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Tomohiro Chaki

dblp:314/1767 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

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 · 67% Robot manipulation · 33%
Theoretical computer science
1 paper
Mathematical optimization · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
dual-arm manipulation
0.812024
Quadratic Programming Based Inverse Kinematics for Precise Bimanual Manipulation · ICRA 2024
Robotics › Motion planning and robot control › robot control
inverse kinematics
0.812024
Quadratic Programming Based Inverse Kinematics for Precise Bimanual Manipulation · ICRA 2024
Robotics › Motion planning and robot control
robot control
0.812024
Quadratic Programming Based Inverse Kinematics for Precise Bimanual Manipulation · ICRA 2024
Mathematical optimization › continuous optimization › nonlinear optimization
quadratic programming
0.212024
Quadratic Programming Based Inverse Kinematics for Precise Bimanual Manipulation · ICRA 2024

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

quadratic programming · 1.5null space projection · 1.5
YearPublicationVenuePosition
2024 Quadratic Programming Based Inverse Kinematics for Precise Bimanual Manipulation
abstract
We discuss the precise cooperative motion of a dual manipulator. In the inverse kinematics of cooperative redundant manipulators, a hierarchical method using null space and an optimization method prioritizing the end-effectors relative position in the objective function have been proposed. However, there is no guarantee that the relative position will be maintained in regions subject to joint limits and task-space reachability constraints. As a result, unacceptable errors may occur, and some tasks cannot be accomplished. We propose designing the maximum permissible errors in advance by expressing the target relative position as inequality constraints in the Quadratic Programming (QP) problem. By extending its description to include a virtual spring, we have also achieved subtle force application by two cooperated manipulators. The proposed method was verified by simulation and experiments.
Tomohiro Chaki, Tomohiro Kawakami
ICRA1