By Topic

General redundancy optimization method for cooperating manipulators using quadratic inequality constraints

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Woong Kwon ; Robotics & Intelligent Syst. Lab., Seoul Nat. Univ., South Korea ; Beom Hee Lee

In redundancy optimization problems related to cooperating manipulators such as optimal force distribution, constraints on the physical limits of the manipulators should be considered. We propose quadratic inequality constraints (QICs), which lead to ellipsoidal feasible regions, to solve the optimization problem more efficiently. We investigate the effect of the use of QICs from the points of view of problem size and change of the feasible region. To efficiently deal with the QICs, we also propose the dual quadratically constrained quadratic programming (QCQP) method. In this method, the size of the optimization problem is reduced so that the computational burden is lightened. The proposed method and another well-known quadratic programming method are applied to the two PUMA robots system and compared with each other. The results show that the use of QICs with the dual QCQP method allows for faster computation than the existing method

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:29 ,  Issue: 1 )