  We employ the proximal averaged Newton-type method for optimal control
(PANOC) to solve obstacle avoidance problems in real time. We introduce a novel
modeling framework for obstacle avoidance which allows us to easily account for
generic, possibly nonconvex, obstacles involving polytopes, ellipsoids,
semialgebraic sets and generic sets described by a set of nonlinear
inequalities. PANOC is particularly well-suited for embedded applications as it
involves simple steps, its implementation comes with a low memory footprint and
its fast convergence meets the tight runtime requirements of fast dynamical
systems one encounters in modern mechatronics and robotics. The proposed
obstacle avoidance scheme is tested on a lab-scale autonomous vehicle.
