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Jeffrey S. Seewald

dblp:274/9204 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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
Legged, aerial and field robots · 46% Robot navigation and mapping · 23% Planning, search and constraint satisfaction · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › target search
autonomous search
0.412020
Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle · ICRA 2020
Robotics › Legged, aerial and field robots › underwater robotics
autonomous underwater vehicle
0.412020
Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle · ICRA 2020
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
hierarchical search
0.412020
Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle · ICRA 2020
Robotics › Legged, aerial and field robots
underwater robotics
0.412020
Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle · ICRA 2020
Machine learning › Reinforcement learning
exploration
0.112020
Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle · ICRA 2020

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

hydrothermal plume model · 0.4
YearPublicationVenuePosition
2020 Demonstration of Autonomous Nested Search for Local Maxima Using an Unmanned Underwater Vehicle
abstract
Ocean Worlds represent one of the best chances for extra-terrestrial life in our solar system. A new mission concept must be developed to explore these oceans. This mission would require traversing the 10s of km thick icy shell and releasing a submersible into the ocean below. During the transit of the icy shell and the exploration of the ocean, the vehicle(s) would be out of contact with Earth for weeks or potentially months at a time. During this time the vehicle must have sufficient autonomy to locate and study scientific targets of interest. One such target of interest is hydrothermal venting. We have previously developed an autonomous nested search method to locate and investigate sources of hydrothermal venting by locating local maxima in hydrothermal vent emissions. In this work we demonstrate this approach on board an OceanServer Iver2 AUV in Chesapeake Bay, MD using simulated sensor data from a hydrothermal plume model. This represents the first step towards the deployment of this approach in conditions analogous to those that we might expect on an Ocean World.
Andrew Branch, James McMahon, Michael V. Jakuba, Christopher R. German, Steve A. Chien, James C. Kinsey, Andrew D. Bowen, Kevin P. Hand, Jeffrey S. Seewald
ICRA10