Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10326006 | Neural Networks | 2005 | 19 Pages |
Abstract
We introduce a new type of neural network-the dynamic wave expansion neural network (DWENN)-for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Dmitry V. Lebedev, Jochen J. Steil, Helge J. Ritter,