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Yu Onogi

dblp:39/7019 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—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
Robot navigation and mapping · 87% Legged, aerial and field robots · 13%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot navigation
bio-inspired navigation
0.012001
Navigation Strategies referring to Insect Homing in Flying Robots · ICRA 2001
Robotics › Robot navigation and mapping › mobile robot navigation › map-based navigation
landmark-based navigation
0.012001
Navigation Strategies referring to Insect Homing in Flying Robots · ICRA 2001
Robotics › Legged, aerial and field robots
aerial robots
0.012001
Navigation Strategies referring to Insect Homing in Flying Robots · ICRA 2001

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

snapshot model · 0.0relative angle model · 0.0
YearPublicationVenuePosition
2001 Navigation Strategies referring to Insect Homing in Flying Robots
abstract
Sensor-based navigation is one of the most important researches for mobile robots domain and then there are many research works for sensor-based navigation. We propose the insects inspired landmark navigation to pinpoint the target position in this paper. There exist several research works such as snapshot model and average landmark model which request the absolute bearing information (compass) and visual one. Whereas our method called relative angle model (RAM) need only visual information in order to extract relative positions of 3 landmarks. By simulation and experiment using 3D blimp-type flying robot, we find that the pinpoint 3D navigation works out well by RAM.
Hiroshi Kobayashi, Kohki Kikuchi, Kazuhiro Ochi, Yu Onogi
ICRA4