Rebecca Castano

Using Autonomy Flight Software to Improve Science Return on Earth Observing One

NASA’s Earth Observing One Spacecraft (EO-1) has been adapted to host an advanced suite of onboard autonomy software designed to dramatically improve the quality and timeliness of science-data returned from remote-sensing missions. The Autonomous Sciencecraft Experiment (ASE) enables the spacecraft to autonomously detect and respond to dynamic scientifically interesting events observed from EO-1’s low earth orbit. ASE includes software systems that perform science data analysis, mission planning, and runtime robust execution. In this article we describe the autonomy flight software, as well as innovative solutions to the challenges presented by autonomy, reliability, and limited computing resources.

An autonomous earth-observing sensorWeb

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the system of systems.

The EO-1 Autonomous Science Agent

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 the constraints and how they were addressed in our agent design, validation, and deployment.

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.

AUTONOMOUS ROCK DETECTION FOR MARS TERRAIN

In this paper we introduce a general framework for an image based autonomous rock detection process for Martian terrain. A rock detection algorithm, based on this framework, is described and demonstrated on examples of real Mars Rover data. An attempt is made to produce a system that is independent of parameters to ease on-board implementation for real time insitu operation. The process utilizes unsupervised hierarchical approaches for object detection and is easily expandable to more complex data sets. Currently, it uses intensity information to detect small rocks and range information (derived from a pair of intensity images) to detect large rocks in the image. The range-based and intensity-based algorithms tend to be complimentary, with one working when the other fails, together they detect most of the rocks in Mars images. The Rock Detection System presented in this paper is one module in autonomous exploration system. This module closes the loop between data acquisition, data analysis and decision-making in situ. It can be used to prioritize what information will be sent back to Earth, where to take more scientific measurements using more time-consuming instrumentation, and which surface regions to explore further. In this manner the system contributes to reducing data downlink and maximizing science return per bit of data.

Automatic detection of dust devils and clouds on Mars

The acquisition of science data in space applications is shifting from teleoperated data collection to an automated onboard analysis, resulting in improved data quality, as well as improved usage of limited resources such as onboard memory, CPU, and communications bandwidth. Science instruments onboard a modern deep-space spacecraft can acquire much more data that can be downloaded to Earth, given the limited communication bandwidth. Onboard data analysis offers a means of compressing the huge amounts of data collected and downloading only the most valuable subset of the collected data. In this paper, we describe algorithms for detecting dust devils and clouds onboard Mars rovers, and summarize the results. These algorithms achieve the accuracy required by planetary scientists, as well as the runtime, CPU, memory, and bandwidth constraints set by the engineering mission parameters. The detectors have been uploaded to the Mars Exploration Rovers, and currently are operational. These detectors are the first onboard science analysis processes on Mars.

Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles

We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize performance (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that are discussed in this paper. We detect and segment (label) obstacles using a novel 3D obstacle algorithm. The material of each labelled obstacle (rock, vegetation, etc) is then determined using a texture or color classification scheme. Terrain load-bearing surface models are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dynamics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be useful in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented.

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.

The Techsat-21 autonomous space science agent

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.