Autonomous Relative Navigation Research Articles

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.

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.

Study on angle-only relative navigation for unmanned aerial vehicle formation in GPS-denied environment

To satisfy the autonomous relative navigation demand of unmanned aerial vehicle (UAV) formation in a GPS-denied environment, an angle-only relative navigation algorithm is proposed. First, the relative states in 3-dimension space are decoupled into relative motion along the horizontal plane and altitude by applying the relative azimuth between the north-east-down global frame and the body-fixed horizontal frame belonging to UAVs. Second, a relative dynamic and measurement model considering the measurement deviation of sensors are established. This solves the problem of colored noise in a majority of sensors used in practical engineering. Third, a consensus constraint model of relative states is established by introducing a geometric constraint among formation members. Additionally, a consensus-extended Kalman filter is designed. Finally, the angle-only relative navigation method is validated using standard Monte Carlo simulations. The simulation results show that all the states of the system are observable when the observability criterion is satisfied. The relative position accuracy achieved using gyros with a drift rate of 50°/h, accelerometers with 1 mg deviation, and optical cameras with 1° Gaussian white noise is within 10 m (3σ). Additionally, the method proposed in this study can be used to estimate the sensor measurement deviations effectively. Furthermore, it can be potentially applied to improve the absolute navigation accuracy of UAVs.

Analytical Nonlinear Observability Analysis for Spacecraft Autonomous Relative Navigation

Spacecraft autonomous relative navigation is widely required in the field of on-orbit services. In previous works, it has been shown that the monocular sequential images (MSIs) is a cost-effective autonomous relative navigation method. However, given the lack of size and initial orbit of noncooperative targets, the relative navigation system (RNS) with MSIs cannot ensure to estimate the entire relative motion states during the whole observation process. It is critical to perform an online observability analysis for RNS with MSIs to ensure autonomous relative motion estimation validity and accuracy. An analytical nonlinear observability analysis method is proposed in this article to improve the computational efficiency. We study the attributes of the relative orbit dynamics model and Lie algebra operations, and find that the observability matrix is only related to the limited states. The relationship among those states, rank, and the Frobenius norm of observability matrix is demonstrated, which simplify the observability criteria conditions and the degree measurements in analytical forms. In addition, based on the proposed method, we analyze the influence of the orbital manifolds on the observability degree of RNS with MSIs. Several suggestions are proposed for orbital manifolds detecting. The conclusion is a theoretical supplement to the past numerical research results. Finally, we implement the numerical simulations, and the current study’s findings verified the effectiveness of the proposed observability analysis method.

Stereo Vision-Based Relative Position and Attitude Estimation of Non-Cooperative Spacecraft

In on-orbit services, the relative position and attitude estimation of non-cooperative spacecraft has become the key issues to be solved in many space missions. Because of the lack of prior knowledge about manual marks and the inability to communicate between non-cooperative space targets, the relative position and attitude estimation system poses great challenges in terms of accuracy, intelligence, and power consumptions. To address these issues, this study uses a stereo camera to extract the feature points of a non-cooperative spacecraft. Then, the 3D position of the feature points is calculated according to the camera model to estimate the relationship. The optical flow method is also used to obtain the geometric constraint information between frames. In addition, an extended Kalman filter is used to update the measurement results and obtain more accurate pose optimization results. Moreover, we present a closed-loop simulation system, in which the Unity simulation engine is employed to simulate the relative motion of the spacecraft and binocular vision images, and a JetsonTX2 supercomputer is involved to conduct the proposed autonomous relative navigation algorithm. The simulation results show that our approach can estimate the non-cooperative target’s relative pose accurately.

Observability analysis of autonomous relative navigation system for small celestial body detection based on sequence images

