Optimal trajectory generation using MPC in robotino and its implementation with ROS system

Paolo Mercorelli; Thomas Voss; Daniel Straßberger; Oleg Sergiyenko; Lars Lindner

doi:10.1109/ISIE.2017.8001493

Optimal trajectory generation using MPC in robotino and its implementation with ROS system

Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

Standard

Optimal trajectory generation using MPC in robotino and its implementation with ROS system. / Mercorelli, Paolo ; Voss, Thomas; Straßberger, Daniel et al.
Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017: Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. p. 1642-1647 8001493 (IEEE International Symposium on Industrial Electronics).

Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

Harvard

Mercorelli, P , Voss, T, Straßberger, D, Sergiyenko, O & Lindner, L 2017, Optimal trajectory generation using MPC in robotino and its implementation with ROS system. in Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017: Proceedings., 8001493, IEEE International Symposium on Industrial Electronics, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 1642-1647. https://doi.org/10.1109/ISIE.2017.8001493

APA

Mercorelli, P., Voss, T., Straßberger, D., Sergiyenko, O., & Lindner, L. (2017). Optimal trajectory generation using MPC in robotino and its implementation with ROS system. In Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017: Proceedings (pp. 1642-1647). Article 8001493 (IEEE International Symposium on Industrial Electronics). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2017.8001493

Vancouver

Mercorelli P , Voss T, Straßberger D, Sergiyenko O, Lindner L. Optimal trajectory generation using MPC in robotino and its implementation with ROS system. In Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017: Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2017. p. 1642-1647. 8001493. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE.2017.8001493

Bibtex

@inbook{d32322056593489abc7b0821c9be41c5,

title = "Optimal trajectory generation using MPC in robotino and its implementation with ROS system",

abstract = "Mobile robots are remarkable cases of high development technology and systems. Robot community developed a complex analysis to meet the increased demands for the control challenges for the movement of Robot. A model predictive control (MPC) approach is used in combination with feedforward Controller in a context of decoupling controller. Implementation of this algorithm in the ROS system is presented at the end of the contribution.",

keywords = "Engineering, Controllers, ndustrial electronics, Motion control, Predictive control systems, Robots, Model predictive control, motion control, ROS",

author = "Paolo Mercorelli and Thomas Voss and Daniel Stra{\ss}berger and Oleg Sergiyenko and Lars Lindner",

year = "2017",

month = aug,

day = "3",

doi = "10.1109/ISIE.2017.8001493",

language = "English",

series = "IEEE International Symposium on Industrial Electronics",

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

pages = "1642--1647",

booktitle = "Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017",

address = "United States",

}

RIS

TY - CHAP

T1 - Optimal trajectory generation using MPC in robotino and its implementation with ROS system

AU - Mercorelli, Paolo

AU - Voss, Thomas

AU - Straßberger, Daniel

AU - Sergiyenko, Oleg

AU - Lindner, Lars

PY - 2017/8/3

Y1 - 2017/8/3

N2 - Mobile robots are remarkable cases of high development technology and systems. Robot community developed a complex analysis to meet the increased demands for the control challenges for the movement of Robot. A model predictive control (MPC) approach is used in combination with feedforward Controller in a context of decoupling controller. Implementation of this algorithm in the ROS system is presented at the end of the contribution.

AB - Mobile robots are remarkable cases of high development technology and systems. Robot community developed a complex analysis to meet the increased demands for the control challenges for the movement of Robot. A model predictive control (MPC) approach is used in combination with feedforward Controller in a context of decoupling controller. Implementation of this algorithm in the ROS system is presented at the end of the contribution.

KW - Engineering

KW - Controllers

KW - ndustrial electronics

KW - Motion control

KW - Predictive control systems

KW - Robots

KW - Model predictive control

KW - motion control

KW - ROS

UR - http://ieeexplore.ieee.org/abstract/document/8001493/

UR - http://www.scopus.com/inward/record.url?scp=85029897635&partnerID=8YFLogxK

U2 - 10.1109/ISIE.2017.8001493

DO - 10.1109/ISIE.2017.8001493

M3 - Contributions to collected editions/anthologies

T3 - IEEE International Symposium on Industrial Electronics

SP - 1642

EP - 1647

BT - Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

ER -

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