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Akio Numakura

dblp:181/4039 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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
Motion planning and robot control · 87% Robot navigation and mapping · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
dynamics learning
0.212016
FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling · ICRA 2016
Robotics › Motion planning and robot control
system identification
0.212016
FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling · ICRA 2016
Robotics › Robot navigation and mapping › state estimation
visual state estimation
0.112016
FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling · ICRA 2016

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

online learning · 0.2image processing · 0.2dynamics learning tree · 0.2
YearPublicationVenuePosition
2016 FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling
abstract
This paper addresses the modeling and measurement of a small boat. In some fishing tasks, anchorage is not applicable in order to capture shellfishes or fishes efficiently. Currently, many fishermen are manually stabilizing boats simultaneously with the capturing task. We propose a boat modeling method named FAD learning. In this method, the free dynamics acceleration and actuator acceleration of a boat are learned using the online learning of two dynamics learning trees (DLTs), which are developed by us. In order to measure the position, velocity, and acceleration, we developed an image processing method with an underwater camera. In the experiment, the motor babbling of a boat was performed on a water pool. The dynamical data from the boat was learned by DLTs. The effectiveness of the modeling was confirmed through the validation of the velocity that was predicted by DLTs.
Akio Numakura, Shigenobu Kato, Kazuyuki Sato, Takeya Tomizawa, Tasuku Miyoshi, Takuya Akashi, Chyon Hae Kim
ICRA1