1,721,251 research outputs found
COMBINA: Relative Navigation for Unknown Uncooperative Resident Space Object
No full textIn recent years, space debris has become a threat for satellites operating in Low Earth Orbit. Even by applying debris mitigation guidelines, their number will still increase in the next century. As a consequence, active debris removal missions as well as On-Orbit Servicing missions have gained momentum at both academic and industrial level. The crucial step in both scenarios is the capability of navigating in the neighborhood of the target Resident Space Object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. Several techniques can deal with known and cooperative targets, fewer model-based methods are available if the investigated object is uncooperative (but known), while only a handful of techniques can deal with completely unknown objects, which require building their map while navigating in their neighborhood. The main downside of methods available in literature is the detection and matching of markers from measurements, a step which may severely impair convergence if poorly performed. To overcome said limitations, this paper proposes an hybrid approach for relative navigation at an unknown and uncooperative target resident space object called COarse Model Based relatIve NAvigation: CoMBiNa. The main idea of this algorithm is to combine the advantages of simultaneous localization and mapping with model-based methods by splitting the mission in two phases. During the first phase, the algorithm reconstructs a coarse model of the target. In the second phase, this coarse model is used as the base of a model-based relative navigation technique, effectively shifting the focus towards state and inertia reconstruction. The adopted model-based method avoids searching for exact feature correspondence, thus overcoming the main limitation of current methods. Additionally, this paper proposes a strategy to leverage the structure of the particular model-based navigation method chosen so that measurement outliers can be detected and rejected automatically. To conclude, this paper presents the results of the application of the proposed approach while tested on a generic target model with validation on a limited resource Single Board Computer
Autonomous GNC strategy for an asteroid impactor mission
Full text linkThe Solar System features thousands of Near-Earth Asteroids that could be at collision risk with our planet in the future. Scientists are investigating the possibility of deflecting asteroids from their trajectory by means of a hyper-velocity impactor spacecraft. The aim of this research is to develop and simulate a GNC strategy to control the spacecraft towards its impact with the asteroid. The navigation is based on the use of a camera to estimate the relative position through image analysis and a filtering process. A zero-effort error strategy is adopted for the control. A simulator has been developed to render the simulated images online and test the GNC algorithms. The simulator is used to assess the performance of the strategy on different scenarios and to perform a sensitivity analysis with respect to the environmental and design parameters
Generalized Predictive Thermal Control of a Thermal-Vacuum Chamber for Space Qualification Tests
No full textTime-Reversed Optimal Control-Based Metric for Maneuvering Targets Correlation
No full textCorrelation plays a crucial role in maintaining a space object catalog, serving the purpose of determining if a known target produced a particular measurement. Statistical distance-based approaches are not always enough when dealing with controlled objects, leading to alternative correlation metrics that exploit the effort linking the track to candidate orbits. This work describes a novel optical measurement correlation assistance tool, exploiting optimal control theory to backpropagate an admissible region of observables to catalog epochs through a patchwork of Taylor polynomial expansions. The resulting minimum expense distributions are combined with a standard statistical distance to support the correlation of maneuvering objects. A sensitivity analysis on the defined metrics is performed on a set of synthetic scenarios involving impulsive maneuvers, chosen to easily connect the detected expense and effect on the orbit to the features of the control action
Guidance Strategy for Autonomous Inspection of Unknown Non-Cooperative Resident Space Objects
Full text linkAs space debris has become a cause of concern for space operations around Earth, active debris removal and satellite servicing missions have gained increasing attention. Within this framework, in specific scenarios, the chaser might be asked to operate autonomously in the vicinity of a non-cooperative, unknown target. This paper presents a sampling-based receding-horizon motion planning algorithm that selects inspection maneuvers while taking many complex constraints into account. The proposed guidance solution is compared with classical approaches and it is shown to take advantage of the characteristics of the natural dynamics of the relative motion to outperform them. In addition, the impact of different input sampling exploration strategies is explored to propose a simple and more robust approach based on subset simulation
Low-Thrust Optimal Control of Spacecraft Hovering for Proximity Operations
Full text linkSpacecraft relative hovering is an important phase for proximity operations around nearearth space. Future space missions have raised the demand for efficient and reliable control strategies for carrying out proximity operations, and the increasing application of small satellites such as Cubesats has imposed more constraints on control algorithms. This paper designs the low-thrust optimal control maneuvers to maintain the relative motion in a bounded region, and proposes control strategies to maximize the residency time of disturbed motion in a hovering region while minimizing the fuel consumption. A semi-analytical approach is introduced to handle the fuel-optimal problem with constant tangential thrust, based on an initial guess from the analytical solution of the energy-optimal problem. Finally, a strategy for long-term hovering is presented by assembling the fundamental steps into a cycle. Numerical simulations are carried out at each stage to verify the efficiency and robustness of proposed approaches
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects
Full text linkThis paper aims to present a deep learning-based pipeline for estimating the pose of an uncooperative target spacecraft from a single grayscale monocular image. The possibility of enabling autonomous vision-based relative navigation in close proximity to a noncooperative resident space object would be especially appealing for mission scenarios such as on-orbit servicing and active debris removal. The relative pose estimation pipeline proposed in this work leverages state-of-the-art convolutional neural network (CNN) architectures to detect the features of the target spacecraft using monocular vision. Specifically, the overall pipeline is composed of three main subsystems. The input image is first processed using an object detection CNN that localizes the bounding box enclosing our target. This is followed by a second CNN that regresses the location of semantic key points of the spacecraft. Eventually, a geometric optimization algorithm exploits the detected key-point locations to solve for the final relative pose. The proposed pipeline demonstrated centimeter-/degree-level pose accuracy on the spacecraft pose estimation dataset (SPEED), along with considerable robustness to changes in illumination and background conditions. In addition, the architecture showed to generalize well on real images, despite having exclusively exploited synthetic data from the SPEED to train the CNNs
Dealing with uncertainties in angles-only initial orbit determination
No full textA method to deal with uncertainties in initial orbit determination (IOD) is presented. This is based on the use of Taylor differential algebra (DA) to nonlinearly map uncertainties from the observation space to the state space. When a minimum set of observations is available, DA is used to expand the solution of the IOD problem in Taylor series with respect to measurement errors. When more observations are available, high order inversion tools are exploited to obtain full state pseudo-observations at a common epoch. The mean and covariance of these pseudo-observations are nonlinearly computed by evaluating the expectation of high order Taylor polynomials. Finally, a linear scheme is employed to update the current knowledge of the orbit. Angles-only observations are considered and simplified Keplerian dynamics adopted to ease the explanation. Three test cases of orbit determination of artificial satellites in different orbital regimes are presented to discuss the feature and performances of the proposed methodology
Analytical Impulsive-to-Continuous Thrust Conversion in Linearized Relative Dynamics
Full text linkThe control law proposed in this study has a mathematical analytic bound used to reliably estimate the required transfer time to guarantee that the maximum thrust constraint is not violated. Favored by its analytic and iteration-free nature, the transformation can be embedded into onboard guidance planning algorithms without affecting numerical efficiency and enabling an online assessment of the consequences of a bounded, continuous thrust. The resulting control law is compared against the solution of minimum-energy, minimum-time, and minimum-fuel optimal control problems in terms of propellant consumption and transfer time. Two alternative impulsive-to-continuous thrust conversion approaches are derived, and are referred to as forward and backward conversions. The approach proposed in this study makes use of third-order polynomials to shape the trajectory without iterative procedures
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