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Estlin, Tara

Publications and source records attributed to Estlin, Tara.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

CASPER Version 2.0

CASPER is designed to perform automated planning of interdependent activities within a system subject to requirements, constraints, and limitations on resources. In contradistinction to the traditional concept of batch planning followed by execution, CASPER implements a concept of continuous planning and replanning in response to unanticipated changes (including failures), integrated with execution. Improvements over other, similar software that have been incorporated into CASPER version 2.0 include an enhanced executable interface to facilitate integration with a wide range of execution software systems and supporting software libraries; features to support execution while reasoning about urgency, importance, and impending deadlines; features that enable accommodation to a wide range of computing environments that include various central processing units and random- access-memory capacities; and improved generic time-server and time-control features.

Chien, Steve↗
NASA NTRS2006

Opportunistic Planning and Execution for Planetary Exploration

We are developing technologies to increase the autonomous capabilities of future rover missions. Our objectives are to make rovers easier to command and to enable them to make more effective use of rover resources when problems arise or when things go better than expected. We will demonstrate OASIS (Onboard Analysis Science Investigation System) which combined planning and scheduling techniques with machine learning to enable rovers to perform robust and opportunistic science operations.

autonomous planning↗
NASA NTRS2006

Plan Execution Interchange Language (PLEXIL)

Plan execution is a cornerstone of spacecraft operations, irrespective of whether the plans to be executed are generated on board the spacecraft or on the ground. Plan execution frameworks vary greatly, due to both different capabilities of the execution systems, and relations to associated decision-making frameworks. The latter dependency has made the reuse of execution and planning frameworks more difficult, and has all but precluded information sharing between different execution and decision-making systems. As a step in the direction of addressing some of these issues, a general plan execution language, called the Plan Execution Interchange Language (PLEXIL), is being developed. PLEXIL is capable of expressing concepts used by many high-level automated planners and hence provides an interface to multiple planners. PLEXIL includes a domain description that specifies command types, expansions, constraints, etc., as well as feedback to the higher-level decision-making capabilities. This document describes the grammar and semantics of PLEXIL. It includes a graphical depiction of this grammar and illustrative rover scenarios. It also outlines ongoing work on implementing a universal execution system, based on PLEXIL, using state-of-the-art rover functional interfaces and planners as test cases.

Estlin, Tara↗
NASA NTRS2006

A Robust Compositional Architecture for Autonomous Systems

Space exploration applications can benefit greatly from autonomous systems. Great distances, limited communications and high costs make direct operations impossible while mandating operations reliability and efficiency beyond what traditional commanding can provide. Autonomous systems can improve reliability and enhance spacecraft capability significantly. However, there is reluctance to utilizing autonomous systems. In part this is due to general hesitation about new technologies, but a more tangible concern is that of reliability of predictability of autonomous software. In this paper, we describe ongoing work aimed at increasing robustness and predictability of autonomous software, with the ultimate goal of building trust in such systems. The work combines state-of-the-art technologies and capabilities in autonomous systems with advanced validation and synthesis techniques. The focus of this paper is on the autonomous system architecture that has been defined, and on how it enables the application of validation techniques for resulting autonomous systems.

Brat, Guillaume↗
NASA NTRS2005

Grid Visualization Tool

The Grid Visualization Tool (GVT) is a computer program for displaying the path of a mobile robotic explorer (rover) on a terrain map. The GVT reads a map-data file in either portable graymap (PGM) or portable pixmap (PPM) format, representing a gray-scale or color map image, respectively. The GVT also accepts input from path-planning and activity-planning software. From these inputs, the GVT generates a map overlaid with one or more rover path(s), waypoints, locations of targets to be explored, and/or target-status information (indicating success or failure in exploring each target). The display can also indicate different types of paths or path segments, such as the path actually traveled versus a planned path or the path traveled to the present position versus planned future movement along a path. The program provides for updating of the display in real time to facilitate visualization of progress. The size of the display and the map scale can be changed as desired by the user. The GVT was written in the C++ language using the Open Graphics Library (OpenGL) software. It has been compiled for both Sun Solaris and Linux operating systems.

