Wheel Development

Research on wheel development spans diverse applications, from autonomous vehicles to assistive robotics, focusing on improving performance, safety, and adaptability. Current efforts involve developing sophisticated models for predicting wheel behavior under various conditions, employing techniques like invariant Kalman filtering for localization and deep learning for stress prediction and hands-on detection. These advancements are crucial for enhancing the capabilities of autonomous systems, improving the design of robotic platforms, and enabling more efficient and safer mobility solutions.