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The dynamic wave expansion neural network model for robot motion planning in time-varying environments
Dmitry V Lebedev1, Jochen J Steil, Helge J Ritter
1Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany. dlebedev@techfak.uni-bielefeld.de
Summary
We developed a new dynamic wave expansion neural network (DWENN) for robot path generation. This parameter-free model efficiently creates shorter paths in dynamic environments compared to existing methods.
Area of Science:
- Robotics
- Artificial Intelligence
- Neural Networks
Background:
- Path generation for robots in dynamic environments is challenging.
- Existing neural networks often struggle with time-varying complexities.
Purpose of the Study:
- Introduce a novel, efficient neural network for robot path generation.
- Compare the dynamic wave expansion neural network (DWENN) against existing trajectory generation models.
Main Methods:
- Developed a parameter-free dynamic wave expansion neural network (DWENN).
- Evaluated DWENN's performance in dynamic environments for mobile robots and manipulators.
- Conducted simulative and long-run comparisons against existing neural network models.
Main Results:
- DWENN demonstrates computational efficiency.
- Model complexity is independent of configuration space dimensionality.
- DWENN consistently generates shorter paths, particularly in highly dynamic scenarios.
Conclusions:
- DWENN offers a superior approach to path generation in dynamic robotic applications.
- The model's efficiency and effectiveness make it suitable for real-world robotic systems.
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