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ASPEN diagramASPEN (Automated Scheduling and Planning ENvironment) is a modular, re-configurable application framework based on Artificial Intelligence techniques, which is capable of supporting a variety of planning and scheduling applications including spacecraft operations planning, planning for mission design, surface rover planning, ground antenna utilization planning, and coordinated multiple rover planning. Based on AI techniques, ASPEN provides a set of reusable software components that implement the elements commonly found in complex planning/scheduling systems, including: an expressive modeling language, a resource management system, a temporal reasoning system, and a graphical interface.

As a ground based system, ASPEN uses an internal spacecraft model and set of high level goals to output a sequence of commands to be executed by the spacecraft to achieve those goals. As a flight-based system, ASPEN receives updates on spacecraft or rover state continuously and updates the current plan to reflect environment changes. As an antenna scheduling system, ASPEN has been used to autonomously control a DSN station. Automated planning/scheduling technologies have great promise in reducing operations cost and increasing the autonomy of aerospace systems.

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CASPER concept artAn autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation.

CASPER (Continuous Activity Scheduling Planning Execution and Replanning) uses iterative repair to support continuous modification and updating of a current working plan in light of changing operating context. Rather than considering planning a batch process in which a planner is presented with goals and an initial state, the planner has a current goal set, a plan, a current state, and a model of the expected future state. At any time an incremental update to the goals or current state may update the current state of the plan and thereby invoke the planner process. This update may be an unexpected event or simply time progressing forward. The planner is then responsible for maintaining a consistent, satisficing plan with the most current information. This current plan and projection is the planner's estimation as to what it expects to happen in the world if things go as expected. However, since things rarely go exactly as expected, the planner stands ready to continually modify the plan. Current iterative repair planning techniques enable incremental changes to the goals and the initial state or plan and then iteratively resolve any conflicts in the plan. After each update, its effects will be propagated through the current projections, conflicts identified, and the plan updated (e.g., plan repair algorithms invoked).

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Mars roverThe Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and continues to successfully demonstrate automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift — by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This site provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

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High Performance Space Computing

Computer processorSpaceflight computing is a natural technology multiplier for space missions.  NASA recently developed a set of use cases for future flight computing capability and derived a set of reference requirements.  NASA evaluated several computing architectures and concluded that a multicore approach would provide the greatest value to realize both a significant computational advance and flexible architectural support for the demands of space missions.  AFRL had been independently planning for a flight computing investment, and the two agencies have entered into a partnership to develop and evaluate hardware architecture designs for a future flight computing system solution.  The current phase of the joint investment is focusing on retiring technical risk.  A subsequent phase would develop a board-level flight computing system solution for validation and first mission use. The ultimate goal is to achieve a capability ten to one hundred times that of today’s generation of computing technology.

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Big Data Technologies for Radio Astronomy

Radio dishes in a desert fieldDuring the past three years JPL has been working on several technologies to deal with big data challenges facing next-generation radio arrays, among other applications. This program has focused on the following four areas:   1) Investigating high-level ASIC architectures that reduce power consumption for cross-correlation of data from large interferometer arrays by one to two orders of magnitude. The cost of operations for the Square Kilometre Array (SKA), which may be dominated by the cost of power for data processing, is a serious concern. A large improvement in correlator power efficiency could have a major positive impact. 2) Data-adaptive algorithms (machine learning) for real-time detection and classification of fast transient signals in high volume data streams (> 10X global internet traffic volume) are being developed and demonstrated. Studies of the dynamic universe, particularly searches for fast (<< 1 second) transient events, require that data be analyzed rapidly and with robust RFI rejection. JPL, in collaboration with the International Center for Radio Astronomy Research in Australia, has developed a fast transient search system for eventual deployment on ASKAP. In addition, a real-time transient detection experiment is now running continuously and commensally on NRAO's Very Long Baseline Array. 3) Scalable frameworks for data archiving, mining, and distribution are being applied to radio astronomy. A set of powerful open-source Object Oriented Data Technology (OODT) tools is now available through Apache. OODT was developed at JPL for Earth science data archives, but it is proving to be useful for radio astronomy, planetary science, health care, Earth climate, and other large-scale archives. 4) Creating automated, event-driven data visualization tools that can be used to extract information from a wide range of complex data sets. Visualization of complex data can be improved through algorithms that detect events or features of interest and autonomously generate images or video to display those features.

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