Prox-1 University-Class Mission to Demonstrate Automated Proximity Operations

Spencer, David A.; Chait, Sean B.; Schulte, Peter; Okseniuk, Kevin J.; Veto, Michael

doi:10.2514/1.a33526

Cited by 18 publications

(3 citation statements)

References 21 publications

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“…In contrast to other state-of-the-art systems based on light detection and ranging (LIDAR) and stereo camera sensors, monocular navigation ensures rapid pose determination under low power and mass requirements [5]. Therefore, monocular pose determination is also the natural candidate as a navigation system in small spacecraft such as CubeSats for future formation flights missions [6,7].…”

Section: Introductionmentioning

confidence: 99%

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

Sharma

Ventura

D’Amico

2018

Journal of Spacecraft and Rockets

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This work addresses the design and validation of a robust monocular vision-based pose initialization architecture for close-range onorbit-servicing and formation-flying applications. The aim is to rapidly determine the pose of a passive space resident object using its known three-dimensional wireframe model and a single low-resolution two-dimensional image collected on board the servicer spacecraft. In contrast to previous works, the proposed architecture is onboard executable and capable of estimating the pose of the client without the use of fiducial markers and without any a priori range measurements or state information. A novel feature detection method based on the filtering of the weak image gradients is used to identify the true edges of the client in the image, even in presence of the Earth in background. The detected features are synthesized using simple geometric constraints to dramatically reduce the search space of the feature correspondence problem, which is solved using the EPnP method. This approach is proven to be an order of magnitude faster than the state-of-the-art random sample consensus methods. A fast Newton-Raphson method that minimizes the fit error between corresponding image and model features is employed to refine the pose estimate and to resolve pose ambiguity. The proposed methodology is tested using actual space imagery collected during the PRISMA mission at about a 700 km altitude and a 10 m interspacecraft separation.

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Section: Introductionmentioning

confidence: 99%

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

Sharma

Ventura

D’Amico

2018

Journal of Spacecraft and Rockets

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“…To facilitate this, solving the collision avoidance problem is essential. To this end, the Artificial Potential Function (APF) method is popular and has been widely developed (Bevilacqua et al, 2011; Nag and Summerer, 2013; Palacios et al, 2015; Spencer et al, 2016; Ni et al, 2016; Huang et al, 2017a; 2017b; Cao et al, 2018). The APF method has several advantages, such as low computing cost and ease of analytical validation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Equal-Collision-Probability-Cure Method for Convex Polygon-shape Spacecraft Safe Proximity Manoeuvres

et al. 2018

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In this paper, the spacecraft close-range safe proximity problem is investigated. In the presence of a “chief” spacecraft, a Multi-Equal-Collision-Probability-Curve (MECPC) method is developed. The influence of the chief spacecraft with a convex polygon shape is considered and the chief spacecraft is divided into several small components. Each component generates a corresponding separate repulsive force and the superposition of these forces is regarded as the ultimate avoidance force. As a result, the proposed MECPC method not only improves the system robustness against control and navigation uncertainties but is also analytically validated in collision avoidance. The MECPC method solves the safe proximity problem in the presence of a convex polygon shape. In addition, an Improved Linear Quadratic Regulator (ILQR) is designed to track the expected trajectory. Numerical simulations are performed in a close-range operation environment to verify the effectiveness of the proposed MECPC method.

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“…Historically, chaser spacecraft have conducted V-bar or R-bar approaches that are either manually piloted [24,25] or controlled by a proportional-integral-derivative or H-infinity controller tracking a pre-defined straight-line (or quasi-straight) trajectory [26,27]. For a historical review on the guidance and control algorithms used in spacecraft rendezvous and proximity operations refer to [28,29].…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

et al. 2017

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Ground-based experimental evaluations of emerging guidance, navigation, and control (GNC) approaches may be used to raise their technological readiness level and determine their performance and limitations on flight-equivalent hardware (i.e., sensors, actuators, and computational systems) [2].An experimental campaign to evaluate the performance of the model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD) guidance methods has been performed at the Naval Postgraduate School POSEI-DYN 1 air-bearing test bed [4]. The focus of this research is limited in scope to the guidance and control of the simulated spacecraft. The navigation problem is solved by the POSEIDYN test bed motion capture system, which, augmented by onboard sensors, is used to provide accurate navigation data. The test vehicles operating in the POSEI-DYN test bed float on top of a 4-by-4 m granite table and exhibit a drag-free and weightless motion on a plane [4]. These test vehicles are referred to as floating spacecraft simulators, or simply as FSS.A spacecraft docking problem is selected for the experimental evaluation of these two different control approaches. A keep-out zone, an entry cone, and a maximum force constraint are added to the docking scenario to evaluate the constraint handling abilities of the two different controllers. A linear-quadratic MPC (LQ-MPC) algorithm with a quadratic programming (QP) solver and an IDVD algorithm with a nonlinear programming (NLP) solver have been chosen for this comparative study. These two controllers have been implemented and, when executed in real-time on board the FSS, they are successful in 1 POSEIDYN stands for Proximity Operation of Spacecraft: Experimental hardware-In-the-loop DYNamic simulator Abstract An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a groundbased air-bearing test bed, using spacecraft simulators that float over a granite table.Keywords Rendezvous and proximity operations · Model predictive control · Inverse dynamics · Hardware-in-theloop

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Prox-1 University-Class Mission to Demonstrate Automated Proximity Operations

Cited by 18 publications

References 21 publications

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

Multi-Equal-Collision-Probability-Cure Method for Convex Polygon-shape Spacecraft Safe Proximity Manoeuvres

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

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