Michele Judd

AEGIS Automated Science Targeting for the MER Opportunity Rover

The 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 has successfully demonstrated 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 article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

Current results from a rover science data analysis system

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for use on future missions. In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars exploration rover (MER) images. These components include the rock-finding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rock-finders performance.

Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution

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.

Enabling autonomous rover science through dynamic planning and scheduling

With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase allows not only the collection of more science data, but enables a number of new and different science collection opportunities. Current mission operations, such as that on the 2003 Mars exploration rovers (MER), require all rover commands to be determined on the ground, which is a time-consuming and largely manual process. However, many science opportunities can be efficiently handled by performing intelligent decision-making onboard the rover itself. This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly oversubscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.

Opportunistic rover science: finding and reacting to rocks, clouds and dust devils

The goal of the Onboard Autonomous Science Investigation System (OASIS) project at NASAs Jet Propulsion Laboratory (JPL) is to evaluate, and autonomously act upon, science data gathered by in-situ spacecraft, such as planetary landers and rovers. 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

Enabling Autonomous Science for a Mars Rover

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify and react to new science opportunities. A planning and scheduling component of the system enables the rover to take advantage of identified science opportunities. In this paper, we provide an overview of the OASIS system and report on our experience testing this software with a Mars rover prototype. In particular we discuss how such capabilities can be enabled during ground operations planning and how this increased autonomy will affect downlinked data. We also introduce a new area of OASIS work, which is to provide autonomous targeting capabilities for the MER rovers.

Experiments in Onboard Rover Traverse Science

The onboard autonomous science investigation system (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of identified science opportunities. In this paper, we provide a brief overview of the entire OASIS system, and then describe new system capabilities with an emphasis on the identification of novel features during a traverse. This capability has been integrated into the full system and validated in field testing. In addition, the system has been integrated with the visual target tracking (VTT) capability recently uploaded to the Mars exploration rovers. VTT enables the system to robustly track a specified target. By integrating this with the autonomous science system, the rover can approach targets identified onboard and acquire targeted measurements both from additional viewing angles as well as from positions in close proximity to the target.

Automated Decision-Making for Mars Rover Onboard Science

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.