a number of non-cooperative space targets, whose position vectors are supposed to be not precisely known in advance, and an improved Q-learning based extended Kalman filter (IQEKF) is developed to obtain the accurate motion state estimate of both the spacecraft and the space targets based on the LOS direction measurements. The main advantage of the presented method is that the LOS directions of the space targets can be acquired with high-accuracy by using the state-of-the-art star camera, such that the superior navigation accuracy is achievable. In addition, the whole motion state of the spacecraft, such as position, velocity, and attitude, can be obtained with the star camera, in the case that the space targets and the stars are observed simultaneously. The high performance of the presented autonomous navigation method is illustrated through a representative simulation of a medium Earth orbit (MEO) satellite. Furthermore, the simulation results indicate that the IQEKF yields more accurate solutions than the traditional navigation filtering algorithms.

Demonstration of Optical Clock-Free Localization and Navigation (CLAN)

Positioning, navigation, and timing (PNT) applications often rely on establishing synchronized clocks to achieve accurate localizations. The accuracy of the measurement is compromised due to clock drifts or inaccurate timing. In this manuscript, we propose and demonstrate autonomous optical clock-free localization and navigation (CLAN) system that eliminates the need for synchronized clocks and uses free-running lasers at the transmitter and receiver sites. Experimentally we show that a remote static or dynamic receiver >1.8km away from the transmitter determines its relative location to the transmitter with a <1cm uncertainty and successfully identifies >2cm incremental changes in distance without using time information. The experimental results are also verified by theoretical modeling and numerical simulations. The proposed technology is proven to be suitable for long-range localizations, autonomous navigations, and timing distributions in GPS-denied environments.

CNN-based Image Processing algorithm for autonomous optical navigation of Hera mission to the binary asteroid Didymos

Hera mission is the European Space Agency’s contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation.

Autonomous Navigation Performance Using Natural Feature Tracking during the OSIRIS-REx Touch-and-Go Sample Collection Event

When the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft collected a sample of surface material from asteroid Bennu in 2020 October, it was the first time that an autonomous optical navigation system relying on natural terrain features had been used to guide a spacecraft to a planetary surface. This system, called Natural Feature Tracking (NFT), works by rendering features from digital terrain models and then correlating them with the terrain in real-time navigation images to estimate the spacecraft's position and velocity with respect to the asteroid. Here we describe how the OSIRIS-REx mission built the catalog of features for NFT and how those features performed during rehearsals for and execution of the Touch-and-Go (TAG) sample collection event. Feature performance (quality and accuracy of match) in the rendering and correlation process is the basis of the NFT measurement. All features scored well above the minimum correlation threshold thanks to the effort invested in selecting and modeling them. Residuals across the TAG trajectory were small, indicating that features in the catalog were defined consistently relative to each other. NFT delivered the spacecraft to within 1 m of the targeted location, with a difference of only 3.5 cm and 1.4 s from the predicted location and time of touch. This exceptional performance was crucial for spacecraft safety given Bennu's rough and hazardous terrain.

Ground Testing of Digital Terrain Models to Prepare for OSIRIS-REx Autonomous Vision Navigation Using Natural Feature Tracking

The OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) spacecraft collected a sample from the asteroid Bennu in 2020. This achievement leveraged an autonomous optical navigation approach called Natural Feature Tracking (NFT). NFT provided spacecraft state updates by correlating asteroid surface features rendered from previously acquired terrain data with images taken by the onboard navigation camera. The success of NFT was the culmination of years of preparation and collaboration to ensure that feature data would meet navigation requirements. This paper presents the findings from ground testing performed prior to the spacecraft's arrival at Bennu, in which synthetic data were used to develop and validate the technical approach for building NFT features. Correlation sensitivity testing using synthetic models of Bennu enabled the team to characterize the terrain properties that worked well for feature correlation, the challenges posed by smoother terrain, and the impact of imaging conditions on correlation performance. The team found that models constructed from image data by means of stereophotoclinometry (SPC) worked better than those constructed from laser altimetry data, except when test image pixel sizes were more than a factor of 2 smaller than those of the images used for SPC, and when topography was underrepresented and resulted in incorrect shadows in rendered features. Degradation of laser altimetry data related to noise and spatial sampling also led to poor correlation performance. Albedo variation was found to be a key contributor to correlation performance; topographic data alone were insufficient for NFT.

