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Lessons Learned in Extended-Extended Spitzer Space Telescope Operations
The Spitzer Space Telescope is executing the ninth year of extended operations beyond its 5.5-year prime mission. The project anticipated a maximum extended mission of about four years when the first mission extension was proposed. The robustness of the observatory hardware and the creativity of the project engineers and scientists in overcoming hurdles to operations has enabled a substantially longer mission lifetime. This has led to more challenges with an aging groundsystem due to resource reductions and decisions made early in the extended mission based on a shorter planned lifetime. We provide an overview of the extended mission phases, challenges met in maintaining and enhancing the science productivity, and what we would have done differently if the extended mission was planned from the start to be nearly twice as long as the prime mission.
Monitoring Airspace Complexity and Determining Contributing Factors
The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.
The Dust Management Project: Characterizing Lunar Environments and Dust, Developing Regolith Mitigation Technology and Simulants
A return to the Moon to extend human presence, pursue scientific activities, use the Moon to prepare for future human missions to Mars, and expand Earth?s economic sphere, will require investment in developing new technologies and capabilities to achieve affordable and sustainable human exploration. From the operational experience gained and lessons learned during the Apollo missions, conducting long-term operations in the lunar environment will be a particular challenge, given the difficulties presented by the unique physical properties and other characteristics of lunar regolith, including dust. The Apollo missions and other lunar explorations have identified significant lunar dust-related problems that will challenge future mission success. Comprised of regolith particles ranging in size from tens of nanometers to microns, lunar dust is a manifestation of the complex interaction of the lunar soil with multiple mechanical, electrical, and gravitational effects. The environmental and anthropogenic factors effecting the perturbation, transport, and deposition of lunar dust must be studied in order to mitigate it?s potentially harmful effects on exploration systems and human explorers. The Dust Management Project (DMP) is tasked with the evaluation of lunar dust effects, assessment of the resulting risks, and development of mitigation and management strategies and technologies related to Exploration Systems architectures. To this end, the DMP supports the overall goal of the Exploration Technology Development Program (ETDP) of addressing the relevant high priority technology needs of multiple elements within the Constellation Program (CxP) and sister ETDP projects. Project scope, plans, and accomplishments will be presented.
Curiosity: the Mars Science Laboratory Project
The Curiosity rover landed successfully in Gale Crater, Mars on August 5, 2012. This event was a dramatic high point in the decade long effort to design, build, test and fly the most sophisticated scientific vehicle ever sent to Mars. The real achievements of the mission have only just begun, however, as Curiosity is now searching for signs that Mars once possessed habitable environments. The Mars Science Laboratory Project has been one of the most ambitious and challenging planetary projects that NASA has undertaken. It started in the successful aftermath of the 2003 Mars Exploration Rover project and was designed to take significant steps forward in both engineering and scientific capabilities. This included a new landing system capable of emplacing a large mobile vehicle over a wide range of potential landing sites, advanced sample acquisition and handling capabilities that can retrieve samples from both rocks and soil, and a high reliability avionics suite that is designed to permit long duration surface operations. It also includes a set of ten sophisticated scientific instruments that will investigate both the geological context of the landing site plus analyze samples to understand the chemical & organic composition of rocks & soil found there. The Gale Crater site has been specifically selected as a promising location where ancient habitable environments may have existed and for which evidence may be preserved. Curiosity will spend a minimum of one Mars year (about two Earth years) looking for this evidence. This paper will report on the progress of the mission over the first few months of surface operations, plus look retrospectively at lessons learned during both the development and cruise operations phase of the mission..
Sustainable Aviation Operations: Reducing the Impact of Aviation on our Planet
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Evolution of KSC EGS Post Space Shuttle - Success Factors and Lessons Learned
The Human Exploration and Operations Mission Directorate (HEOMD) Knowledge Capture & Transfer (KCT) team conducted video interviews with element managers in the Exploration Ground Systems (EGS) Program Office at Kennedy Space Center (KSC). The immediate goal was to capture a point-in-time profile of challenges, solutions, and lessons learned derived from EGS element development activity from the end of the Space Shuttle Program (SSP) to the present time.
