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

Applications that require multirobot 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 article introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing control barrier functions (CBFs). The aforementioned framework is constructed by modeling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.

I. Introduction

The deployment of multirobot teams in real-world applications often requires operating in dynamic environments for extended periods of time. Examples of such real-world deployment scenarios include search and rescue [1] and precision agriculture [2]. In these scenarios, it is often difficult to model the system exactly due to environmental disturbances, such as varying terrain, weather patterns, and intrinsic changes in the dynamics of the robots (e.g., motor degradation). In addition to degrading the performance of the robots, these disturbances can even lead to catastrophic failures for safety-critical systems. Indeed, if these disturbances are not addressed, the possibility of failure becomes almost assured if the robots are required to operate over long time horizons. Therefore, long-term deployment motivates the need for robust control frameworks that can efficiently account for these uncertainties in a rigorous manner.