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Akira Yajima

dblp:81/2759 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

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 manipulation · 77% Motion planning and robot control · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot design › static balancing
weight compensation mechanism
0.112010
A passive weight compensation mechanism with a non-circular pulley and a spring · ICRA 2010

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

transposed jacobian analysis · 0.1
YearPublicationVenuePosition
2010 A passive weight compensation mechanism with a non-circular pulley and a spring
abstract
We propose a new weight compensation mechanism with a non-circular pulley and a spring. We show the basic principle and numerical design method to derive the shape of the non-circular pulley. After demonstration of the weight compensation for an inverted/ordinary pendulum system, we extend the same mechanism to a parallel five-bar linkage system, analyzing the required torques using transposed Jacobian matrices. Finally, we develop a three degree of freedom manipulator with relatively small output actuators and verified that the weight compensation mechanism significantly contributes to decrease static torques to keep the same posture within manipulator's work space.
Gen Endo, Hiroya Yamada, Akira Yajima, Masaru Ogata, Shigeo Hirose
ICRA3
2003 Cooperation among an intelligent environment and mobile agents by media agents
abstract
In this paper, we propose an autonomous multiagent system composed of an intelligent environment (static agents) and mobile agents (robots). We focus on procedure that autonomous mobile agents construct feasible paths. In the system, not a mobile agent but an environment gathers information and makes decisions. We formulate the optimal condition and an optimization algorithm by means of media agents that play the role of communication links between static agents. We also formulate the appropriate ratio of the media agents in the system. We discuss the efficiency of the system by results of simulations.
Daisuke Kurabayashi, Takenori Matsuoka, Akira Yajima
IROS3