The Onboard Autonomous Science Investigation System has been developed to enable a rover to identify and react to serendipitous science opportunities. Using the FIDO rover in the Mars Yard at JPL, we have successfully demonstrated a fully autonomous opportunistic science system. The closed loop system tests included the rover acquiring image data, finding rocks in the image, analyzing rock properties and identifying rocks that merit further investigation. When the system on the rover alerts the rover to take additional measurements of interesting rocks, the planning and scheduling component determines if there are enough resources to meet this additional science data request. The rover is then instructed to either turn toward the rock, or to actually move closer to the rock to take an additional, close-up image. Prototype dust devil and cloud detection algorithms were delivered to an infusion task which refined the algorithms specifically for Mars Exploration Rovers ͑MER͒. These algorithms have been integrated into the MER flight software and were recently uploaded to the rovers on Mars.

Abstract-The Onboard Autonomous Science Investigation System (OASIS) was used in the first formal demonstration of closed loop opportunistic detection and reaction during a rover traverse on the FIDO rover at NASA's Jet Propulsion Laboratory. In addition to hardware demonstrations, the system has been demonstrated and exercised in simulation using the Rover Analysis, Modeling, and Simulation (ROAMS) planetary rover simulator [1]. We discuss several system enhancements including new planning and scheduling capabilities and image prioritization. We also describe the new end-of-traverse capability that includes taking a partial panorama of images, assessing these for targets of interest, and collecting narrow angle images of selected targets. Finally, we present several methods for estimating properties of rocks and provide a comparative assessment. Understanding the relationship of these methods is important to correctly interpret autonomous rock analyses performed during a traverse.

The goal of the Onboard Autonomous Science Investigation System (OASIS) project at NASA's Jet Propulsion Laboratory (JPL) is to evaluate, and autonomously act upon, science data gathered by in-situ spacecraft, such as planetary landers and rovers. 1,2 Using the FIDO rover in the Mars Yard at JPL, we have successfully demonstrated a closed loop system test of the rover acquiring image data, finding rocks in the image, analyzing rock properties and identifying rocks that merit further investigation. When the system on the rover alerts the rover to take additional measurements of interesting rocks, the planning and scheduling component determines if there are enough resources to meet this additional science data request. The rover is then instructed to either turn toward the rock, or to actually move closer to the rock to take an additional, close up, picture. In addition to these hardware integration successes, the OASIS team has also continued its autonomous science research by collaboratively working with other scientists and technologists to identify and react to other scientific phenomena -such as clouds and dust devils. Prototype dust devil and cloud detection algorithms were delivered to an infusion task which has refined the algorithms specifically for Mars Exploration Rovers (MER) and is integrating the code into the next release of MER flight software.

Abstract-This paper presents technology for performing autonomous commanding of a planetary rover. Through the use of AI planning, scheduling and execution techniques, the OASIS autonomous science system provides capabilities for the automated generation of a rover activity plan based on science priorities, the handling of opportunistic science, including new science targets identified by onboard data analysis software, other dynamic decision-making such as modifying the rover activity plan in response to problems or other state and resource changes. We first describe some of the particular challenges this work has begun to address and then describe our system approach. Finally, we report on our experience testing this software with a Mars rover prototype.

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