Invention Description
Worms are a fascinating model for bio-inspired robotics as they exhibit remarkable adaptability, agility and efficiency in navigating environments. Studying their biomechanics and behaviors provides insights into locomotion principles which can translate into novel robot systems. Bio-inspired worm robots can operate in environments that are otherwise inaccessible or hazardous to humans, however, traditional control methods often struggle with the nonlinear motion dynamics, making precise control difficult.
Researchers at Arizona State University have developed an innovative approach for enhancing the locomotion control of a worm robot by integrating neural networks with the Koopman operator framework and fractional order control techniques. Leveraging fractional sliding mode control, robustness against disturbances is improved and chattering is reduced. Inspired by biological wave-like locomotion, the worm robot design is optimized using dynamic parameters for efficient propulsion. The approach facilitates advanced control strategies in a latent space, significantly enhancing tracking accuracy and reducing control efforts as demonstrated through simulations. By modeling the nonlinear dynamics of a segmented worm robot and transforming these into a linear framework using the Koopman operator, the method enables efficient control and prediction. Simulation results demonstrate superior tracking performance and reduced error compared to traditional control methods.
This technology represents a novel approach to linearize and predict the complex nonlinear dynamics of worm robots.
Potential Applications
- Robotic systems requiring precise locomotion control such as bio-inspired robots
- Search and rescue operations in complex environments
- Medical robots performing minimally invasive procedures
- Autonomous exploration in hazardous or confined spaces
- Advanced robotic research and development platforms
- Industrial inspection robots navigating confined spaces
Benefits and Advantages
- Improved adaptability and accuracy in controlling nonlinear robotic systems
- Enhanced robustness against system uncertainties and external disturbances and reduced chatting through fractional sliding mode control
- Real-time state prediction and control using neural network approximations
- Effective transformation of nonlinear dynamics into linear control frameworks
- Demonstrated superior tracking performance through simulation
- Biologically inspired locomotion design for optimized propulsion
- Capable of handling uncertainties and disturbances effectively
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