With the rise of increasingly complex autonomous systems powered by black box
AI models, there is a growing need for Run Time Assurance (RTA) systems that
provide online safety filtering to untrusted primary controller output.
Currently, research in RTA tends to be ad hoc and inflexible, diminishing
collaboration and the pace of innovation. The Safe Autonomy Run Time Assurance
Framework presented in this paper provides a standardized interface for RTA
modules and a set of universal implementations of constraint-based RTA capable
of providing safety assurance given arbitrary dynamical systems and
constraints. Built around JAX, this framework leverages automatic
differentiation to populate advanced optimization based RTA methods minimizing
user effort and error. To validate the feasibility of this framework, a
simulation of a multi-agent spacecraft inspection problem is shown with safety
constraints on position and velocity