Integrated autonomous optical navigation using Q-Learning extended Kalman filter

PurposeThis paper aims to improve the performance of the autonomous optical navigation using relativistic perturbation of starlight, which is a promising technique for future space missions. Through measuring the change in inter-star angle due to the stellar aberration and the gravitational deflection of light with space-based optical instruments, the position and velocity vectors of the spacecraft can be estimated iteratively.Design/methodology/approachTo enhance the navigation performance, an integrated optical navigation (ION) method based on the fusion of both the inter-star angle and the inter-satellite line-of-sight measurements is presented. A Q-learning extended Kalman filter (QLEKF) is designed to optimize the state estimate.FindingsSimulations illustrate that the integrated optical navigation outperforms the existing method using only inter-star angle measurement. Moreover, the QLEKF is superior to the traditional extended Kalman filter in navigation accuracy.Originality/valueA novel ION method is presented, and an effective QLEKF algorithm is designed for information fusion.

Autonomous optical navigation technology for Mars probe based on image edge information

Autonomous optical navigation technology for Mars probe based on image edge information

Autonomous optical navigation of Mars probe aided by one-way Doppler measurements in capture stage

The optical navigation errors of Mars probe in the capture stage depend closely on which targets are selected to be observed in the Mars system. As for this problem, an integrated navigation scheme is proposed wherein the optical observation is aided by one-way Doppler measurements. The errors are then analyzed respectively for the optical observation and one-way Doppler measurements. The real-time calculating scheme which exploits the extended Kalman filter (EKF) framework is designed for the integrated navigation. The simulation tests demonstrate that the errors of optical navigation, which select the Mars moon as the observation target, are relatively smaller than those in the Mars-orientation optical navigation case. On one hand, the integrated navigation errors do not depend on the selecting pattern of optical observation targets. On the other hand, the integrated navigation errors are significantly reduced as compared with those in the optical-alone autonomous navigation mode.

小行星探测下降着陆段光学导航技术发展综述

Autonomous optical navigation is a key technology for asteroid exploration missions. Terrain relative navigation methods based on optical images have been successfully applied in previous asteroid exploration missions. Optical navigation uses features such as the asteroid's surface topography and albedo to accurately establish reference datums and navigation landmarks, and give precise relative positions, attitudes, and velocities during descent based on the matching results of real-time observation images. The development of optical navigation technology for asteroid probe descent and landing was reviewed, and its key technologies and development trends were summarized, which provided a reference for China's asteroid exploration mission.

火星探测环绕段自主光学导航算法研究（特约）

火星探测环绕段自主光学导航算法研究（特约）

StarNAV: Autonomous Optical Navigation of a Spacecraft by the Relativistic Perturbation of Starlight.

Future space exploration missions require increased autonomy. This is especially true for navigation, where continued reliance on Earth-based resources is often a limiting factor in mission design and selection. In response to the need for autonomous navigation, this work introduces the StarNAV framework that may allow a spacecraft to autonomously navigate anywhere in the Solar System (or beyond) using only passive observations of naturally occurring starlight. Relativistic perturbations in the wavelength and direction of observed stars may be used to infer spacecraft velocity which, in turn, may be used for navigation. This work develops the mathematics governing such an approach and explores its efficacy for autonomous navigation. Measurement of stellar spectral shift due to the relativistic Doppler effect is found to be ineffective in practice. Instead, measurement of the change in inter-star angle due to stellar aberration appears to be the most promising technique for navigation by the relativistic perturbation of starlight.

High-accuracy location algorithm of planetary centers for spacecraft autonomous optical navigation

This study provides an on-board image processing algorithm for a spacecraft optical navigation sensor system. We propose a high-precision planetary center localization algorithm that provides effective information for subsequent spacecraft navigation measurement considering the geometric properties and special texture features of the planetary image. First, we combine a morphological opening with a minimum spanning circle method to preprocess an image. Second, a gradient direction partial area effect method in the edge region of interest is proposed for high-precision subpixel edge extraction. This study combines the law of edge energy distribution with a gradient direction partial area effect on planetary edge detection. A nonlocal filtering method is proposed to improve the results under complex conditions. Finally, the center coordinates of the planet are obtained by fitting the circular arc edges using least squares. The simulated and real images show that our algorithm can obtain high-precision subpixel edges and center coordinates for regular planetary images, with strong robustness to planetary images with noise, complex background, and rich texture.

