Data-Driven Methods Applied to Robot Modeling and Control
A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".
Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1520
Special Issue Editor
Special Issue Information
Dear Colleagues,
Robotic systems are characterized by their intricate and dynamic nature, making deriving an accurate dynamic model a significant challenge. Traditional analytical methods often struggle to capture the full complexity of these systems due to their nonlinearity and high dimensionality. In contrast, data-driven methods offer a robust alternative by utilizing large datasets to uncover underlying patterns and relationships within the system’s behavior. These approaches leverage advanced algorithms and machine learning techniques to create dynamic models that are both precise and adaptable. By continuously learning from real-time data and adjusting the model accordingly, data-driven methods improve the accuracy of dynamic modeling and enhance the control and performance of robotic systems. This paradigm shift represents a significant advancement in robotics, providing a more flexible and effective way to handle dynamic robotic environments' complexities.
Dr. Mehran Rahmani
Guest Editor
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Keywords
- data-driven
- robotics
- dynamics
- control
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