Autonomous
Sciencecraft
Experiment

Co-Winner: 2005 NASA Software of the Year Award

Mission Status: Check on the latest totals of ASE

Background

Since the dawn of the space age, unmanned spacecraft have flown blind with little or no ability to make autonomous decisions based on the content of the data they collect. The Autonomous Sciencecraft Experiment (ASE) is operating onboard the Earth Observing-1 mission since 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. This software demonstrates the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data.

AI Technology

The ASE onboard flight software includes several autonomy software components:

Onboard science algorithms

that analyzes the image data to detect trigger conditions such as science events, interesting features, changes relative to previous observations, and cloud detection for onboard image editing

Robust execution management software

using the Spacecraft Command Language (SCL) package to enable event-driven processing and low-level autonomy

Continuous Activity Scheduling Planning Execution and Replanning (CASPER) software

that replan activities, including downlink, based on science observations in the previous orbit cycles

Tracking Europa Surface Ice

The onboard science algorithms analyzes the images to extract static features and detect changes relative to previous observations. Applied to EO-1 Hyperion data, these algorithms automatically identify regions of interest including regions of change (such as flooding, ice melt, and lava flows). Using these algorithms onboard enables retargeting and search, e.g., retargeting the instrument on a subsequent orbit cycle to identify and capture the full extent of a flood. On future interplanetary space missions, onboard science analysis will enable capture of short-lived science phenomena at the finest time-scales without overwhelming onboard memory or downlink capacities. Examples include: eruption of volcanoes on Io, formation of jets on comets, and phase transitions in ring systems. Generation of derived science products (e.g., boundary descriptions, catalogs) and change-based triggering will also reduce data volumes to a manageable level for extended duration missions that study long-term phenomena such as atmospheric changes at Jupiter and flexing and cracking of the ice crust on Europa.

The onboard planner (CASPER) generates mission operations plans from goals provided by the onboard science analysis module. The model-based planning algorithms enables rapid response to a wide range of operations scenarios based on a deep model of spacecraft constraints, including faster recovery from spacecraft anomalies. The onboard planner accepts as inputs the science and engineering goals and ensure high-level goal-oriented behavior.

The robust execution system (SCL) accepts the CASPER-derived plan as an input and expands the plan into low-level commands. SCL monitors the execution of the plan and has the flexibility and knowledge to perform event-driven commanding to enable local improvements in execution as well as local responses to anomalies.

Problem

Constrained downlink resources limit the science return of current and future space missions.

Impact

Short-Lived Eruption on Io

Demonstration of these capabilities in a flight environment opens up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology. This technology:

• Dramatically increases the science per fixed downlink by enabling downlink of the highest priority science data.
• Enables study of short-lived science events (such as volanic eruptions, dust storms, etc.)
• Reduces downtime lost to anomalies due to robust execution enabled by autonomy software.
• Reduces instrument setup time by using autonomy software take advantage of execution information to streamline operations.

Status

			Mission		Last Week		Yesterday				Upcoming
	Images Taken		16949		105		15				22
	Sensorweb		2177		7		3				0
	Science Scenarios Executed		1452		1		0				0
	Positive Triggers		257		0		0
	Ground Contacts		16430		102		16				17
	X-Band		5577		34		5				6
	S-Band		10853		68		11				11
	Planner Goals		131456		698		119				120

Description

+ ASE Mission Concept Animation

A typical ASE demonstration scenario involves monitoring of active volcano regions such as Mt. Etna in Italy. Hyperion data have been used in ground-based analysis to study this phenomenon. The ASE concept is applied as follows:

1. Initially, ASE has a list of science targets to monitor that have been sent as high-level goals from the ground.
2. As part of normal operations, CASPER generates a plan to monitor the targets on this list by periodically imaging them with the Hyperion instrument. For volcanic studies, the IR and near IR bands are used.
3. During execution of this plan, the EO-1 spacecraft images Mt. Etna with the Hyperion instrument.
4. The onboard science algorithms analyzes the image and detects a fresh lava flow. Based on this detection the image is downlinked. Had no new lava flow been detected, the science software would generate a goal for the planner to acquire the next highest priority target in the list of targets. The addition of this goal to the current goal set triggers CASPER to modify the current operations plan to include numerous new activities in order to enable the new science observation.
5. The SCL software executes the CASPER generated plans in conjunction with several autonomy elements.
6. This cycle is then repeated on subsequent observations.

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