The detection of small celestial bodies is significance for exploring the origin of universe and solving the problem of earth resources. Researches about the small celestial body explorer and implementation of small celestial body detection have become the focus of the future space development of various nations. Affected by the command delay of the ground control network, the small celestial body explorers need to independently obtain the position and speed information of the small celestial body, which should have the ability of autonomous relative navigation. The autonomous relative navigation based on sequence images can not only provide the angle observations of small celestial bodies, which contain the relative motion information about the small celestial bodies, but also has the advantages of low cost and low power consumption. Since that, the autonomous relative navigation based on sequence images is an ideal method for the small celestial body detection, and it has been the research hotspots in the relative navigation field. Observability analysis can evaluate the observation ability of the system, which is the basis for studying the autonomous relative navigation system based on sequence images. For the problem of nonlinearity observability analysis of the small celestial body detection autonomous relative navigation system with sequence images, this paper proposes the observability matrix based on the derivation of the 0 to 2nd order Lie derivatives of the system by the differential geometry theory. The locally weakly observability of the system under the typical orbital manifolds of small celestial body detection is realized based on the rank criterion of the observability matrix, and the results show that the small celestial body explorer can fully estimate the motion state of the small celestial body by the sequence images alone. In addition, the observability degree of the system is quantified according to the condition number method, and the influence of the varying of the relative orbital manifolds on the observability degree is analyzed. The conclusion can guide the orbit design of small celestial body detectors and have certain engineering values.

Autonomous relative navigation error compensation method for small body exploration based on sequence-images

<p indent="0mm">Deep-space small bodies are typical space non-cooperative targets. The autonomous relative navigation of space non-cooperative targets based on sequence-images is an important technical method to realize the small body exploration. Due to the complex on-orbit environment, it is inevitable that the camera will suffer the measurement deviations and the focal length deviation, which will severely affect the relative navigation state estimation accuracy. On the basis of making full use of the information of star sequence images in the field of view of the camera, the initial value of the camera on-orbit calibration is calculated by the least square method. Combined with the measurement deviation state transition model and the starlight angular distance measurement equation between two stars, the measurement deviations are expanded into state estimation parameters and the relative navigation state estimation model based on camera on-orbit error compensation is constructed. Combined with the unscented Kalman filter algorithm, the iterative estimation is carried out through comparing the information between the frames of sequence images. Then the camera measurement deviation and the relative navigation system state are obtained. In this way, the state estimation accuracy is improved. In the simulation analysis, the small body 2016HO3 is considered as the relative navigation target. The simulation results show the effectiveness of the method, which can effectively compensate the camera measurement deviation and achieve the high-precision estimation of the relative navigation system state. The research results provide certain technical support for the subsequent related tasks.

Centralized Autonomous Relative Navigation of Multiple Cubesats around Didymos System

Centralized Autonomous Relative Navigation of Multiple Cubesats around Didymos System

Generalized Angles-Only Navigation Architecture for Autonomous Distributed Space Systems

This paper addresses the development and verification of an estimation architecture for autonomous relative navigation in multi-satellite systems using angles-only measurements. Unlike traditional angles-only navigation approaches, which are generally hindered by poor handling of observability constraints, this prototype solution is designed to be flexible and applicable to a multitude of relevant missions. The estimator converges accurately and robustly in varying orbital environments without requiring maneuvers by exploiting system nonlinearities using an unscented Kalman filter that is streamlined by formulating the measurement and dynamics models using relative orbital elements. While the majority of prior work has focused on only solving for the relative motion of a single target, the approach here enables estimation of several additional parameters. Key design trades are evaluated for estimating additive sensor biases, target ballistic properties in environments with strong nonconservative perturbations, adaptive process noise statistics, and the absolute orbit of the observing satellite. The architecture is generalized for relative navigation with multiple targets, and for decentralized estimation using multiple coordinated observers. These filter strategies are used to inform the development of a novel on-orbit demonstration of angles-only navigation known as the Starling Formation-flying Optical eXperiment (StarFOX) in partnership with NASA. Key hardware-in-the-loop algorithm verification results for StarFOX-specific scenarios are provided.

Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

An integrated autonomous relative navigation method based on vision and IMU data fusion was proposed in this paper, which can improve the position accuracy effectively and has strong adaptability to environmental changes. Firstly, IMU pre-integration formula based on Runge Kutta method was derived, which can improve the pre-integration position accuracy and reduce the accumulated error effectively. Secondly, an inverse depth estimation method based on the mixed probability model was proposed during the system initialization process, which can improve the accuracy of camera depth estimation and provide better initial conditions for back-end optimization. Thirdly, a sliding window filtering method based on the probability graph was proposed, which can avoid repeated calculations and improve the sliding window filtering efficiency. Forthly, combined with the advantages of the direct method and the feature point method, a mixed re-projection optimization method was proposed, which can expand the application scope of the method and improve the optimization accuracy effectively. Finally, in the closed-loop optimization, a closed-loop optimization method based on similar transformation is proposed to eliminate the accumulated error. In order to verify the environmental adaptability of the method and the impact of closed-loop detection on the relative navigation system, indoor and outdoor experiments were carried out with a hand-held camera and an IMU. EuRoC dataset was used in the experiments and the proposed method was compared with some classical methods. The experimental results showed that this method has high accuracy and robustness.

Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.

Autonomous relative navigation for noncooperative target using stereo vision measurements

An autonomous relative navigation method based on stereo vision measurements is proposed for noncooperative targets without any prior information. Firstly, a target identification model using only stereo vision measurements is established. Based on the relative position vector computed from the stereo image sequence, the initial state vector of the noncooperative target and navigation filter covariance matrix are initialized by taken spacecraft attitude and camera installation information into account. Then, an extended Kalman filter considering state noise compensation as well as observation variance matrix and state noise matrix adaptive correction is formulated to solve the noncooperative relative navigation problem. Finally, a low Earth orbit, noncooperative target approaching navigation scenario is considered. The simulation results demonstrate the effectiveness of the proposed method for noncooperative autonomous relative navigation.

Autonomous relative navigation around uncooperative spacecraft based on a single camera

The interest of the space community toward missions like On-Orbit Servicing of functional satellite to extend their operative life, or Active Debris Removal to reduce the risk of collision among artificial objects in the most crowded orbital belts, is significantly increasing for both economical and safety aspects. These activities present significant technical challenges and, thus, can be enabled only by increasing the level of autonomy and robustness of space systems in terms of guidance, navigation and control functionalities. Clearly this goal requires the design and development of ad-hoc technologies and algorithms. In this framework, this paper presents an original architecture for relative navigation based on a single passive camera able to fully reconstruct the relative state between a chaser spacecraft and a non-cooperative, known target. The proposed architecture is loosely coupled, meaning that pose determination and full relative state estimation are entrusted to separate, but rigidly interconnected processing blocks. Innovative aspects are relevant to both the pose determination algorithms and the filtering scheme. Preliminary performance assessment is carried out by means of numerical simulations considering multiple realistic target/chaser relative dynamics and target geometries. Results allow demonstrating robustness against measurement error sources caused possibly by image processing as well as fast rotational dynamics.

Comparison of filtering techniques for relative attitude estimation of uncooperative space objects

Nowadays, one of the most active research fields in space engineering is autonomous relative navigation around uncooperative objects. A common approach used to tackle this problem is through vision-based pose determination techniques. This paper investigates the possibility of using non-linear filtering techniques to improve the attitude estimation performance of vision-based methods. Furthermore, a simulation study is presented to compare the proposed nonlinear techniques with the multiplicative extended Kalman filter for attitude estimation. First-order and second-order nonlinear filters are adapted, implemented and tested for relative attitude estimation. Finally, the consequences of uncertainty in the knowledge of the target inertia matrix are investigated.

Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms

This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and active debris removal, which require a chaser spacecraft to execute autonomous relative navigation maneuvers in close-proximity of a space target. Both the problems of pose acquisition and tracking are addressed. The former one is carried out by combining the concepts of principal component analysis and template matching in order to limit both the computational effort and the amount of on-board data storage compared with traditional approaches. The latter is entrusted to a customized implementation of the iterative closest point algorithm, which adopts multiple model-measurement matching strategies as well as a refinement step, in order to respectively increase robustness and accelerate algorithm convergence. Also, safe transition from acquisition to tracking is implemented by means of autonomous detection of failures of the pose acquisition algorithm. The performance of the proposed techniques is investigated by means of numerical simulations in which the operation of an active LIDAR system as well as the target-chaser relative dynamics are realistically reproduced. Results demonstrate algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape, in spite of considerable sparseness of the measured datasets.

