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Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Published online by Cambridge University Press:  19 January 2018

Tiago P. Nascimento Open the ORCID record for Tiago P. Nascimento [Opens in a new window] ,
Carlos E. T. Dórea  and
Luiz Marcos G. Gonçalves
Tiago P. Nascimento*
Affiliation:
Embedded Systems and Robotics Lab (LaSER), Computer Systems Department, Federal University of Paraíba (UFPB), Brazil
Carlos E. T. Dórea
Affiliation:
Computer and Automation Engineering Department, Federal University of Rio Grande do Norte (UFRN), Brazil. E-mails: [email protected], [email protected]
Luiz Marcos G. Gonçalves
Affiliation:
Computer and Automation Engineering Department, Federal University of Rio Grande do Norte (UFRN), Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Model predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

Keywords

Model predictive controlMobile robotsControl of robotic systemsTrajectory trackingNavigation
Type
Articles
Information
Robotica , Volume 36 , Issue 5 , May 2018 , pp. 676 - 696
Copyright
Copyright © Cambridge University Press 2018 

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