This paper focuses on developing a rollover prevention method for mobile robots using control barrier functions (CBF) theory. It addresses the importance of safety in diverse environments where mobile robots operate, emphasizing the prevention of rollovers as a critical aspect. The study introduces safety measures based on the zero moment point to provide conditions on control inputs through CBFs. By incorporating differentiator-based safety-critical controllers, the paper aims to estimate time-varying and noisy parameters to achieve rigorous safety guarantees. Additionally, it explores the use of Projection-to-State Safety (PSSf) to ensure safety in the presence of disturbances. The effectiveness of the proposed method is demonstrated through experiments on a tracked robot facing rollover potential on steep slopes.
Several methods have been developed to measure the risk of rollover in mobile robots, including stability measures like force-angle stability, moment-height stability, and zero moment point (ZMP). Leveraging these characterizations, various control techniques have been devised to prevent rollovers such as nonlinear programming, chance-constrained optimal control, and invariance control. However, these methods often rely on high-fidelity models or require numerous sensors, limiting their practical applicability in real-world scenarios.
The paper introduces a theoretic framework for synthesizing safety filters that are robust to time-varying parameters and applies this experimentally to achieve rollover prevention on a mobile robot. By integrating ISS differentiator dynamics with CBFs, the study aims to provide robust safety guarantees against disturbances while preventing rollovers effectively.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Ersin Das,Aa... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.08916.pdfDeeper Inquiries