The Journal of the Astronautical Sciences
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Published By Springer-Verlag

2195-0571, 0021-9142

Author(s):  
Daniel P. Engelhart ◽  
Jacqueline A. Reyes ◽  
Vanessa G. Murray ◽  
Dale C. Ferguson ◽  
Ryan C. Hoffmann
Keyword(s):  

Author(s):  
Ethan M. Albrecht ◽  
Audra M. Jensen ◽  
Erik G. Jensen ◽  
Kody A. Wilson ◽  
Michael K. Plummer ◽  
...  

Author(s):  
Andrea Brandonisio ◽  
Michèle Lavagna ◽  
Davide Guzzetti

AbstractLeading space agencies are increasingly investing in the gradual automation of space missions. In fact, autonomous flight operations may be a key enabler for on-orbit servicing, assembly and manufacturing (OSAM) missions, carrying inherent benefits such as cost and risk reduction. Within the spectrum of proximity operations, this work focuses on autonomous path-planning for the reconstruction of geometry properties of an uncooperative target. The autonomous navigation problem is called active Simultaneous Localization and Mapping (SLAM) problem, and it has been largely studied within the field of robotics. Active SLAM problem may be formulated as a Partially Observable Markov Decision Process (POMDP). Previous works in astrodynamics have demonstrated that is possible to use Reinforcement Learning (RL) techniques to teach an agent that is moving along a pre-determined orbit when to collect measurements to optimize a given mapping goal. In this work, different RL methods are explored to develop an artificial intelligence agent capable of planning sub-optimal paths for autonomous shape reconstruction of an unknown and uncooperative object via imaging. Proximity orbit dynamics are linearized and include orbit eccentricity. The geometry of the target object is rendered by a polyhedron shaped with a triangular mesh. Artificial intelligent agents are created using both the Deep Q-Network (DQN) and the Advantage Actor Critic (A2C) method. State-action value functions are approximated using Artificial Neural Networks (ANN) and trained according to RL principles. Training of the RL agent architecture occurs under fixed or random initial environment conditions. A large database of training tests has been collected. Trained agents show promising performance in achieving extended coverage of the target. Policy learning is demonstrated by displaying that RL agents, at minimum, have higher mapping performance than agents that behave randomly. Furthermore, RL agent may learn to maneuver the spacecraft to control target lighting conditions as a function of the Sun location. This work, therefore, preliminary demonstrates the applicability of RL to autonomous imaging of an uncooperative space object, thus setting a baseline for future works.


Author(s):  
Toby Sanders ◽  
Robert Hedges ◽  
Timothy Schulz ◽  
Melena Abijaoude ◽  
John Peters ◽  
...  
Keyword(s):  

Author(s):  
Heather M. Cowardin ◽  
John M. Hostetler ◽  
James I. Murray ◽  
Jacqueline A. Reyes ◽  
Corbin L. Cruz

AbstractThe NASA Orbital Debris Program Office (ODPO) develops, maintains, and updates orbital debris environmental models, such as the NASA Orbital Debris Engineering Model (ORDEM), to support satellite designers and operators by estimating the risk from orbital debris impacts on their vehicles in orbit. Updates to ORDEM utilize the most recent validated datasets from radar, optical, and in situ sources to provide estimates of the debris flux as a function of size, material density, impact speed, and direction along a mission orbit. On-going efforts within the NASA ODPO to update the next version of ORDEM include a new parameter that highly affects the damage risk – shape. Shape can be binned by material density and size to better understand the damage assessments on spacecraft. The in situ and laboratory research activities at the NASA ODPO are focused on cataloging and characterizing fragments from a laboratory hypervelocity-impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provides a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. A subset of fragments is being analyzed further in NASA’s Optical Measurement Center (OMC) using broadband, bidirectional reflectance measurements to provide insight into the optical-based NASA Size Estimation Model. Additionally, pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization. This paper provides an overview of DebriSat, the status of the project, and ongoing fragment characterization efforts within the OMC.


Author(s):  
Jesse A. Greaves ◽  
Daniel J. Scheeres

AbstractInternational interest in the sustained development of cislunar space will generate traffic and debris in the region which requires monitoring; similar to how current space situation awareness is necessary for the traffic and debris near Earth. There are many challenges associated with developing a cislunar situation awareness program, but 2 primary issues addressed by this paper are observational strategies and maneuver detection methods. This work proposes an observational strategy that utilizes the ballistic Optimal Control Based Estimator (OCBE) to filter measurements from cislunar optical observers. To reduce numerical issues associated with filtering, new modifications to the ballistic Optimal Control Based Estimator (OCBE) are introduced that preserve the OCBE update equations in Square Root Information (SRI) space. This new derivation produces a more stable version of the ballistic OCBE which is beneficial for filtering larger data sets with non-linear motion and measurements. Applying the SRI OCBE to the estimation problem it was found that only a single L2 observer with angle and angle-rate measurements provided sufficient information for consistent estimation. Then a newly developed maneuver detection method is presented to statistically identify maneuvers. The method applies a binary hypothesis test to the optimal control policy of the ballistic OCBE to quantify mismodeling. This method was tested given a impulsive maneuver policy with a mean of 50 mm/s and standard deviation of 15 mm/s, and 194 out of 200 tests correctly identified if a maneuver occurred. The OCBE control policy also provided appropriate impulse estimates of mismodeling, which may be used to reconstruct maneuvers in future work. Together, the proposed observation and maneuver detection methodology yields reliable tracking and provides a statistical framework to detect maneuvers.


Author(s):  
V. Franzese ◽  
F. Topputo ◽  
F. Ankersen ◽  
R. Walker

AbstractThe Miniaturised Asteroid Remote Geophysical Observer (M-ARGO) mission is designed to be ESA’s first stand-alone CubeSat to independently travel in deep space with its own electric propulsion and direct-to-Earth communication systems in order to rendezvous with a near-Earth asteroid. Deep-space Cubesats are appealing owing to the scaled mission costs. However, the operational costs are comparable to those of traditional missions if ground-based orbit determination is employed. Thus, autonomous navigation methods are required to favour an overall scaling of the mission cost for deep-space CubeSats. M-ARGO is assumed to perform an autonomous navigation experiment during the deep-space cruise phase. This paper elaborates on the deep-space navigation experiment exploiting the line-of-sight directions to visible beacons in the Solar System. The aim is to assess the experiment feasibility and to quantify the performances of the method. Results indicate feasibility of the autonomous navigation for M-ARGO with a 3σ accuracy in the order of 1000 km for the position components and 1 m/s for the velocity components in good observation conditions, utilising miniaturized optical sensors.


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