Model Predictive Control With Environment Adaptation for Legged Locomotion

Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic lo...

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Autores principales:	Niraj Rathod, Angelo Bratta, Michele Focchi, Mario Zanon, Octavio Villarreal, Claudio Semini, Alberto Bemporad
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Legged locomotion mobility nonlinear model predictive control online re-planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/00d6119675544068b478e38e457d3b4b
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id	oai:doaj.org-article:00d6119675544068b478e38e457d3b4b
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spelling	oai:doaj.org-article:00d6119675544068b478e38e457d3b4b2021-11-03T23:00:12ZModel Predictive Control With Environment Adaptation for Legged Locomotion2169-353610.1109/ACCESS.2021.3118957https://doaj.org/article/00d6119675544068b478e38e457d3b4b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9564053/https://doaj.org/toc/2169-3536Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a variety of terrains. We introduce a mobility-based criterion to define an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and improves adaptation to the terrain features. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at 25 Hz with a prediction horizon of 2 seconds. We use the single rigid body dynamic model defined in the center of mass frame in order to increase the computational efficiency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney, and to locomote over rough terrain. In real experiments, we demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT’s 87 kg quadruped robot HyQ to achieve an omni-directional walk on flat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk.Niraj RathodAngelo BrattaMichele FocchiMario ZanonOctavio VillarrealClaudio SeminiAlberto BemporadIEEEarticleLegged locomotionmobilitynonlinear model predictive controlonline re-planningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 145710-145727 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Legged locomotion mobility nonlinear model predictive control online re-planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Legged locomotion mobility nonlinear model predictive control online re-planning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Niraj Rathod Angelo Bratta Michele Focchi Mario Zanon Octavio Villarreal Claudio Semini Alberto Bemporad Model Predictive Control With Environment Adaptation for Legged Locomotion
description	Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a variety of terrains. We introduce a mobility-based criterion to define an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and improves adaptation to the terrain features. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at 25 Hz with a prediction horizon of 2 seconds. We use the single rigid body dynamic model defined in the center of mass frame in order to increase the computational efficiency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney, and to locomote over rough terrain. In real experiments, we demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT’s 87 kg quadruped robot HyQ to achieve an omni-directional walk on flat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk.
format	article
author	Niraj Rathod Angelo Bratta Michele Focchi Mario Zanon Octavio Villarreal Claudio Semini Alberto Bemporad
author_facet	Niraj Rathod Angelo Bratta Michele Focchi Mario Zanon Octavio Villarreal Claudio Semini Alberto Bemporad
author_sort	Niraj Rathod
title	Model Predictive Control With Environment Adaptation for Legged Locomotion
title_short	Model Predictive Control With Environment Adaptation for Legged Locomotion
title_full	Model Predictive Control With Environment Adaptation for Legged Locomotion
title_fullStr	Model Predictive Control With Environment Adaptation for Legged Locomotion
title_full_unstemmed	Model Predictive Control With Environment Adaptation for Legged Locomotion
title_sort	model predictive control with environment adaptation for legged locomotion
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/00d6119675544068b478e38e457d3b4b
work_keys_str_mv	AT nirajrathod modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT angelobratta modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT michelefocchi modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT mariozanon modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT octaviovillarreal modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT claudiosemini modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion AT albertobemporad modelpredictivecontrolwithenvironmentadaptationforleggedlocomotion
_version_	1718445311939051520

Model Predictive Control With Environment Adaptation for Legged Locomotion

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