Cart (Loading....) | Create Account
Close category search window
 

Integration of Cell-Mapping and Reinforcement-Learning Techniques for Motion Planning of Car-Like Robots

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

4 Author(s)
Gomez Plaza, M. ; Dept. de Autom., Univ. de Alcala, Alcala de Henares, Spain ; Martinez-Marin, T. ; Prieto, S.S. ; Meziat Luna, D.

The aim of this work has been to integrate the Cartesian space together with the kinematics and dynamics spaces of a car-like robot. We propose a new algorithm that obtains a minimum-time solution to the optimal motion planning of the vehicle. The new algorithm is based on the combination of cell-mapping and reinforcement-learning techniques. This algorithm can obtain the environment and vehicle parameters from received experience without needing a mathematical model. The algorithm uses a transformation of the cell-to-cell transitions to reduce the time that is spent in the knowledge of the vehicle dynamics and environment. Four state variables have been considered: 1) the velocity of the vehicle; 2) the x Cartesian coordinate; 3) the y Cartesian coordinate; and 4) the orientation of the vehicle. In addition, two different control actions can act on the vehicle: 1) the traction torque that was used for speeding up/braking the vehicle and 2) the steering angle. The results show the applicability of the proposed algorithm in environments with the presence of obstacles.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:58 ,  Issue: 9 )

Date of Publication:

Sept. 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.