Embedded nonlinear model predictive control for obstacle avoidance using PANOC

Ajay Sathya, Pantelis Sopasakis, Ruben Van Parys, Andreas Themelis, Goele Pipeleers, Panagiotis Patrinos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)
171 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2018 European Control Conference
Subtitle of host publication12/06/2018 → 15/06/2018 Limassol, Cyprus
Publisher IEEE
Pages1523-1528
Number of pages6
ISBN (Print)978-3-9524-2698-2
DOIs
Publication statusPublished - 29 Nov 2018
EventEuropean Control Conference - Amathus Beach Hotel, Limassol, Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018
http://www.ecc18.eu/

Conference

ConferenceEuropean Control Conference
Abbreviated titleECC'18
CountryCyprus
CityLimassol
Period12/06/201815/06/2018
Internet address

Keywords

  • Nonlinear model predictive control
  • NMPC
  • Model predictive control
  • Autonomous navigation
  • Obstacle avoidance
  • Robotics
  • Embedded optimization
  • Nonconvex optimisation

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