Chouinard, Caroline↗
NASA NTRS2005

Software for Automation of Real-Time Agents, Version 2

Version 2 of Closed Loop Execution and Recovery (CLEaR) has been developed. CLEaR is an artificial intelligence computer program for use in planning and execution of actions of autonomous agents, including, for example, Deep Space Network (DSN) antenna ground stations, robotic exploratory ground vehicles (rovers), robotic aircraft (UAVs), and robotic spacecraft. CLEaR automates the generation and execution of command sequences, monitoring the sequence execution, and modifying the command sequence in response to execution deviations and failures as well as new goals for the agent to achieve. The development of CLEaR has focused on the unification of planning and execution to increase the ability of the autonomous agent to perform under tight resource and time constraints coupled with uncertainty in how much of resources and time will be required to perform a task. This unification is realized by extending the traditional three-tier robotic control architecture by increasing the interaction between the software components that perform deliberation and reactive functions. The increase in interaction reduces the need to replan, enables earlier detection of the need to replan, and enables replanning to occur before an agent enters a state of failure.

Fisher, Forest↗
NASA NTRS2005

Software for Optimizing Plans Involving Interdependent Goals

A computer program enables construction and optimization of plans for activities that are directed toward achievement of goals that are interdependent. Goal interdependence is defined as the achievement of one or more goals affecting the desirability or priority of achieving one or more other goals. This program is overlaid on the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been described in a number of prior NASA Tech Briefs articles. Unlike other known or related planning programs, this program considers interdependences among goals that can change between problems and provides a language for easily specifying such dependences. Specifications of the interdependences can be formulated dynamically and provided to the associated planning software as part of the goal input. Then an optimization algorithm provided by this program enables the planning software to reason about the interdependences and incorporate them into an overall objective function that it uses to rate the quality of a plan under construction and to direct its optimization search. In tests on a series of problems of planning geological experiments by a team of instrumented robotic vehicles (rovers) on new terrain, this program was found to enhance plan quality.

Estlin, Tara↗
NASA NTRS2005

Robust and Opportunistic Planning for Planetary Exploration

This slide presentation discusses the use of robust planning for rover vehicles to enable planetary exploration. The objective of this work is to use onboard planning, scheduling and execution techniques to enable a rover to take autonomous action to take advantage of new opportunities, and to respond to unexpected problems, and to improve the overall utilization of rover resources.

onboard science analysis↗
NASA NTRS2005

Centralized Planning for Multiple Exploratory Robots

A computer program automatically generates plans for a group of robotic vehicles (rovers) engaged in geological exploration of terrain. The program rapidly generates multiple command sequences that can be executed simultaneously by the rovers. Starting from a set of high-level goals, the program creates a sequence of commands for each rover while respecting hardware constraints and limitations on resources of each rover and of hardware (e.g., a radio communication terminal) shared by all the rovers. First, a separate model of each rover is loaded into a centralized planning subprogram. The centralized planning software uses the models of the rovers plus an iterative repair algorithm to resolve conflicts posed by demands for resources and by constraints associated with the all the rovers and the shared hardware. During repair, heuristics are used to make planning decisions that will result in solutions that will be better and will be found faster than would otherwise be possible. In particular, techniques from prior solutions of the multiple-traveling- salesmen problem are used as heuristics to generate plans in which the paths taken by the rovers to assigned scientific targets are shorter than they would otherwise be.

Estlin, Tara↗
NASA NTRS2005

Current results from a Rover Science Data Analysis System

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 rockfinding 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 rockfinder's performance.

Coupled Layer Architecture for Robotic Autonomy (C↗
NASA NTRS2005

Survey of Command Execution Systems for NASA Spacecraft and Robots

NASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions.

Verma, Vandi↗
NASA NTRS2004

Prioritizing Scientific Data for Transmission

A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.

Castano, Rebecca↗
NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗
NASA NTRS2004

Onboard planning for geological investigations using a rover team

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-Rover Integrated Science Understanding System (MISUS) combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers.

scheduling↗
NASA NTRS2004

Intensity-based rock detection for acquiring onboard rover science

Missions to Mars already under study call for very long autonomous traverses (on the order of hundreds of meters per sol) during which there will be limited contact with Earth. The onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing science opportunities. By prioritizing data for downlink onboard, it is expected that the set of images selected for downlink by OASIS will have a consistently higher scientific interest then any set of equal number of images of the same traverse obtained using random or periodic sampling. Thus, OASIS can be used to increase the science returned from a long traverse. OASIS prioritizes data and identifies science opportunities by extracting information from image data and then using this information to prioritize the data and identify potential new science opportunities. Currently, the information extracted from the images is the location of the rocks in the image and several properties of these rocks such as size, albedo, texture and shape, In this paper we give an overview of the role of rock detection in the OASIS system and describe the functionality of the rock detector. We then present results on representative images

Judd, Michele↗