By Topic

Trajectory planning of a 6-DOF robot based on RBF neural networks

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

3 Author(s)
Qingwen Qu ; Sch. of Mech. Eng., Shandong Univ. of Technol., Zibo ; Jixiang Wan ; Xiujun Sun

A new method for smooth trajectory planning of a 6-DOF robot in joint space is described in this paper. By the researching processes of concrete analysis of trajectory planning on robot's manipulator arm, imitation of trajectory based on kinematics and optimization of trajectory in the joint space, an one-input-six-output RBF neural network model is built and trained taking the discrete time as input and the values of six angles as outputs in joint space. With character of rapid convergence and near approximation, this new algorithm is fault tolerant and irrelative with order of inputs, which can ensure the result trajectory is firing enough. The algorithm has been tested in simulation when the virtual model of the robot was established in software ADAMS, yielding good results by studying the kinematics and the dynamics performance of the robot.

Published in:

Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on

Date of Conference:

15-18 Dec. 2007