Exploring Informal Learning at the Airlines
Airline pilot training is extensive, highly structured, and defined by aircraft and airspace system operating requirements, yet pilots describe a tradition of between-pilot knowledge transfer and self-directed learning. This learning supplements their approved training programs. While industry and regulators focus on “formal learning” systems, pilots report relying on “informal learning” to build operational expertise. The persistence of informal learning suggests gaps in how successfully formal learning prepares pilots to handle operational complexities. The community that researches learning has extensively studied informal learning, and its characteristics seem to align with how pilots report increasing their skills and knowledge informally. However, no research into informal learning practices among airline pilots seems to exist. In this paper we provide examples of informal learning in commercial aviation, how they fit into two existing frameworks for workplace learning, and propose that researching informal learning might help identify opportunities to improve formal aviation learning systems.
LabVIEW Serial Driver Software for an Electronic Load
A LabVIEW-language computer program enables monitoring and control of a Transistor Devices, Inc., Dynaload WCL232 (or equivalent) electronic load via an RS-232 serial communication link between the electronic load and a remote personal computer. (The electronic load can operate at constant voltage, current, power consumption, or resistance.) The program generates a graphical user interface (GUI) at the computer that looks and acts like the front panel of the electronic load. Once the electronic load has been placed in remote-control mode, this program first queries the electronic load for the present values of all its operational and limit settings, and then drops into a cycle in which it reports the instantaneous voltage, current, and power values in displays that resemble those on the electronic load while monitoring the GUI images of pushbuttons for control actions by the user. By means of the pushbutton images and associated prompts, the user can perform such operations as changing limit values, the operating mode, or the set point. The benefit of this software is that it relieves the user of the need to learn one method for operating the electronic load locally and another method for operating it remotely via a personal computer.
Performance improvement in remote manipulation with time delay by means of a learning system.
A teleoperating system is presented that involves shared control between a human operator and a general-purpose computer-based learning machine. This setup features a trainable control network termed the autonomous control subsystem (ACS) which is able to observe the operator's control actions, learn the task at hand, and take appropriate control actions. A working ACS system is described that has been put in operation for the purpose of exploring the uses of a remote intelligence of this type. The expansion of the present system into a multifunctional learning machine capable of a greater degree of autonomy is also discussed.
Mars Exploration Rover Mission
Two rovers with a sophisticated geological payload have been operating on the surface of Mars since January of 2004. Future missions and their related technology developments will benefit from the lessons learned during these surface operations. The planning cycle was dictated by the communications opportunities and the times of day that the rovers could operate, and the team and tools were tuned to optimize the mission return for that cycle time. The ability to traverse and to approach and perform in situ investigations on targets was limited in speed by the same cycle time, due to required human involvement in the related planning and risk decisions. In addition traverse was limited by the speed of the on-board terrain and hazard assessment, and in situ operations were limited by a lack of autonomy. Different planning cycles and levels of autonomy should be considered for future surface missions, which will result in different approaches to science decision making.
Software Defined Radio Architecture Contributions to Next Generation Space Communications
Space communications architecture concepts, comprising the elements of the system, the interactions among them, and the principles that govern their development, are essential factors in developing National Aeronautics and Space Administration (NASA) future exploration and science missions. Accordingly, vital architectural attributes encompass flexibility, the extensibility to insert future capabilities, and to enable evolution to provide interoperability with other current and future systems. Space communications architectures and technologies for this century must satisfy a growing set of requirements, including those for Earth sensing, collaborative observation missions, robotic scientific missions, human missions for exploration of the Moon and Mars where surface activities require supporting communications, and in-space observatories for observing the earth, as well as other star systems and the universe. An advanced, integrated, communications infrastructure will enable the reliable, multipoint, high-data-rate capabilities needed on demand to provide continuous, maximum coverage for areas of concentrated activity. Importantly, the cost/value proposition of the future architecture must be an integral part of its design; an affordable and sustainable architecture is indispensable within anticipated future budget environments. Effective architecture design informs decision makers with insight into the capabilities needed to efficiently satisfy the demanding space-communication requirements of future missions and formulate appropriate requirements. A driving requirement for the architecture is the extensibility to address new requirements and provide low-cost on-ramps for new capabilities insertion, ensuring graceful growth as new functionality and new technologies are infused into the network infrastructure. In addition to extensibility, another key architectural attribute of the space communication equipment's interoperability with other NASA communications systems, as well as those communications and navigation systems operated by international space agencies and civilian and government agencies. In this paper, we review the philosophies, technologies, architectural attributes, mission services, and communications capabilities that form the structure of candidate next-generation integrated communication architectures for space communications and navigation. A key area that this paper explores is from the development and operation of the software defined radio for the NASA Space Communications and Navigation (SCaN) Testbed currently on the International Space Station (ISS). Evaluating the lessons learned from development and operation feed back into the communications architecture. Leveraging the reconfigurability provides a change in the way that operations are done and must be considered. Quantifying the impact on the NASA Space Telecommunications Radio System (STRS) software defined radio architecture provides feedback to keep the standard useful and up to date. NASA is not the only customer of these radios. Software defined radios are developed for other applications, and taking advantage of these developments promotes an architecture that is cost effective and sustainable. Developments in the following areas such as an updated operating environment, higher data rates, networking and security can be leveraged. The ability to sustain an architecture that uses radios for multiple markets can lower costs and keep new technology infused.
