Relative Navigation Research Articles

Signals of opportunity for space navigation: An application-oriented review

Space navigation using X-Ray pulsars is known as opportunistic navigation, where we rely on signals of opportunity from natural phenomena to determine navigation solutions for spacecraft. However, this definition becomes too small or shallow, as it doesn't include other forms of navigation using natural beacons reported over the last two decades. Since a classification or review of all opportunistic techniques is still missing, this article presents the first survey on the field of signals of opportunity (SOOP), and introduces a framework for this scientific area, where natural beacons could be associated to stars, distant planets or natural phenomena, and signals could come with distinct characteristics and wavelengths. The new classification groups dozens of existing technologies of navigation using natural beacons, making easier for newcomers to interpret quickly a variety of works and mathematical formalisms. In addition, we deepen into an engineering perspective on the ongoing status of SOOP-based navigation, highlighting what is required to achieve the level of readiness on the methods that still on theory. We also give answer to a number of open questions, such as e.g. what criterion defines if a signal from a given natural beacon could be used or not for navigational purposes, as well as what is the minimum intersatellite distance that we can detect using an opportunistic signal, what type of signal characteristics we need from natural phenomena to generate navigation solutions or how far apart natural beacons preferentially need to be. These results enrich the field and become fundamental with an increasing interest of the space industry to bring novel forms of navigation into low-cost spacecraft of limited hardware capabilities (such as Cubesats).Furthermore, using results from past remote sensing missions, we preliminary found that Earth's lightning is a natural resource available in large scale and in sufficient variability that can be potentially used for relative opportunistic navigation. The physics of the problem is introduced, where at least theoretically, multiple lightning events can be used to map the arrival times to cooperating spacecraft, fixing their positions relative to each other independently from ground.

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

InSAR Simulation and Speeded-Up Robust Features Algorithm for Terrain Relative Navigation in PSRs on the Moon

Applying Machine Learning Techniques for Optical Relative Navigation in Planetary Missions

Applying Machine Learning Techniques for Optical Relative Navigation in Planetary Missions

Range-Only EKF-Based Relative Navigation for UAV Swarms in GPS-Denied Zones

Range-Only EKF-Based Relative Navigation for UAV Swarms in GPS-Denied Zones

Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images

Autonomous spacecraft relative navigation via monocular images became a hot topic in the past few years and, recently, received a further push thanks to the constantly growing field of artificial neural networks and the publication of several spaceborne image datasets. Despite the proliferation of spacecraft relative-state initialization algorithms developed, most architectures adopt computationally expensive solutions relying on convolutional neural networks (CNNs) that provide accurate output at the cost of a high computational burden that seems unfeasible for current spaceborne hardware. The paper addresses this issue by proposing a novel pose initialization algorithm based on lightweight CNNs. Inspired by previous state-of-the-art algorithms, the developed architecture leverages a fast and accurate target detection CNN followed by a line segment detection CNN capable of running with low inference time on mobile devices. The line segments and their junctions are grouped into complex geometrical groups, reducing the solution search space, and subsequently, they are adopted to extract the final pose estimate. As a main outcome, the analyses demonstrate that the lightweight architecture developed scores high accuracy in the pose estimation task, with a mean estimation error of less than 10 cm in translation and 2.5°in rotation. The baseline algorithm scores a mean SLAB error of 0.04552 with a standard deviation of 0.22972 in the test dataset. Detailed analyses demonstrate that the uncertainties on the overall pose score are driven mainly by errors in the relative attitude, which gives the highest contribution to the pose error metric adopted. The analyses on the error distributions point out that the uncertainties on the estimated relative position are higher in the camera boresight axis direction. Concerning the relative attitude, the algorithm proposed has higher uncertainties in estimating directions of the target x and y axes due to ambiguities related to the target geometry. Notably, the target detection CNN trained in this work outperforms the previous top scores in the benchmark dataset. The performances of the proposed algorithm have been investigated further by analyzing the effects on the accuracy due to the relative distance and the presence of background in the images. Lastly, the paper delves into the possibility of adopting a sub-portion of the 2D-to-3D match matrix made by the most complex perceptual groups identified that positively affects the overall run-time, pointing out the performances in terms of accuracy of the estimates and providing a comparison of both the baseline and the reduced match matrix versions against state-of-the-art algorithms concerning relative position and attitude errors and solution availability, highlighting the high accuracy and solution availability of the proposed architectures.

