Instance Segmentation for Feature Recognition on Noncooperative Resident Space Objects

Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with the possibility to perform them through the use of an autonomous chasing spacecraft. In this work, new resources are proposed to aid the implementation of guidance, navigation, and control algorithms for satellites devoted to the inspection of noncooperative targets before any proximity operation is initiated. In particular, the use of convolutional neural networks (CNN) performing object detection and instance segmentation is proposed, and its effectiveness in recognizing the components and parts of the target satellite is evaluated. Yet, no reliable training images dataset of this kind exists to date. A tailored and publicly available software has been developed to overcome this limitation by generating synthetic images. Computer-aided design models of existing satellites are loaded on a three-dimensional animation software and used to programmatically render images of the objects from different points of view and in different lighting conditions, together with the necessary ground truth labels and masks for each image. The results show how a relatively low number of iterations is sufficient for a CNN trained on such datasets to reach a mean average precision value in line with state-of-the-art performances achieved by CNN in common datasets. An assessment of the performance of the neural network when trained on different conditions is provided. To conclude, the method is tested on real images from the Mission Extension Vehicle-1 on-orbit servicing mission, showing that using only artificially generated images to train the model does not compromise the learning process

Relative Navigation Strategy About Unknown and Uncooperative Targets

In recent years, space debris has become a threat for satellites operating in low Earth orbit. Even by applying mitigation guidelines, their number will still increase over the course of the century. As a consequence, active debris removal missions and 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 a target resident space object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. While several techniques are available when the target is cooperative or its shape is known, no approach is mature enough to deal with uncooperative and unknown targets. This paper proposes a hybrid method to tackle this issue called Coarse Model-Based Relative Navigation (CoMBiNa). The main idea of this algorithm is to split the mission into two phases. During the first phase, the algorithm constructs a coarse model of the target. In the second phase, this coarse model is used as a reference for a relative navigation technique, effectively shifting the focus toward state and inertia estimation. In addition, this paper proposes a strategy to leverage the structure of the selected navigation method to detect and reject outliers. To conclude, CoMBiNa is tested on a simulated environment to highlight its benefits and its shortcomings, while also assessing its applicability on a limited-resource single-board computer

Nonlinear control of leader-follower formation flying

This paper considers the problem of relative motion control involved in a leader-follower formation keeping mission. More specifically, center of mass dynamics of two Earth orbiting satellite is modeled, including the nonlinearity due to Earth oblateness. Next, the differential algebra is exploited to compute an high order Taylor expansion of the State-Dependent Riccati Equation (SDRE) solution. This new approach reduces the computational cost of the online Algebraic Riccati Equation solution required by SDRE algorithm; in fact, the differential algebraic formulation gives a polynomial representation which can be directly evaluated for SDRE solutions or exploited to define an initial first guess for iterative SDRE algorithms

Analysis of some global optimization algorithms for space trajectory design

In this paper, we analyze the performance of some global search algorithms on a number of space trajectory design problems. A rigorous testing procedure is introduced to measure the ability of an algorithm to identify the set of ²-optimal solutions. From the analysis of the test results, a novel algorithm is derived. The development of the novel algorithm starts from the redefinition of some evolutionary heuristics in the form of a discrete dynamical system. The convergence properties of this discrete dynamical system are used to derive a hybrid evolutionary algorithm that displays very good performance on the particular class of problems presented in this paper

Insights from a Case of Good’s Syndrome (Immunodeficiency with Thymoma)

Immunodeficiency with thymoma was described by R.A. Good in 1954 and is also named after him. The syndrome is characterized by hypogammaglobulinemia associated with thymoma and recurrent infections, bacterial but also viral, fungal and parasitic. Autoimmune diseases, mainly pure red cell aplasia, other hematological disorders and erosive lichen planus are a common finding. We describe here a typical case exhibiting all these clinical features and report a detailed immunophenotypic assessment, as well as the positivity for autoantibodies against three cytokines (IFN-alpha, IL-6 and GM-CSF), which may add to known immune abnormalities. A review of the published literature, based on case series and immunological studies, offers some hints on the still unsolved issues of this rare condition

An automatic domain splitting technique to propagate uncertainties in highly nonlinear orbital dynamics

Current approaches to uncertainty propagation in astrodynamics mainly refer to linearized models or Monte Carlo simulations. Naive linear methods fail in nonlinear dynamics, whereas Monte Carlo simulations tend to be computationally intensive. Differential algebra has already proven to be an efficient compromise by replacing thousands of pointwise integrations of Monte Carlo runs with the fast evaluation of the arbitrary order Taylor expansion of the flow of the dynamics. However, the current implementation of the DA-based high-order uncertainty propagator fails in highly nonlinear dynamics or long term propagation. We solve this issue by introducing automatic domain splitting. During propagation, the polynomial of the current state is split in two polynomials when its accuracy reaches a given threshold. The resulting polynomials accurately track uncertainties, even in highly nonlinear dynamics. The method is tested on the propagation of (99942) Apophis post-encounter motion

Propagation of Large Uncertainty Sets in Orbital Dynamics by Automatic Domain Splitting

Current approaches to uncertainty propagation in astrodynamics mainly refer to linearized models or Monte Carlo simulations. Naive linear methods fail in nonlinear dynamics, whereas Monte Carlo simulations tend to be computationally intensive. Differential algebra has already proven to be an efficient compromise by replacing thousands of pointwise integrations of Monte Carlo runs with the fast evaluation of the arbitrary order Taylor expansion of the flow of the dynamics. However, the current implementation of the DA-based high-order uncertainty propagator fails when the non-linearities of the dynamics prohibit good convergence of the Taylor expansion in one or more directions. We solve this issue by introducing automatic domain splitting. During propagation, the polynomial expansion of the current state is split into two polynomials whenever its truncation error reaches a predefined threshold. The resulting set of polynomials accurately tracks uncertainties, even in highly nonlinear dynamics. The method is tested on the propagation of (99942) Apophis post-encounter motion