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The Autonomous Sciencecraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-1) spacecraft, integrates several autonomy software technologies enabling autonomous science analysis and mission planning.
This paper describes the design of the Autonomous Sciencecraft Experiment, a software agent that has been running on-board the EO-I spacecraft since 2003. The agent recognizes science events, retargets the spacecraft to respond to the science events, and reduces data downlink to only the highest value science data. The autonomous science agent was designed using a layered architectural approach with specific redundant safeguards to reduce the risk of an agent malfUnction to the EO-I spacecraft. The agent was designed to be "safe" by first preventing anomalies, then by automatically detecting and responding to them when possible. This paper describes elements of the design that increase the safety of the agent, the anomalies that occurred during the experiment, and how the agent responded to these anomalies.
The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASAs New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control software, image formation software, and science processing software will be uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 to detect surface modification related to volcanism, ice formation and retreat, and flooding.
Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. Here in this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.
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NASA's New Millennium Program (NMP) Autonomous Sciencecraft Experiment (ASE) [1-3] has been successfully demonstrated in Earth-orbit. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control, image formation, and science processing software was uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 and has been undergoing onboard testing since May 2004 for the near real-time detection of surface modification related to transient geological and hydrological processes such as volcanism [4], ice formation and retreat [5], and flooding [6]. Space autonomy technology developed as part of ASE creates the new capability to autonomously detect, assess, react to, and monitor dynamic events such as flooding. Part of the challenge has been the difficulty to observe flooding in real time at sufficient temporal resolutions; more importantly, it is the large spatial extent of most drainage networks coupled with the size of the data sets necessary to be downlinked from satellites that make it difficult to monitor flooding from space. Below is a description of the algorithms (referred to as ASE Flood water Classifiers) used in tandem with the Hyperion spectrometer instrument on EO-1 to identify flooding and some of the test results.
In 2006, the Air Force TechSat 21 mission will fly the Autonomous Sciencecraft Experiment (ASE). ASE will perform onboard: science analysis, mission re-planning, and robust execution. These technologies will enable TechSat 21 to respond onboard to sciene opportunities by retargeting future science observations and returning only the most important science data. This capability can dramatically increase science return and is central to future mission concepts to monitor dynamic phenomena such as lo volcanoes, the Europa ice crust, and Martian sandstorms.
Software has been developed to perform a number of functions essential to autonomous operation in the Autonomous Sciencecraft Experiment (ASE), which is scheduled to be demonstrated aboard a constellation of three spacecraft, denoted TechSat 21, to be launched by the Air Force into orbit around the Earth in January 2006. A prior version of this software was reported in Software for an Autonomous Constellation of Satellites (NPO-30355), NASA Tech Briefs, Vol. 26, No. 11 (November 2002), page 44. The software includes the following components: Algorithms to analyze image data, generate scientific data products, and detect conditions, features, and events of potential scientific interest; A program that uses component-based computational models of hardware to analyze anomalous situations and to generate novel command sequences, including (when possible) commands to repair components diagnosed as faulty; A robust-execution-management component that uses the Spacecraft Command Language (SCL) software to enable event-driven processing and low-level autonomy; and The Continuous Activity Scheduling, Planning, Execution, and Replanning (CASPER) program for replanning activities, including downlink sessions, on the basis of scientific observations performed during previous orbit cycles.
NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASA s New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. ASE is scheduled to fly on the US Air Force Research Laboratory (AFRL) Techsat-21 constellation in 2006. Tech- Sat-21 consists of three satellites flying in a variable-geometry formation in Earth orbit. Each satellite is equipped with X-band Synthetic Aperture Radar, yielding high spatial resolution images (approx. 3 m) of the Earth s surface. The constellation will fly at an altitude of 550 km, in a 35.4 inclination circular orbit, yielding exact repeat-track observations every 13 days. Prior to full deployment, elements of the versatile ASE spacecraft command and control software, image formation software and science processing software will be utilized and tested on two very different platforms in 2003: AirSAR and EO-1 (described below). Advantages of Autonomous Operations: ASE will demonstrate advanced autonomous science data acquisition, processing, and product downlink prioritization, as well as autonomous spacecraft command and control, and fault detection. The advantages of spacecraft autonomy are to future missions include: (a) making the best use of reduced downlink; (b) the overcoming of communication delays through decisionmaking in situ, enabling fast reaction to dynamic events; (c) an increase of science content per byte of returned data; and (d) an avoidance of return of null (no-change/no feature) datasets: if there is no change detectable between two scenes of the same target, there is no need to return the second dataset.
This paper presents an overview of the New Millennium Space Technology 6 (ST6) Autonomous Rendezvous Experiment (ARX) mission and system.
