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Sam Ann Rahok

dblp:44/9506 · also Rahok Yokokawa Sam Ann · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2011
0000-0002-4462-4064ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 2 · 2 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
Robot navigation and mapping · 67% Motion planning and robot control · 33%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.112011
Play-back navigation for outdoor mobile robot using trajectory tracking based on environmental magnetic field · ICRA 2011
Robotics › Robot navigation and mapping › localization › signal-based localization
magnetic localization
0.112011
Play-back navigation for outdoor mobile robot using trajectory tracking based on environmental magnetic field · ICRA 2011
Robotics › Motion planning and robot control › robot control
trajectory tracking
0.112011
Play-back navigation for outdoor mobile robot using trajectory tracking based on environmental magnetic field · ICRA 2011

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

magnetic sensing · 0.1
YearPublicationVenuePosition
2011 Play-back navigation for outdoor mobile robot using trajectory tracking based on environmental magnetic field
abstract
This paper presents a play-back navigation for outdoor mobile robots using trajectory tracking method based on environmental magnetic field. Two 3-axis magnetic sensors with horizontal xy-axes opposed to each other, are used to scan magnetic field occurred in the environment to build two different magnetic databases during the teaching stage. In the play-back navigation, the robot rotates 180 degrees and then uses one among the two magnetic sensors readings to perform trajectory tracking based on the magnetic database of another magnetic sensor. The experimental results show that the robot is able to perform the play-back navigation with a reliable accuracy.
Sam Ann Rahok, Koichi Ozaki
ICRA1
2010 Trajectory tracking using environmental magnetic field for outdoor autonomous mobile robots
abstract
This paper describes a trajectory tracking method using environmental magnetic field for outdoor autonomous mobile robots. In this research, a 3-axis magnetic sensor is used to scan DC magnetic field in the outdoor environment to build a database. The robot then performs trajectory tracking based on the database. The experimental results show that by applying the proposed method the robot is possibly able to navigate in the outdoor environment with a reliable accuracy.
Sam Ann Rahok, Yoshihito Shikanai, Koichi Ozaki
IROS1