# Embedded nonlinear model predictive control for obstacle avoidance using PANOC

## TL;DR

This paper presents a real-time obstacle avoidance method using PANOC, a fast optimization algorithm, with a new modeling framework for various obstacle shapes, demonstrated on an autonomous vehicle.

## Contribution

Introducing a novel obstacle modeling framework compatible with PANOC for real-time control in complex environments.

## Key findings

- PANOC achieves fast convergence suitable for embedded systems.
- The framework handles nonconvex, nonlinear obstacles like polytopes and ellipsoids.
- Successful implementation on a lab-scale autonomous vehicle.

## 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.