While operating spacecraft, many teams break operations planning into two processes. One process focuses on planning for the upcoming cycle, while another process governs the development of unique activities that require careful study, testing, and review before they are ready for use in flight. While developing unique activities that require extra testing and review, teams often make use of testbeds on earth to ensure the activity is well planned and that sequences of commands are compatible with hardware and software configurations. Despite careful tracking, it can be easy for mistakes to creep in before sequence delivery and uplink while teams are testing and iterating over difference sequence versions. For example, a sequence that required an update for success in the testbed could have an earlier version inadvertently submitted to the flight sequence database. The Engineering Operations team for NASA's Mars Science Laboratory Curiosity Rover had a desire to improve the traceability of these sequences as they are tested, reviewed, and uplinked. This paper details the design, development, and implementation of a new tool that uses the Sequence Checksum to make it easier for operators to prove three key things during the review process. First, that the sequences evaluated in the testbed match what has been delivered to the sequence database for use in flight. Second, that all sequences being delivered as part of an activity were indeed tested. Third, that all sequences necessary for activity completion have been delivered to the sequence database. While this may be trivial to accomplish for simple activities, keeping track of all this information can be difficult for activities that include tens of sequences. This paper will discuss the design and usefulness of the tool, and also the overall activity review and approval process.
PlumeCAS is an all-in-one sample capture, volatile metering, and Capillary Absorption Spectrometer (CAS) system that can make sensitive isotopic and abundance measurements of H2O, CO2, CH4, and C2H6 in Enceladus’ plume to aid in the search for life. The instrument accumulates gaseous and icy phases during plume flythroughs in an aerogel and indium collector that preserves the integrity of small organics. The flight system would leverage a heritage hermetic sealing design to contain that sample, and a volatile metering system delivers aliquots to the CAS for analysis. There, hydrogen and carbon isotopic ratios of H2O and CO2 are measured to generate a baseline against which to compare isotopic ratios in CH4. PlumeCAS will also determine the abundances of CH4 and C2H6 to generate a CH4/C2H6 abundance ratio. These isotopic and abundance indicators are among the best-understood biosignatures available for measurement in Enceladus plume material. This analysis is nondestructive, and therefore the system has the potential to have additional gas analysis instruments added in series with the CAS. In this paper we describe the scientific motivation for the instrument, the breadboard hardware development and test campaign that validated this concept to TRL 4, and future work that may be done to better prepare the system for flight.
Stratospheric high-altitude balloons are non-extensible, sealed flexible structures designed to operate in the earth's stratosphere for extended periods. These balloons do not have propulsion engines, and their dynamics are entirely based on prevailing atmospheric wind conditions, making them vulnerable to being carried by wind currents. Station-keeping involves maintaining the balloon within a specific region for an extended duration. The standard control strategy utilizes atmospheric wind velocity variability with altitude, allowing a controller to predict the altitude with favourable wind velocities. In recent years, reinforcement learning-based station-keeping controllers have gained popularity. These controllers require extensive, realistic historical atmospheric training datasets to perform effectively. In the absence of such datasets, we propose a data-driven control strategy based on dual-mode extremum-seeking control (ESC) with vanishing oscillation for the navigation and station-keeping of high-altitude balloon platforms. Through simulation studies using real wind data from the National Oceanic and Atmospheric Administration (NOAA), we demonstrated that our proposed real-time optimization algorithm can successfully steer the balloon from one location to another without explicit knowledge of the prevailing wind dynamics.
The autonomous operation of an unmanned aerial vehicle (UAV) relies on reliable self-localization, which is typically achieved using global navigation satellite systems (GNSS). However, GNSS data can be unreliable due to effects of space weather phenomena or interference from GNSS jamming. To ensure accurate localization in such conditions, vision-based approaches for UAV positioning offer a potential alternative, though they often come with trade-offs in positioning accuracy or computational efficiency. In this paper, we present a real-time method for vision-based UAV self-localization that achieves GNSS-like accuracy. This approach involves extracting high-level semantic features from captured images and matching them to geo-referenced OpenStreetMap (OSM) data of the flight area. The global location of the UAV is then determined based on the matching results. We also compare and evaluate different metrics for measuring scene similarity to enhance the system's performance. Moreover, we demonstrate that even when OSM data is partially inaccurate, it can still be used to achieve accurate localization. This holds true even with a non-optimal neural network for segmentation and in environments with limited semantic features. The dataset used for evaluation will be made available with publication of this paper.
Space systems are complex, expensive, and challenging to design, develop, and launch, with limited options for servicing, upgrading, and extending their operational life. Decisions regarding their continued use or obsolescence are often subjective, influenced by budgetary or political factors rather than technical criteria. As systems age, performance may degrade, requirements evolve, and environmental factors shift, affecting system sufficiency. There is a need for a formal method to assess aging status and its relevance after space systems are deployed. The Technology Readiness Level (TRL), created in the 1970s, assesses technology maturity pre-deployment but is not designed to to monitor operational lifecycle stages, aging, or retirement. To fill this gap, the authors apply the extended Technology Readiness Level (eTRL), which builds on set theory to track system sufficiency through the intersection of requirements, performance, and environment. Changes in these factors can impact system relevance and lead to obsolescence. The application of eTRL is demonstrated through two case studies: the Hubble Space Telescope, which has exceeded its design life, and the retired NASA Space Shuttle. These examples highlight how the eTRL can effectively evaluate space systems' aging and contextual relevance throughout their operational lifecycles.
