1 The EO-1 Autonomous Science Agent Architecture Steve Chien, Rob Sherwood, Daniel Tran, Benjamin Cichy, Gregg Rabideau, Rebecca Castano, Ashley Davies, Rachel Lee Jet Propulsion Laboratory, California Institute of Technology Dan Mandl, Stuart Frye 1 , Bruce Trout 2 , Jerry Hengemihle 2 , Jeff D’Agostino 3 , Seth Shulman 4 , Stephen Ungar, Thomas Brakke Goddard Space Flight Center Darrell Boyer, Jim Van Gaasbeck, Interface & Control Systems Ronald Greeley, Thomas Doggett, Arizona State University Victor Baker, James Dohm, Felipe Ip, University of Arizona Contact: steve.chien@jpl.nasa.gov Abstract— An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe these constraints and how they are reflected in our agent architecture. 1 1. INTRODUCTION The Autonomous Sciencecraft Experiment (ASE) is currently flying autonomous agent software on the Earth Observing One (EO-1) spacecraft [19]. This software demonstrates several integrated autonomy technologies to enable autonomous science. Several algorithms to detect the occurrence of science events based on remote sensing imagery analyze science data onboard. These algorithms will be used to downlink science data only on change, and will detect features of scientific interest such as volcanic eruptions, flooding, ice breakup, and presence of cloud cover. These onboard science algorithms are inputs to onboard decision- making algorithms that then modifies the spacecraft observation plan to capture high value science events. This new observation plan is then be executed by a robust goal and task oriented execution system, able to adjust the plan to succeed 1 - MitreTek, 2 - Microtel LLC, 3 – the Hammers Company, 4- Honeywell. despite run-time anomalies and uncertainties. Together these technologies enable autonomous goal-directed exploration and data acquisition to maximize science return. This paper describes the Autonomous Sciencecraft Experiment (ASE) effort to develop and deploy the Autonomous Science Agent on the Earth Observing One spacecraft. The ASE onboard flight software includes several autonomy software components: • Onboard science algorithms that will analyze the image data to detect trigger conditions such as science events, “interesting” features, changes relative to previous observations, and cloud detection for onboard image masking • Robust execution management software using the Spacecraft Command Language (SCL) [10] package to enable event-driven processing and low-level autonomy • The Continuous Activity Scheduling Planning Execution and Replanning (CASPER) [5] software that will replan activities, including downlink, based on science observations in the previous orbit cycles The onboard science algorithms will analyze the images to extract static features and detect changes relative to previous observations. Prototype software has already been demonstrated on EO-1 Hyperion data to automatically identify regions of interest including land, ice, snow, water, and thermally hot areas. Repeat imagery using these