The dynamic wave expansion neural network model for robot motion planning in time-varying environments.
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.
Lebedev
Dmitry V
DV
Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany. dlebedev@techfak.uni-bielefeld.de
Steil
Jochen J
JJ
Ritter
Helge J
HJ
D002490:Central Nervous System / Q000502:physiology; D008124:Locomotion / Q000502:physiology; D009039:Motion Perception / Q000502:physiology*; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009949:Orientation / Q000502:physiology; D012371:Robotics / Q000379:methods*; D013028:Space Perception / Q000502:physiology*; D013997:Time Factors