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Constrained model predictive control for mobile robotic manipulators

Published online by Cambridge University Press:  03 April 2017

Giovanni Buizza Avanzini ,
Andrea Maria Zanchettin  and
Paolo Rocco
Giovanni Buizza Avanzini*
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: [email protected], [email protected]
Andrea Maria Zanchettin
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: [email protected], [email protected]
Paolo Rocco
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133, Milano, Italy. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper discusses the application of a constraint-based model predictive control (MPC) to mobile manipulation tracking problems. The problem has been formulated so as to guarantee offset-free tracking of piecewise constant references, with convergence and recursive feasibility guarantees. Since MPC inputs are recomputed at every control iteration, it is possible to deal with dynamic and unknown scenarios. A number of motion constraints can also be easily included: Acceleration, velocity and position constraints have been enforced, together with collision avoidance constraints for the mobile base and the arm and field-of-view constraints. Such constraints have been extended over the prediction horizon maintaining a linear-quadratic formulation of the problem. Navigation performance has been improved by devising an online algorithm that includes an additional goal to the problem, derived from the classical vortex field approach. Experimental validation shows the applicability of the proposed approach.

Keywords

Control of robotic systemsMobile manipulatorsPredictive controlRedundant manipulatorsNavigationMotion planning
Type
Articles
Information
Robotica , Volume 36 , Issue 1 , January 2018 , pp. 19 - 38
Copyright
Copyright © Cambridge University Press 2017 

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