On-Orbit Performance of MODIS Solar Diffuser Stability Monitor
Moderate Resolution Imaging Spectroradiometer (MODIS) is currently operated on both the Terra and Aqua spacecraft. It collects data in 20 reflective solar bands (RSB) and 16 thermal emissive bands. MODIS RSB calibration is reflectance based via an on-board solar diffuser (SD). On-orbit changes in the SD bidirectional reflectance factor are tracked by an onboard solar diffuser stability monitor (SDSM). The SDSM functions as an independent ratioing radiometer with nine filtered detectors, covering wavelengths in the visible (VIS) and near-infrared (NIR) spectral regions. A brief overview of SDSM design functions, on-orbit operations, and performance for both Terra and Aqua MODIS is provided. In addition to the SD on-orbit degradation at different wavelengths, the changes in SDSM detector responses and their potential impact on tracking SD on-orbit degradation are examined. After more than 12 years of on-orbit operation, Aqua MODIS SD has shown degradation varying from 0.6% at 0.94 microns to 19.0% at 0.41 microns. Due to more frequent solar exposure and longer operation time, the Terra MODIS SD has experienced a much larger degradation, varying from 2.3% at 0.94 microns to 48.0% at 0.41 microns. For both Terra and Aqua MODIS, the SD has experienced more degradation at shorter wavelengths. Meanwhile, the SDSM detector responses have also experienced wavelength-dependent degradation. The largest change in the SDSM detector responses, however, occurred at longer NIR wavelengths. Since launch, the SDSM systems on both Terra and Aqua MODIS have continued their nominal operations, enabling critical parameters to be derived in support of the RSB on-orbit calibration. The calibration strategies developed for and lessons learned from MODIS SDSM operations, and a preliminary performance comparison with the S-NPP VIIRS SDSM are discussed.
ISS Regenerative Life Support: Challenges and Success in the Quest for Long-Term Habitability in Space
The International Space Station's (ISS) Regenerative Environmental Control and Life Support System (ECLSS) was launched in 2008 to continuously recycle urine and crew sweat into drinking water and oxygen using brand new technologies. This functionality was highly important to the ability of the ISS to transition to the long-term goal of 6-crew operations as well as being critical tests for long-term space habitability. Through the initial activation and long-term operations of these systems, important lessons were learned about the importance of system redundancy and operational workarounds that allow Systems Engineers to maintain functionality with limited on-orbit spares. This presentation will share some of these lessons learned including how to balance water through the different systems, store and use water for use in system failures and creating procedures to operate the systems in ways that they were not initially designed to do.
An adaptive learning control system for aircraft
A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.
Probabilistic Modeling of Heavy Machinery Using Machine Learning
NASA Glenn Research Center’s facility operations seeks to leverage its extensive instrumentation and historical data with machine learning to increase system efficiencies. The ultimate goal of this effort is to probabilistically model the behavior of Glenn’s central air service compressors for optimal decision making and planning. This project is a first step in that direction. We propose a multimodel approach that uses high-dimensional models to ask simple questions about complex dependent structures, and low-dimensional models to ask complex questions about simple dependent structures. While the low-dimensional models make strong assumptions, they can be visualized and they can be insightful. We show good fits for univariate models of compressor sensors, and preliminary work on high-dimensional multivariate models.
Towards Autonomous Lunar Resource Excavation via Reinforcement Learning
To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.
Goldstone-Apple Valley Radio Telescope System Theory of Operation
The purpose of this learning module is to enable learners to describe how the Goldstone-Apple Valley Radio Telescope (GAVRT) system functions in support of Apple Valley Science and Technology Center's (AVSTC) client schools' radio astronomy activities.