Vision-based adaptive path following controller for 4W mobile robots on varied terrains

Area of Science:

• Robotics and Control Systems
• Machine Learning Applications
• Computer Vision for Robotics

Background:

• Path-following control for wheeled mobile robots is challenging due to terrain variations affecting steering dynamics.
• Existing methods often struggle with real-time adaptation to diverse terrains.
• Accurate modeling of robot dynamics is crucial for effective motion control.

Purpose of the Study:

• To develop a novel path-following controller for mobile robots capable of adapting to varying terrains.
• To integrate real-time terrain identification and dynamic parameter matching into the control system.
• To enhance robot navigation performance by minimizing path errors and convergence time.

Main Methods:

• Modeling robot steering dynamics as a first-order system with terrain-specific parameters.
• Employing Support Vector Machine (SVM) methodology for instantaneous image recognition and parameter matching.
• Designing a time-varying path-following controller using Lyapunov stability theory.
• Conducting comparative experiments on linear, square, and circular paths in variable terrain environments.

Main Results:

• The terrain-adaptive controller demonstrated significantly faster convergence to predefined paths compared to fixed-parameter controllers.
• Reduced overshoot was observed in the adaptive controller's path-following performance.
• The SVM-based real-time parameter matching effectively compensated for terrain-induced dynamic variations.

Conclusions:

• The proposed time-varying parameter path-following controller enhances mobile robot navigation accuracy and efficiency on diverse terrains.
• Real-time adaptation using SVM and image recognition is a viable strategy for robust robotic control.
• Lyapunov stability theory provides a solid foundation for designing adaptive controllers for dynamic systems.