Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Abstract

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.

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• Forward-backward quasi-Newton methods for nonsmooth optimization problems
• Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms
• Forward-backward truncated Newton methods for convex composite optimization

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