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David Battle

dblp:09/599 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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% Motion planning and robot control · 46% Optimization for machine learning · 7%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › underwater robotics
autonomous underwater vehicle
0.112007
Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array · ICRA 2007
Robotics › Motion planning and robot control › robot control
behavior-based control
0.112007
Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array · ICRA 2007
Robotics › Motion planning and robot control
robot control
0.112007
Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array · ICRA 2007
Robotics › Legged, aerial and field robots
underwater robotics
0.112007
Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array · ICRA 2007
Machine learning › Optimization for machine learning
multi-objective optimization
0.012007
Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array · ICRA 2007

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

multi-objective optimization · 0.1interval programming · 0.1behavior-based control · 0.1
YearPublicationVenuePosition
2007 Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array
abstract
This paper is about the autonomous control of an autonomous underwater vehicle (AUV), and the particular considerations required to allow proper control while towing a 100-meter vector sensor array. Mission related objectives are tempered by the need to consider the effect of a sequence of maneuvers on the motion of the towed array which is thought not to tolerate sharp bends or twists in sensitive material. We describe and motivate an architecture for autonomy structured on the behavior-based control model augmented with a novel approach for performing behavior coordination using multi-objective optimization. We provide detailed in-field experimental results from recent exercises with two 21-inch AUVs in Monterey Bay California.
Michael R. Benjamin, David Battle, Donald P. Eickstedt, Henrik Schmidt, Arjuna P. Balasuriya
ICRA2