Vision Based Navigation for Autonomous Cooperative Docking of CubeSats

A realistic rendezvous and docking navigation solution applicable to CubeSats is investigated. The scalability analysis of the ESA Autonomous Transfer Vehicle Guidance, Navigation & Control (GNC) performances and the Russian docking system, shows that the docking of two CubeSats would require a lateral control performance of the order of 1 cm. Line of sight constraints and multipath effects affecting Global Navigation Satellite System (GNSS) measurements in close proximity prevent the use of this sensor for the final approach. This consideration and the high control accuracy requirement led to the use of vision sensors for the final 10 m of the rendezvous and docking sequence. A single monocular camera on the chaser satellite and various sets of Light-Emitting Diodes (LEDs) on the target vehicle ensure the observability of the system throughout the approach trajectory. The simple and novel formulation of the measurement equations allows differentiating unambiguously rotations from translations between the target and chaser docking port and allows a navigation performance better than 1 mm at docking. Furthermore, the non-linear measurement equations can be solved in order to provide an analytic navigation solution. This solution can be used to monitor the navigation filter solution and ensure its stability, adding an extra layer of robustness for autonomous rendezvous and docking. The navigation filter initialization is addressed in detail. The proposed method is able to differentiate LEDs signals from Sun reflections as demonstrated by experimental data. The navigation filter uses a comprehensive linearised coupled rotation/translation dynamics, describing the chaser to target docking port motion. The handover, between GNSS and vision sensor measurements, is assessed. The performances of the navigation function along the approach trajectory is discussed

Guidance, Navigation and Control for Autonomous Cooperative Docking of CubeSats

Is it possible to dock CubeSats in Low Earth Orbit? The challenges are mainly associated with the level of miniaturisation. A docking mechanism was designed, built and tested in the laboratory. Results show that a relative precision better than 1 cm and 2 degrees is required for the docking. The docking mechanism and metrology system, composed of a monocular camera and sets of light- emitting diodes, are contained within 0.5U volume and can thus be used on nano-satellites. The chaser and target satellites have a complete 3-axis attitude pointing capability and are equipped with available CubeSats attitude sensors and actuators. The chaser is further equipped with a 6 degrees of freedom low-thrust cold gas propulsion system. Different robust control schemes have been investigated and their stability and performance assessed. Non-linear Monte Carlo simulations have been performed to assess the Guidance, Navigation and Control (GNC) performance and fuel consumption. Results show that the proposed GNC is robust to the various sources of uncertainties and that a lateral accuracy better than 5 mm is obtained at docking. Furthermore, it is not affected by the loss of the star trackers or by illumination conditions and can thus take place on a variety of orbits

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using the One-Class SVM (OC-SVM). We propose here to use nested entire solution path algorithms for the OC-SVM and CS-SVM in order to accelerate the parameter selection and alleviate the dependency to labeled ``changed'' samples. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and the performance of the two methods proposed compared

Unsupervised Change Detection via Hierarchical Support Vector Clustering

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system

Semi-Supervised Novelty Detection using SVM entire solution path

Very often, the only reliable information available to perform change detection is the description of some unchanged regions. Since sometimes these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform Semi-Supervised Novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the Cost-Sensitive Support Vector Machine (CS-SVM), but this requires a heavy parameter search. We propose here to use entire solution path algorithms for the CS-SVM in order to facilitate and accelerate the parameter selection for SSND. Two algorithms are considered and evaluated. The first one is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, the optimization of a separate model for each hyperparameter set is avoided. The second forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low density criterion for selecting the optimal classification boundaries, thus avoiding the recourse to cross-validation that usually requires information about the ``change'' class. Experiments are performed on two multitemporal change detection datasets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The low density criterion proposed achieves results that are close to the ones obtained by cross-validation, but without using information about the changes

Rollable Multisegment Dielectric Elastomer Minimum Energy Structures for a Deployable Microsatellite Gripper

Debris in space presents an ever-increasing problem for spacecraft in Earth orbit. As a step in the mitigation of this issue, the CleanSpace One (CSO) microsatellite has been proposed. Its mission is to perform active debris removal of a decommissioned nanosatellite (the CubeSat SwissCube). An important aspect of this project is the development of the gripper system that will entrap the capture target. We present the development of rollable dielectric elastomer minimum energy structures (DEMES) as the main component of CSO's deployable gripper. DEMES consist of a prestretched dielectric elastomer actuator membrane bonded to a flexible frame. The actuator finds equilibrium in bending when the prestretch is released and the bending angle can be changed by the application of a voltage bias. The inherent flexibility and lightweight nature of the DEMES enables the gripper to be stored in a rolled-up state prior to deployment. We fabricated proof-of-concept actuators of three different geometries using a robust and repeatable fabrication methodology. The resulting actuators were mechanically resilient to external deformation, and display conformability to objects of varying shapes and sizes. Actuator mass is less than 0.65 g and all the actuators presented survived the rolling-up and subsequent deployment process. Our devices demonstrate a maximum change of bending angle of more than 60° and a maximum gripping (reaction) force of 2.2 mN for a single actuator

