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

A biological inspired neural network approach to real-time collision-free motion planning of a nonholonomic car-like robot

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)
Yang, Simon X. ; Sch. of Eng., Guelph Univ., Ont., Canada ; Meng, M. ; Xiaobu Yuan

In this paper, a novel biologically inspired neural network approach is proposed for real-time motion planning with obstacle avoidance of a nonholonomic car-like robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. The robot configuration space constitutes the state space of the neural network. There are only local connections among neurons. Thus the computational complexity linearly depends on the neural network size. The neural activity propagation is subject to the kinematic constraints of the nonholonomic car-like robot. The real-time robot motion is planned through the dynamic neural activity landscape without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore the model algorithm is computationally efficient. The stability of the neural network system is proved by qualitative analysis and a Lyapunov stability theory. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach with encouraging experimental results

Published in:

Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on  (Volume:1 )

Date of Conference:


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.