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Michele Judd

dblp:25/5538 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Planning, search and constraint satisfaction · 88% Legged, aerial and field robots · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
planning and scheduling
0.112007
Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution · ICRA 2007
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › scheduling
rover activity planning
0.112007
Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution · ICRA 2007
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
autonomous science
0.012007
Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution · ICRA 2007
Robotics › Legged, aerial and field robots › field robotics
planetary rover
0.012007
Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution · ICRA 2007

Methods — techniques the papers use, named apart from their topics

scheduling · 0.1execution · 0.1AI planning · 0.1
YearPublicationVenuePosition
2012 AEGIS Automated Science Targeting for the MER Opportunity Rover
abstract
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.
Tara A. Estlin, Benjamin J. Bornstein, Daniel M. Gaines, Robert C. Anderson, David R. Thompson 0001, Michael C. Burl, Rebecca Castaño, Michele Judd
ACM Trans. Intell. Syst. Technol.8
2007 Increased Mars Rover Autonomy using AI Planning, Scheduling and Execution
abstract
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.
Tara A. Estlin, Daniel M. Gaines, Caroline Chouinard, Rebecca Castaño, Benjamin J. Bornstein, Michele Judd, Issa A. D. Nesnas, Robert C. Anderson
ICRA6