Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
Das, Ersin, Ames, Aaron D., Burdick, Joel W.
–arXiv.org Artificial Intelligence
This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety measure based on a zero moment point condition through the lens of CBFs. However, these conditions depend on time-varying and noisy parameters. To address this issue, we present a differentiator-based safety-critical controller that estimates these parameters and pairs Input-to-State Stable (ISS) differentiator dynamics with CBFs to achieve rigorous safety guarantees. Additionally, to ensure safety in the presence of disturbances, we utilize a time-varying extension of Projection-to-State Safety (PSSf). The effectiveness of the proposed method is demonstrated via experiments on a tracked robot with a rollover potential on steep slopes.
arXiv.org Artificial Intelligence
Jun-15-2024
- Country:
- North America > United States > California > Los Angeles County > Pasadena (0.04)
- Genre:
- Research Report (0.64)
- Technology:
- Information Technology > Artificial Intelligence > Robots > Locomotion (0.92)