Optical Image Generation and High-precision Line-of-Sight Extraction for Mars Approach Navigation

A high-precision line-of-sight extraction technique is essential for autonomous optical navigation during the Mars approach phase. To support future Mars exploration missions, an optical image simulation system is a necessary ground verification facility for Mars image generation and line-of-sight extraction algorithm tests. In this paper, an optical image generation procedure is first developed according to projection relationships, reference flight profiles and camera parameters. Next, a hybrid image processing and subpixel-level line-of-sight extraction algorithm is proposed through modification of moment-based sub-pixel edge detection and improvement of direct least-square fitting approaches. Finally, an optical image simulation system is established, and the experimental results show that the proposed procedure can effectively simulate the optical image in the field-of-view of a Mars spacecraft, and the hybrid extraction algorithm can obtain high-precision Mars centroid information.

Autonomous optical navigation using nanosatellite-class instruments: a Mars approach case study

This paper examines the effectiveness of small star trackers for orbital estimation. Autonomous optical navigation has been used for some time to provide local estimates of orbital parameters during close approach to celestial bodies. These techniques have been used extensively on spacecraft dating back to the Voyager missions, but often rely on long exposures and large instrument apertures. Using a hyperbolic Mars approach as a reference mission, we present an EKF-based navigation filter suitable for nanosatellite missions. Observations of Mars and its moons allow the estimator to correct initial errors in both position and velocity. Our results show that nanosatellite-class star trackers can produce good quality navigation solutions with low position (<300,text {m}) and velocity (<0.15,text {m/s}) errors as the spacecraft approaches periapse.

Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme.

The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

Автономная оптическая навигация на окололунных орбитах и при посадке на Луну с помощью сверхширокоугольной камеры

Автономная оптическая навигация на окололунных орбитах и при посадке на Луну с помощью сверхширокоугольной камеры

Performance enhancement of X-ray pulsar navigation using autonomous optical sensor

This paper develops an integrated navigation method based on the X-ray pulsar navigation (XNAV) system and an autonomous optical navigation system for spacecrafts. The X-ray pulsar navigation is implemented by using the difference between the measured and predicated pulse arrival time, which is calculated by comparing an observed pulse profile with a standard pulse profile. A problem arises from the X-ray signal processing in that the spacecraft’s orbit information, which may be unknown, is required to construct the observed pulse profile. The effect of the spacecraft orbit error on the accuracy of the pulse TOA (time of arrival) difference determination is analyzed. It is specified that the performance of the XNAV system may be degraded in the presence of large orbit error. In order to improve the navigation accuracy, an integrated navigation scheme is presented by fusing the measurement information of a X-ray detector and an ultraviolet optical sensor. The XNAV/optical integrated navigation system is effective to mitigate the effect of the spacecraft orbit error. The superiority of the presented scheme is illustrated through numerical simulations.

Suitability estimation of GSHHG coast line map for autonomous optical spacecraft navigation

Suitability estimation of GSHHG coast line map for autonomous optical spacecraft navigation

Attitude and position determination based on craters for precision landing

According to the problem of autonomous optical navigation, this paper presents an easy and high-precision algorithm to estimate the attitude and position of a lander by using at least three extracted marginal elliptic curves of craters. The geometric and algebraic constraints between the marginal elliptic curves of craters and its 2D images are derived, and then the linear equations about lander’s motion are established by using Kronecker product. Consequently, the laner’s attitude and position relative to targeted features are uniquely acquired from the linear equations. In particular, the algorithm is easy to carry out because all computations involved in this algorithm are linear matrix operations. The extensive experiments over simulated images and parameters demonstrate the robustness, accuracy and effectiveness of our method.

A High-accuracy Extraction Algorithm of Planet Centroid Image in Deep-space Autonomous Optical Navigation

A planet centroid is an important observable object in autonomous optical navigation. A high-accuracy algorithm is presented to extract the planet centroid from its raw image. First, we proposed a planet segmentation algorithm to segment the planet image block to eliminate noise and to reduce the computation load. Second, we developed an effective algorithm based on Prewitt-Zernike moments to detect sub-pixel real edges by determining possible edges with the Prewitt operator, removing pseudo-edges in backlit shady areas, and relocating real edges to a sub-pixel accuracy in the Zernike moments. Third, we proposed an elliptical model to fit sub-pixel edge points. Finally, we verified the performance of this algorithm against real images from the Cassini-Huygens mission and against synthetic simulated images. Simulation results showed that the accuracy of the planet centroid is up to 0·3 pixels and that of the line-of-sight vector is at 2·1 × 10−5rad.