Comparison of Extended Jacobian and Lagrange Multiplier Based Methods for Resolving Kinematic Redundancy

Authors:
Charles A. Klein;Li-Chung Chu
Affiliations:
Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH, 43210, USA/ e-mail: klein@ ee.eng.ohio-state.edu, chul@ er4.eng.ohio-state.edu;Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH, 43210, USA/ e-mail: klein@ ee.eng.ohio-state.edu, chul@ er4.eng.ohio-state.edu
Venue:
Journal of Intelligent and Robotic Systems
Year:
1997

Citing 3
Cited 3

Robotics: control, sensing, vision, and intelligence

Robotics: control, sensing, vision, and intelligence
Task-priority based redundancy control of robot manipulators

International Journal of Robotics Research
On the inverse kinematics of redundant manipulators

International Journal of Robotics Research

A fast workspace-density-driven inverse kinematics method for hyper-redundant manipulators

Robotica
Resolve redundancy with constraints for obstacle and singularity avoidance subgoals

International Journal of Robotics and Automation
Approximation of Jacobian Inverse Kinematics Algorithms: Differential Geometric vs. Variational Approach

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several methods have been proposed in the past for resolving the control of kinematically redundant manipulators by optimizing a secondary criterion. The extended Jacobian method constrains the gradient of this criterion to be in the null space of the Jacobian matrix, while the Lagrange multiplier method represents the gradient as being in the row space. In this paper, a numerically efficient form of the Lagrange multiplier method is presented and is compared analytically, computationally, and operationally to the extended Jacobian method. This paper also presents an improved method for tracking algorithmic singularities over previous work.