Tara Estlin

OASIS: Onboard Autonomous Science Investigation System for Opportunistic Rover Science

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 (MER). These algorithms have been integrated into the MER flight software and were recently uploaded to the rovers on Mars.

Rover traverse science for increased mission science return

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. Ths circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gather by the 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. Although our techniques are applicable to a wide range of data modalities, our initial emphasis has focused on image analysis, since images consume a large percentage of downlink bandwidth. We have fkther focused our foundational work on rocks. Rocks are among the primary features populating the Martian landscape. Characterization and understanding of rocks on the surface is a-first step leading towards more complex in situ regional geological assessmeats by the rover. IEEEAC paper #1267, Updated November 3,2002 TABLE OF CONTENTS

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

Autonomous onboard traverse science system

The Onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing data onboard, and identifying and reacting to unanticipated science opportunities. Rovers of the future will have the capacity to collect more data than can be downlinked back to Earth. OASIS can increase mission science return by carefully selecting the data with the highest science interest for downlink. These rovers may also be required to traverse long distances with little to no interaction with the science team on Earth. OASIS can act as a geologists assistant and can autonomously direct the rover to take additional measurements of interesting rocks. The importance of characterizing the terrain along these traverses, a study that is now becoming known as traverse science, increases with the distances the rover must travel. This paper provides a brief overview of the entire OASIS system and how it analyzes one type of data - grayscale images taken by the rover for engineering and hazard avoidance purposes. Although the OASIS system can apply the same type of analysis to different data types, such as color images, hyperspectral images or point spectrometer data, we will only focus on grayscale images here. The paper also describes the latest advances in two key aspects of the system: image prioritization and the science alert. In image prioritization, we combine the results from three distinct prioritization methods to arrive at an overall downlink ranking of the images collected during a traverse. The science alert is a capability that enables the rover to identify and react to a pre-specified, and scientifically significant, signature. Once this signature has been detected via the onboard science analysis component, the planning and scheduling module updates the rover command sequence to stop the traverse and signal Earth of the find. If there is sufficient time and onboard resources before the next downlink opportunity, additional data samples of the target may be autonomously collected.

Onboard Autonomous Rover Science

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 NASAs 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, A. Jain et al (2003). 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.

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.