Embedded nonlinear model predictive control for obstacle avoidance using PANOC

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

doi:10.23919/ECC.2018.8550253

Embedded nonlinear model predictive control for obstacle avoidance using PANOC

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

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

76 Citations (Scopus)

601 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 language	English
Title of host publication	2018 European Control Conference
Subtitle of host publication	12/06/2018 → 15/06/2018 Limassol, Cyprus
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1523-1528
Number of pages	6
ISBN (Print)	978-3-9524-2698-2
DOIs	https://doi.org/10.23919/ECC.2018.8550253
Publication status	Published - 29 Nov 2018
Event	European Control Conference - Amathus Beach Hotel, Limassol, Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018 http://www.ecc18.eu/

Conference

Conference	European Control Conference
Abbreviated title	ECC'18
Country/Territory	Cyprus
City	Limassol
Period	12/06/2018 → 15/06/2018
Internet address	http://www.ecc18.eu/

Keywords

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

Access to Document

10.23919/ECC.2018.8550253Licence: Unspecified

Embedded NMPC for obstacle avoidance
Copyright 2018 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 1.34 MBLicence: Unspecified

Cite this

Sathya, A., Sopasakis, P., Van Parys, R., Themelis, A., Pipeleers, G., & Patrinos, P. (2018). Embedded nonlinear model predictive control for obstacle avoidance using PANOC. In 2018 European Control Conference : 12/06/2018 → 15/06/2018 Limassol, Cyprus (pp. 1523-1528). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2018.8550253

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title = "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.",

keywords = "Nonlinear model predictive control, NMPC, Model predictive control, Autonomous navigation, Obstacle avoidance, Robotics, Embedded optimization, Nonconvex optimisation",

author = "Ajay Sathya and Pantelis Sopasakis and {Van Parys}, Ruben and Andreas Themelis and Goele Pipeleers and Panagiotis Patrinos",

year = "2018",

month = nov,

day = "29",

doi = "10.23919/ECC.2018.8550253",

language = "English",

isbn = "978-3-9524-2698-2",

pages = "1523--1528",

booktitle = "2018 European Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "European Control Conference, ECC'18 ; Conference date: 12-06-2018 Through 15-06-2018",

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Sathya, A, Sopasakis, P, Van Parys, R, Themelis, A, Pipeleers, G & Patrinos, P 2018, Embedded nonlinear model predictive control for obstacle avoidance using PANOC. in 2018 European Control Conference : 12/06/2018 → 15/06/2018 Limassol, Cyprus. Institute of Electrical and Electronics Engineers Inc., pp. 1523-1528, European Control Conference, Limassol, Cyprus, 12/06/2018. https://doi.org/10.23919/ECC.2018.8550253

Embedded nonlinear model predictive control for obstacle avoidance using PANOC. / Sathya, Ajay; Sopasakis, Pantelis; Van Parys, Ruben et al.
2018 European Control Conference : 12/06/2018 → 15/06/2018 Limassol, Cyprus. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1523-1528.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Embedded nonlinear model predictive control for obstacle avoidance using PANOC

AU - Sathya, Ajay

AU - Sopasakis, Pantelis

AU - Van Parys, Ruben

AU - Themelis, Andreas

AU - Pipeleers, Goele

AU - Patrinos, Panagiotis

PY - 2018/11/29

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N2 - 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.

AB - 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.

KW - Nonlinear model predictive control

KW - NMPC

KW - Model predictive control

KW - Autonomous navigation

KW - Obstacle avoidance

KW - Robotics

KW - Embedded optimization

KW - Nonconvex optimisation

U2 - 10.23919/ECC.2018.8550253

DO - 10.23919/ECC.2018.8550253

M3 - Conference contribution

SN - 978-3-9524-2698-2

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BT - 2018 European Control Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - European Control Conference

Y2 - 12 June 2018 through 15 June 2018

ER -

Embedded nonlinear model predictive control for obstacle avoidance using PANOC

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Keywords

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