Autonomous relative navigation using stereo-vision in a dual-agent system for proximity operations in a lunar orbit

This paper presents a novel autonomous relative navigation architecture for inspector spacecraft in proximity operations to a known structure in a lunar orbit. Autonomous on-orbit servicing and assembly of large space structures are at the forefront of research for advancing space exploration beyond low-earth orbit. Communication delays with ground stations and limited computational power located on the inspector spacecraft make current relative navigation methods unfeasible and in turn, make it difficult to maintain an accurate estimate of the relative position and orientation of the inspector spacecraft with respect to the structure. The proposed method utilizes the camera measurements from two identical inspector spacecraft, each equipped with a single camera, to estimate the relative position and orientation of the structure. This shared measurement strategy overcomes the deficiencies in monocular vision-based relative navigation techniques, such as the range ambiguities, as is proven in the simulated results of this paper. The proposed stereo-vision system in this work allows for a deterministic solution of the relative position and orientation from the measurements of each inspector spacecraft to the structure. The stereo-vision system was tested with a Multiplicative Extended Kalman Filter (MEKF) for each spacecraft to estimate the relative states of the structure while maintaining a fixed relative position to the structure. The performance of the proposed stereo-vision system was evaluated in terms of accuracy and reliability of the estimated relative position and orientation of two inspector spacecraft to the structure over 100 Monte Carlo simulations for three case studies. The results showed that the stereo-vision system significantly outperforms the monocular vision system even when fewer points on the structure are visible, as is the case for proximity operations. Additionally, the stereo-vision system is consistently more accurate for the relative velocity, attitude, and angular velocity states regardless of how many points are visible. The proposed stereo-vision relative navigation system has the potential to be applied autonomously in proximity operations of spacecraft for various space missions, such as rendezvous and docking, inspection, and on-orbit servicing.

Modelling Detection Distances to Small Bodies Using Spacecraft Cameras

Small bodies in the Solar System are appealing targets for scientific and technological space missions, owing to their diversity in intrinsic and extrinsic properties, besides orbit and other factors. Missions to small bodies pass through the critical onboard object detection phase, where the body’s light becomes visible to the spacecraft camera. The relative line-of-sight to the object is acquired and processed to feed relative guidance and navigation algorithms, therefore steering the spacecraft trajectory towards the target. This work assesses the distance of detection for each small body in the Solar System considering the target radiometric properties, three typical spacecraft camera setups, and the relative observation geometry by virtue of a radiometric model. Several uncertainties and noises are considered in the modelling of the detection process. The detection distances for each known small body are determined for small-, medium-, and large-class spacecraft. This proves useful for early mission design phases, where a waypoint for detection needs to be determined, allowing the shift from an absolute to a relative guidance and navigation phase. The work produces an extensive dataset that is freely accessible and useful for teams working on the design phases of space missions.

Physical Body Orientation Impacts Virtual Navigation Experience and Performance.

Most human navigation studies in MRI rely on virtual navigation. However, the necessary supine position in MRI makes it fundamentally different from daily ecological navigation. Nonetheless, until now, no study has assessed whether differences in physical body orientation (BO) affect participants' experienced BO during virtual navigation. Here, combining an immersive virtual reality navigation task with subjective BO measures and implicit behavioral measures, we demonstrate that physical BO (either standing or supine) modulates experienced BO. Also, we show that standing upright BO is preferred during spatial navigation: participants were more likely to experience a standing BO and were better at spatial navigation when standing upright. Importantly, we report that showing a supine virtual agent reduces the conflict between the preferred BO and physical supine BO. Our study provides critical, but missing, information regarding experienced BO during virtual navigation, which should be considered cautiously when designing navigation studies, especially in MRI.

Extended Kalman filters for close-range navigation to noncooperative targets

This work presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations. All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behavior of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.

A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms

Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing.

Integrated robust navigation and guidance for the kinetic impact of near-earth asteroids based on deep reinforcement learning

The defense against near-Earth asteroids (NEAs) using kinetic impact is faced with various challenges, including limited maneuverability of the impactor, inaccurate dynamic models, poor observability of relative navigation, and control execution errors. To address these challenges, an integrated robust navigation and guidance method for the kinetic impact of NEAs based on deep reinforcement learning (DRL) is proposed in this paper, which can directly map angle measurements by a monocular camera to guidance maneuvers. Firstly, a discrete partial observable Markov decision process (POMDP) is modeled for the integrated navigation and guidance problem of NEA interception. To address the situation where impactors are usually only equipped with aiming cameras and only the line of sight (LOS) angle is measured, past and current LOS measurements are concatenated into a one-dimensional state observation vector to directly introduce the memory of historical state information. A shaping reward function design based on potential energy has also been proposed to solve the problem of sparse reward as the main goal. Subsequently, proximal policy optimization (PPO) is used to solve the established POMDP model, obtaining an integrated navigation and guidance policy that directly maps from the original output of the navigation sensor to the guidance command. The potentially hazard asteroid (PHA) Bennu is used as the target and a typical kinetic impact defense scenario is designed to simulate and verify the model and method proposed in this paper, considering the influence of multiple factors. Numerical simulation results show that the proposed method achieves an interception accuracy of 203.58 m (mean value). The proposed method abandons the traditional separate design of navigation and guidance algorithms, and its robustness has been tested and verified in a wide range of uncertain environments.

A High‐Resolution Digital Terrain Model Mosaic of the Mars 2020 Perseverance Rover Landing Site at Jezero Crater

AbstractWe demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High‐Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co‐aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359.

