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Yuzo Maeshima

dblp:151/9507 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 manipulation · 56% Motion planning and robot control · 44%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
parameter identification
0.212014
A new method for parameter identification for N-DOF hydraulic robots · ICRA 2014
Robotics › Motion planning and robot control
robot control
0.212014
A new method for parameter identification for N-DOF hydraulic robots · ICRA 2014
Robotics › Robot manipulation › robot design › manipulator design
hydraulic manipulator
0.112014
A new method for parameter identification for N-DOF hydraulic robots · ICRA 2014

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

model validation · 0.2
YearPublicationVenuePosition
2014 A new method for parameter identification for N-DOF hydraulic robots
abstract
Parameter identification for N-DOF hydraulic robots (e.g., BigDog, PETMAN and developing automated excavators) is not an old problem at all. Indeed, recent papers still identify difficult parameters (the bulk modulus and the flow gain coefficients) by trial and error. This paper is the first report to solve this problem. First, new structural properties of hydraulic robots are found in comparison with electric robots and then the drawbacks of the conventional methods are discussed. Second, by modifying one of the conventional methods, a scientific method is proposed and applied to an actual hydraulic manipulator. Third, the validity of the proposed method is confirmed by model validation. Remarkably, the difficult parameters are identified very well without any trial and error. The proposed method is applicable to electric robots even on the unknown inclination.
Satoru Sakai, Yuzo Maeshima
ICRA2