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Masanori Onishi

dblp:162/0952 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1991
—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
Motion planning and robot control · 100%
Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning		 0.011991
Locomotion functions in the mobile robot language, MML · ICRA 1991
Robotics › Motion planning and robot control › robot task specification
path specification		 0.011991
Locomotion functions in the mobile robot language, MML · ICRA 1991
Programming languages and type systems
domain-specific languages		 0.011991
Locomotion functions in the mobile robot language, MML · ICRA 1991
Robotics › Motion planning and robot control
robot kinematics		 0.011991
Locomotion functions in the mobile robot language, MML · ICRA 1991

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

coordinate transformation · 0.0
YearPublicationVenuePosition
1991 Locomotion functions in the mobile robot language, MML
abstract
A few fundamental concepts useful for building a high-level mobile robot language are discussed. The method of describing a vehicle path by a sequence of configurations, the use of coordination transformation (or composition of configurations), and the concept of symmetric configurations are examined, with programming examples and the resultant paths. These concepts are robot independent and can be applied to the kernel of any high-level mobile robot language in the future. These functions have already been successfully implemented in the Model-based Mobile Robot Language (MML).>
Yutaka Kanayama, Masanori Onishi
 ICRA2