With the advancement of artificial intelligence and machine learning methods, autonomous approaches are recognized to have great potential for performing more efficient scattering experiments. In our view, it is crucial for such approaches to provide thorough evidence about respective performance improvements in order to increase acceptance within a scientific community. Therefore, we propose a benchmarking procedure designed as a cost-benefit analysis that is applicable to any scattering method sequentially collecting data during an experiment. For a given approach, the performance assessment is based on how much benefit, given a certain cost budget, it is able to acquire in predefined test cases. Different approaches thus get a chance for comparison and can make their advantages explicit and visible. Key components of the procedure, i.e., cost measures, benefit measures, and test cases, are made precise for the setting of three-axes spectrometry (TAS) as an illustration. Finally, we discuss neglected aspects and possible extensions for the TAS setting and comment on the procedure’s applicability to other scattering methods. A Python implementation of the procedure to simplify its utilization by interested researchers from the field is also provided.
ASE has successfully demonstrated that a spacecraft can be driven by science analysis and autonomously controlled. ASE is available for flight on other missions. Mission hardware design should consider ASE requirements for available onboard data storage, onboard memory size and processor speed.
The need for higher level of spacecraft autonomy is gaining increased importance in future space missions planning.
We develop integrated instruments and platforms suitable for economical, frequent space access for autonomous life science experiments and processes in outer space. The technologies represented by three of our recent free-flyer small-satellite missions are the basis of a rapidly growing toolbox of miniaturized biologically/biochemically-oriented instrumentation now enabling a new generation of in-situ space experiments. Autonomous small satellites (~ 1 50 kg) are less expensive to develop and build than fullsize spacecraft and not subject to the comparatively high costs and scheduling challenges of human-tended experimentation on the International Space Station, Space Shuttle, and comparable platforms. A growing number of commercial, government, military, and civilian space launches now carry small secondary science payloads at far lower cost than dedicated missions; the number of opportunities is particularly large for so-called cube-sat and multicube satellites in the 1 10 kg range. The recent explosion in nano-, micro-, and miniature technologies, spanning fields from telecommunications to materials to bio/chemical analysis, enables development of remarkably capable autonomous miniaturized instruments to accomplish remote biological experimentation. High-throughput drug discovery, point-of-care medical diagnostics, and genetic analysis are applications driving rapid progress in autonomous bioanalytical technology. Three of our recent missions exemplify the development of miniaturized analytical payload instrumentation: GeneSat-1 (launched: December 2006), PharmaSat (launched: May 2009), and O/OREOS (organism/organics exposure to orbital stresses; scheduled launch: May 2010). We will highlight the overall architecture and integration of fluidic, optical, sensor, thermal, and electronic technologies and subsystems to support and monitor the growth of microorganisms in culture in these small autonomous space satellites, including real-time tracking of their culture density, gene expression, and metabolic activity while in the space environment. Flight data and results will be presented from GeneSat-1, which tracked gene expression levels of GFP-labeled E. coli and from PharmaSat, which monitored the dose dependency of an antifungal agent against S. cerevisiae. The O/OREOS SESLO instrument, which will study the effects of radiation and microgravity upon the viability and growth characteristics of B. subtilis and the halophile Halorubrum chaoviatoris for periods of 0 - 6 months in space, will be described as well. The ongoing expansion of the small satellite toolbox of biological technologies will be summarized.
Mars Express (MEX), Rosetta and Venus Express (VEX) are ESA interplanetary spacecrafts (S/C) launched in June 2003, March 2004 and November 2005, respectively. Mars Express was injected into Mars orbit end of 2003 with routine operations starting in spring 2004. Rosetta is since launch on its way to rendezvous comet Churyumov-Gerasimenko in 2014. It has completed several test and commissioning activities and is performing several planetary swingbys (Earth in spring 2005, Mars in spring 2007, Earth in autumn 2007 and again two years later). Venus Express has also started routine operations since the completion of the Venus orbit insertion maneuver sequence beginning of May 2006. All three S/C are three axes stabilized with a similar attitude and orbit control system (AOCS). The attitude is estimated on board using star and rate sensors and controlled using four reaction wheels. A bipropellant reaction control system with 10N thrusters serves for wheel off loadings and attitude control in safe mode. Mars Express and Venus Express have an additional 400N engine for the planetary orbit insertion. Nominal Earth communication is accomplished through a high gain antenna. All three S/C are equipped with a redundant set of autonomous star trackers (STR) which are based on almost the same hardware. The STR software is especially adapted for the respective mission. This paper addresses several topics related to the experience gained with the STR operations on board the three S/C so far.
This paper describes the planning and execution phases of the experiment. The paper presents results obtained from the flight experiment, and shares lessons learned.
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Spacecraft autonomy is become an important aspect of the design and operation of future space missions. While science objectives continually increase, budgets for development and operations remain fairly flat.