A testbed for visual based navigation and control during space rendezvous operations

Visual based navigation systems are considered essential tools in the framework of close proximity space operations, such as rendezvous and docking, both in the role of primary devices and in the role of back-up systems. Autonomy in such operations is a requirement that has been increasingly underlined, especially in the case of non-cooperative tumbling targets, as for example in the active debris removal concepts. In such a case the time delays and the partial communication coverage make the human intervention unsuitable. On the other hand, robustness of the guidance and control system is certainly an issue for autonomous docking missions. In this paper, algorithms for autonomous relative navigation by means of a single camera are detailed, and tested both numerically and experimentally. At the same time a guidance strategy has been defined in order to increase the system robustness. In order to prove the soundness of the proposed navigation and guidance approach a docking mission has been successfully performed by means of two free floating platforms – a chaser and a target – on an air-bearing table, both in a nominal and in a non-nominal (i.e. with a tumbling target) scenarios.

GNC architecture for autonomous robotic capture of a non-cooperative target: Preliminary concept design

Recent studies of the space debris population in low Earth orbit (LEO) have concluded that certain regions have already reached a critical density of objects. This will eventually lead to a cascading process called the Kessler syndrome. The time may have come to seriously consider active debris removal (ADR) missions as the only viable way of preserving the space environment for future generations. Among all objects in the current environment, the SL-8 (Kosmos 3M second stages) rocket bodies (R/Bs) are some of the most suitable targets for future robotic ADR missions. However, to date, an autonomous relative navigation to and capture of an non-cooperative target has never been performed. Therefore, there is a need for more advanced, autonomous and modular systems that can cope with uncontrolled, tumbling objects. The guidance, navigation and control (GNC) system is one of the most critical ones. The main objective of this paper is to present a preliminary concept of a modular GNC architecture that should enable a safe and fuel-efficient capture of a known but uncooperative target, such as Kosmos 3M R/B. In particular, the concept was developed having in mind the most critical part of an ADR mission, i.e. close range proximity operations, and state of the art algorithms in the field of autonomous rendezvous and docking. In the end, a brief description of the hardware in the loop (HIL) testing facility is made, foreseen for the practical evaluation of the developed architecture.

An algorithm and observability research of autonomous navigation and joint attitude determination in asteroid exploration descent stage

In this paper, an autonomous relative navigation and joint attitude determination algorithm in asteroid exploration descent stage is researched based on feature point information of perpendicular asteroid surface image observed by optical navigation camera, distance vectors from spacecraft to asteroid measured by three angled installed lidars and relative velocity increment measured by accelerometer when the relative distance vector to the centroid of asteroid can not be obtained. The inertial attitude of spacecraft is determined by sun vector, star vectors and inertial angular velocity respectively measured by sun sensor, star trackers and inertial reference unit. Also, in order to obtain measurement error model transferred from sensor noise, a covariance matrix solver considering error correlation is presented via the error model of normalized vector to first order. Numerical simulation and improved observability evaluation of filtering are undertaken to discuss the results of complete sensor observation and weak observation of lidars, and verify the effectiveness of the presented relative navigation and attitude determination algorithm.

Autonomous Release of a Snagged Solar Array: Technologies and Laboratory Demonstration

This paper describes a novel concept of operations, payload hardware, terminal navigation strategy, and localized vision scheme for autonomous release of a snagged solar array using a robotic servicing spacecraft. The hardware suite on the servicer consists of two robot arms, end effector cameras, a scanning laser sensor, two local fixed lasers, an end effector fiducial tool, and various active light sources for long-range and localized target illumination. The concept of operations consists of a terminal approach phase, a servicer preparation phase, and an array release phase. The scanning laser sensor is used throughout all phases to determine target pose, whereas the end effector cameras are used during the approach phase for variable baseline stereo ranging and during the release phase for localized scene imaging and arm guidance. Realistic full-scale laboratory tests in a six-degree-of-freedom relative navigation and control testbed demonstrate the complex integration and interaction of close-range autonomous relative navigation, cooperative dual-arm robotics, and localized machine vision necessary to perform such a task.

Vision navigation algorithm for mine agent based on quaternion

Autonomous relative navigation is the key technology in mining or rescuing. On the basis of mine requirement and the theories of machine vision, an autonomous relative navigation algorithm for mine agent based on quaternion was proposed. In rotation transformation, this algorithm was represented by using quaternion. Because the Jacobi matrix of this method is lower than that of Hall algorithm, the calculation efficiency is improved by using this algorithm. The results of these experiments show that the computation burden of this algorithm based on quaternion is about half that of Hall algorithm. This algorithm based on quaternion is improved as a valid way to solve the problem of mine real-time navigation.