Robust Phase-Correlation based Registration of Airborne Videos using Motion Estimation

This paper presents a robust algorithm for the registration of airborne video sequences with reference images from a different source (airborne or satellite), based on phase-correlation. Phase-correlations using Fourier-Melin Invariant (FMI) descriptors allow to retrieve the rigid transformation parameters in a fast and non-iterative way. The robustness to multi-sources images is improved by an enhanced image representation based on the gradient norm and the extrapolation of registration parameters between frames by motion estimation. A phase-correlation score, indicator of the registration quality, is introduced to regulate between motion estimation only and frame-toreference image registration. Our Robust Phase-Correlation registration algorithm using Motion Estimation (RPCME) is compared with state-of-the-art Mutual Information (MI) algorithm on two different airborne videos. RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. Our algorithm shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method

Development of a Space Bioreactor using Microtechnology

A miniature bio-reactor for the cultivation of cells aboard Spacelab is presented. Yeast cells are grown in a 3 milliliter reactor chamber. A supply of fresh nutrient medium is provided by a piezo-electric silicon micro-pump. In the reactor, pH, temperature, and redox potential are monitored and the pH is regulated at a constant value. The complete instrument is fitted in a standard experiment container of 63 x 63 x 85 mm. The bioreactor was used on the IML-2 mission in July 1994 and is being refurbished for a reflight in the spring of 1996

Uncooperative Rendezvous and Docking for MicroSats

This paper proposes a solution to perform active debris removal with a cost effective microsatellite. A complex aspect of debris removal in space is the detection and positive identification of the debris, medium to close approach as well as the orbital rendezvous and following on-site operations. These aspects will require a mix of several technologies, some of which already exist, and some of which will need to be miniaturized and adapted for programs such as CleanSpace One. The rendezvous phases in particular will require a good knowledge of the position of the chaser as well as that of the target. In the CleanSpace One concept, the approach and in-orbit maneuvering will be performed by a micropropulsion system based on miniature thrusters. This concept also proposes that grabbing will be done by means of a robotic claw, which will adapt itself to the form of a non-cooperating object. These are key technologies that currently being developed in EPFL laboratories. The overall microsatellite uses CubeSat and COTS technologies

Design of TRUST, a non-interventional, multicenter, 3-year prospective study investigating an integrated patient management approach in patients with relapsing-remitting multiple sclerosis treated with natalizumab

Background: Natalizumab provides rapid and high-efficacy control of multiple sclerosis disease activity with long-term stabilization. However, the benefits of the drug are countered by a risk of developing progressive multifocal leukoencephalopathy in patients infected with the John Cunningham Virus. Close monitoring is required in patients with increased progressive multifocal leukoencephalopathy risk receiving natalizumab in the long-term for an optimal benefit-risk evaluation. Standardized high-quality monitoring procedures may provide a superior basis for individual benefit and risk evaluation and thus improve treatment decisions. The non-interventional study TRUST was designed to capture natalizumab effectiveness under real-life conditions and to examine alternate approaches for clinical assessments, magnetic resonance imaging monitoring and use of biomarkers for progressive multifocal leukoencephalopathy risk stratification. Methods/Design: TRUST is a non-interventional, multicenter, prospective cohort study conducted at approximately 200 German neurological centers. The study is intended to enroll 1260 relapsing-remitting multiple sclerosis patients with ongoing natalizumab therapy for at least 12 months. Patients will be followed for a period of 3 years, irrespective of treatment changes after study start. Data on clinical, subclinical and patient-centric outcomes will be documented in order to compare the effectiveness of continuous versus discontinued natalizumab treatment. Furthermore, the type and frequency of clinical, magnetic resonance imaging and biomarker assessments, reasons for continuation or discontinuation of therapy and the safety profile of natalizumab will be collected to explore the impact of a systematic patient management approach and its potential impact on patient outcome. Specifically, the role of biomarkers, the use of expert opinions, the impact of high-frequency magnetic resonance imaging assessment for early progressive multifocal leukoencephalopathy detection and the role of additional radiological and clinical expert advice will be explored. Discussion: TRUST was initiated in spring 2014 and enrollment is anticipated to be completed by mid 2016. Annual interim analyses will deliver continuous information and transparency with regard to the patient cohorts and the completeness and quality of data as well as closely monitor any safety signals in the natalizumab-treated cohort. The study’s results may provide insights into opportunities to improve the benefit-risk assessment in clinical practice and support treatment decisions