MISUS: Multi-rover integrated science understanding system
Explore the source record for details and available documents.
data analysis↗
Engineering topics
Estlin, Tara
Publications and source records attributed to Estlin, Tara.
Intelligent decision making for autonomous rover operations
Explore the source record for details and available documents.
rover operations↗
Demonstrating Robotic Autonomy in NASA's Intelligent Systems Project
This paper will provide an overview of NASA's investments in autonomy during the past five years within the Intelligent Systems Project, with particular attention paid to investments that have resulted in mission infusion of autonomy technology, in particular, into the recent Mars Exploration Rover (MER) mission. The content of the paper will be divided into two primary topic areas: a technical overview of the component technologies developed under the program, and a programmatic overview of the history and organization of the NASA IS project itself, with a focus on describing the program elements related to autonomy and intelligent robotics. The paper will also provide an overview of the September 2004 autonomy demonstrations, including a discussion of objectives, organization, and preliminary results (to the extent they are available before the submission deadline).
Morris, Robert↗
Rover traverse science for increased mission science return
In this paper, we will describe out methods for the prioritization of geologic data acquired by an in-situ rover. Our techniques are applicable to a wide range of data modalites, however out initial demonstration is focused on image analysis, as images consume a large volume of the downlink bandwidth for such missions.
data prioritization↗
CLARAty Functional-Layer Software
Functional-layer software for the Coupled Layer Architecture for Robotics Autonomy (CLARAty) is being developed. [CLARAty was described in Coupled-Layer Architecture for Advanced Software for Robots (NPO-21218), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48. To recapitulate: CLARAty was proposed to improve the modularity of robotic software while tightening the coupling between planning/execution and control subsystems. Whereas prior robotic software architectures have typically contained three levels, the CLARAty architecture contains two layers: a decision layer and a functional layer.] Just as an operating system provides abstraction from computational hardware, the CLARAty functional-layer software provides for abstraction for the different robotic systems. The functional-layer software establishes interrelated, object-oriented hierarchies that contain active and passive objects that represent the different levels of system abstrations and components. The functional-layer software is decomposed into a set of reusable core components and a set of extended components that adapt the reusable set to specific hardware implementations. The reusable components (a) provide behavior and interface definitions and implementations of basic functionality, (b) provide local executive capabilities, (c) manage local resources, and (d) support state and resource queries by the decision layer. Software for robotic systems can be built by use of these components.
Nesnas, Issa↗
Autonomously generating operations sequences for a Mars Rover using AI-based planning
This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from highlevel science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules.
Mars Rover↗
An integrated planning and scheduling prototype for automated Mars Rover Command Generation
This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level goals using AI-based planning software.
planning↗
Automated Planning for a Deep Space Communications Station
This paper describes the application of Artificial Intelligence planning techniques to the problem of antenna track plan generation for a NASA Deep Space Communications Station. Me described system enables an antenna communications station to automatically respond to a set of tracking goals by correctly configuring the appropriate hardware and software to provide the requested communication services. To perform this task, the Automated Scheduling and Planning Environment (ASPEN) has been applied to automatically produce antenna trucking plans that are tailored to support a set of input goals. In this paper, we describe the antenna automation problem, the ASPEN planning and scheduling system, how ASPEN is used to generate antenna track plans, the results of several technology demonstrations, and future work utilizing dynamic planning technology.
Estlin, Tara↗
An AI Approach to Ground Station Autonomy for Deep Space Communications
This paper describes an architecture for an autonomous deep space tracking station (DS-T). The architecture targets fully automated routine operations encompassing scheduling and resource allocation, antenna and receiver predict generation. track procedure generation from service requests, and closed loop control and error recovery for the station subsystems. This architecture has been validated by the construction of a prototype DS-T station, which has performed a series of demonstrations of autonomous ground station control for downlink services with NASA's Mars Global Surveyor (MGS).
Fisher, Forest↗