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Sjigeo Ohtake

dblp:82/1253 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1995
—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 · 44% Robot manipulation · 44% Multi-agent systems · 13%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
redundant manipulator control
0.011995
Shape Control of Hyper Redundant Manipulator · ICRA 1995
Robotics › Robot manipulation › deformable object manipulation
shape control
0.011995
Shape Control of Hyper Redundant Manipulator · ICRA 1995
Knowledge, reasoning and agents › Multi-agent systems
swarm intelligence
0.011995
Shape Control of Hyper Redundant Manipulator · ICRA 1995

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

distributed control law · 0.0
YearPublicationVenuePosition
1995 Shape Control of Hyper Redundant Manipulator
abstract
This paper shows a control law to let hyper redundant manipulators be in specified shapes. Such shape controls are necessary for whole arm manipulation, crevice penetrations and obstacle avoidance. The proposed control is complete local-wise law. Each controller can determine its joint motion by itself while every controller has the same hardware and common control rule with the same parameters. Thus, the set of these distributed local controllers are just as a swarm intelligence. This type of manipulators are easily implemented, easily expandable and robust against partial damages.
Hisato Kobayashi, Sjigeo Ohtake
ICRA2