  This paper considers safe control synthesis for dynamical systems with either
probabilistic or worst-case uncertainty in both the dynamics model and the
safety constraints. We formulate novel probabilistic and robust (worst-case)
control Lyapunov function (CLF) and control barrier function (CBF) constraints
that take into account the effect of uncertainty in either case. We show that
either the probabilistic or the robust (worst-case) formulation leads to a
second-order cone program (SOCP), which enables efficient safe and stable
control synthesis. We evaluate our approach in PyBullet simulations of an
autonomous robot navigating in unknown environments and compare the performance
with a baseline CLF-CBF quadratic programming approach.
