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Ming Jun Kuo

dblp:128/8406 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1994
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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
Multi-agent systems · 33% Motion planning and robot control · 33% Robot manipulation · 33%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › cooperative manipulation
cooperating manipulators
0.011994
Dynamic load-carrying capacity and inverse dynamics of multiple cooperating robotic manipulators · IEEE Trans. Robotics Autom. 1994
Robotics › Motion planning and robot control › robot dynamics
inverse dynamics
0.011994
Dynamic load-carrying capacity and inverse dynamics of multiple cooperating robotic manipulators · IEEE Trans. Robotics Autom. 1994
Knowledge, reasoning and agents › Multi-agent systems
multi-robot systems
0.011994
Dynamic load-carrying capacity and inverse dynamics of multiple cooperating robotic manipulators · IEEE Trans. Robotics Autom. 1994

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

quadratic programming · 0.0linear programming · 0.0d'alembert's principle · 0.0
YearPublicationVenuePosition
1994 Dynamic load-carrying capacity and inverse dynamics of multiple cooperating robotic manipulators
abstract
A unified method for computing the dynamic load-carrying capacity (DLCC) and the inverse dynamics of multiple cooperating robotic manipulators is developed in this paper. In this method, the kinematic constraints and the governing dynamic equations of the multi-robot system are formulated in the joint space based on a coordinate partitioning technique and the generalized D'Alembert's principle. This approach not only gives the minimum number of equations but also allows separate computations of the dynamics of the robots and that of the payload. The upper limit of the DLCC at any points on a given trajectory is obtained by solving a small-size linear programming problem. An iterative algorithm for evaluating the maximum allowable DLCC of the trajectory is also developed. After the DLCC is determined, the optimum distribution of actuator torques are computed by using a quadratic programming technique. The proposed method is conceptually straightforward, and it is applicable to wide varieties of multi-robot systems as well as fully parallel robots with redundant actuators.>
Li-Chun T. Wang, Ming Jun Kuo
IEEE Trans. Robotics Autom.2