Data-Driven Robust Barrier Functions for Safe, Long-Term Operation

Understand

Applications that require multi-robot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain.

• To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness.
• Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations.
• Toward this end, this paper introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing Control Barrier Functions (CBFs).