  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). The aforementioned
framework is constructed by modelling 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.