Precise Relative Navigation in Medium Earth Orbits with Global Navigation Satellite Systems and Intersatellite Links for Black Hole Imaging

The Event Horizon Telescope (EHT) is a ground-based array of Very Long Baseline Interferometry (VLBI) telescopes designed to image the event horizon of black holes. To overcome its limitations, this study explores a mission concept involving a two-satellite constellation of VLBI telescopes deployed in Medium Earth Orbit (MEO). Achieving high-resolution black hole images requires precise baseline determination at the millimetre level. To address this challenge, each satellite in the constellation is equipped with two Global Navigation Satellite System (GNSS) receivers and an optical Intersatellite Link (ISL) to enhance orbit determination. The results highlight the importance of integer ambiguity resolution and reveal that the ISL primarily improves baseline estimation along the link direction, with minimal impact along the black hole direction. Large intersatellite distances lead to sub-optimal relative orbit accuracy, challenging the attainment of the 3.5 mm relative position accuracy goal along the black hole direction.

Studying the Ephemeris Effect on Position Accuracy Based on Criteria Applied to Baseline Lengths by New MATLAB Program (NMP)

Studying the Ephemeris Effect on Position Accuracy Based on Criteria Applied to Baseline Lengths by New MATLAB Program (NMP)

Autonomous relative optical navigation based on unified modeling of external systematic errors

To solve the problem that external systematic errors of the optical camera cannot be fully estimated due to limited computing resources, a unified dimensionality reduction representation method for the external systematic errors of the optical camera is proposed, and autonomous relative optical navigation is realized. The camera translational and misalignment errors are converted into a three-dimensional rotation error, whose differential model can be established through specific attitude control and appropriate assumption. Then, the rotation error and the relative motion state are jointly estimated in an augmented Kalman filter framework. Compared with the traditional method that estimates the camera translational and misalignment errors, the proposed method reduces the computational complexity in that the estimated state dimension is reduced. Furthermore, as demonstrated by numerical simulation, the estimation accuracy is improved significantly.

Fast angles-only relative navigation using polynomial dynamics

A fast and efficient method for initial relative orbit determination from bearing-angle measurements is introduced. The range ambiguity problem for angles-only relative navigation is addressed by modeling nonlinear effects with a novel second-order mapping from relative orbit elements (ROE) to relative position coordinates. This model is used to form a system of polynomial constraint equations linking the line-of-sight measurements to the ROE. An efficient method for solving this system is developed around the insight that the ROE scale with the ratio of the inter-spacecraft separation to the orbit radius and are therefore small for most applications of interest. The method uses a truncated expansion of the quadratic formula to recursively eliminate unknowns, reduce the dimension of the system, and ultimately acquire an approximate solution. Strategies for improving robustness, efficiency, and accuracy are developed and the method is applied to general second-order systems as well as to a broad range of intial relative orbit determination scenarios. Modifications to the constraint equations and a solution algorithm are introduced to address the challenge of bias in the bearing-angle measurements.

Survey of Relative Navigation Methods for Multi-Agent Unmanned Aerial Vehicle Systems

Multi-agent Unmanned Aerial Vehicle (UAV) systems require stable and high-precision navigation. The existing navigation solutions, such as global navigation satellite systems (GNSS) and inertial navigation systems, may perform inefficiently in some application scenarios. The relative navigation methods can help solve this problem. Relative navigation enables UAVs to precisely estimate their positions relative to each other, as opposed to absolute navigation, which calculates the UAVs’ position relative to the Earth. Despite the abundance of relative navigation articles, there are no systematic reviews of relative navigation methods. Additionally, various articles on relative navigation use a variety of terms for comparable concepts, which makes it more difficult to understand the subject. Therefore, this review comprehensively studies systematizes relative navigation methods, and analyzes their strengths and weaknesses. We categorize relative navigation methods appropriate for multi-UAV systems, compare them, and make conclusions based on our findings. The relative navigation methods discussed in this review include differential GNSS, radio-frequency-based, visual, and their combinations. We evaluate the achievable accuracy and range for each type of method according to related studies. We also describe the limitations and vulnerabilities of each method. As a result, we outline relative navigation’s primary capabilities and assess its condition now.

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

AbstractTo guide the movement of a robotic swarm in a corridor‐like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self‐localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self‐localization drift. Similar to the “many wrongs principle,” it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

Space Relative Position and Attitude Measurement Method Based on Solid State Lidar and High Precision Cooperative Target

With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement.This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.

A comparative analysis of the measurement tools of fuel consumption in a passenger car

The article presents the results of practical application of the author's application for a mobile device that allows measuring fuel consumption in a passenger car. The application has two modules and works while driving the car. One of the modules retrieves data from the on-board computer using the OBD II protocol. The other module uses the phone's GPS and data entered by the user. Simultaneous data collecting from the modules allows them to be compared. Based on the results, it is concluded that the inaccuracy of GPS navigation in relation to OBD II for gasoline cars does not exceed 6%, and for those with diesel engines, 21%. The results of the work will make it easier for users to appropriately select a technique for testing the fuel consumption of a passenger car.