Probabilistic Reachable Set Estimation for Saturated Systems with Unbounded Additive Disturbances
The episode discusses a paper on estimating probabilistic reachable sets for saturated systems with unbounded additive disturbances using convex optimization. Hosts discuss how this method computes a contraction factor to create tight bounds, allowing designers to build more accurate, user-defined risk tolerance maps for autonomous systems.