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

A high order method for orbital conjunctions analysis: Monte Carlo collision probability computation

Three methods for the computation of the probability of collision between two space objects are presented. These methods are based on the high order Taylor expansion of the time of closest approach (TCA) and distance of closest approach (DCA) of the two orbiting objects with respect to their initial conditions. The identification of close approaches is first addressed using the nominal objects states. When a close approach is identified, the dependence of the TCA and DCA on the uncertainties in the initial states is efficiently computed with differential algebra (DA) techniques. In the first method the collision probability is estimated via fast DA-based Monte Carlo simulation, in which, for each pair of virtual objects, the DCA is obtained via the fast evaluation of its Taylor expansion. The second and the third methods are the DA version of Line Sampling and Subset Simulation algorithms, respectively. These are introduced to further improve the efficiency and accuracy of Monte Carlo collision probability computation, in particular for cases of very low collision probabilities. The performances of the methods are assessed on orbital conjunctions occurring in different orbital regimes and dynamical models. The probabilities obtained and the associated computational times are compared against standard (i.e. not DA-based) version of the algorithms and analytical methods. The dependence of the collision probability on the initial orbital state covariance is investigated as wel

Probabilistic data association: the orbit set

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