Multi-stage neural networks with application to motion planning of a mechanical snake

Date
2003-06
Advisor
Instructor
Source Title
Proceedings of the American Control Conference, 2003
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1278 - 1283
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

An efficient approach to nonlinear control problems in which the plant is to be driven to a desired state using a neural network controller in a number of steps is by representing the whole process as a multi - stage neural network. In this paper, an explicit formulation of the back propagation algorithm is developed for such networks. Later, this approach is used to build up a path planner for a mechanical snake (a robot composed of a sequence of articulated links). This path planner, together with a tracking algorithm, is shown to get the mechanical snake out of a collision-free closed region.

Course
Other identifiers
Book Title
Keywords
Algorithms, Backpropagation, Motion planning, Neural networks, Mechanical snake, Nonlinear control systems
Citation
Published Version (Please cite this version)