Literature DB >> 19665555

Vestibular humanoid postural control.

Thomas Mergner1, Georg Schweigart, Luminous Fennell.   

Abstract

Many of our motor activities require stabilization against external disturbances. This especially applies to biped stance since it is inherently unstable. Disturbance compensation is mainly reactive, depending on sensory inputs and real-time sensor fusion. In humans, the vestibular system plays a major role. When there is no visual space reference, vestibular-loss clearly impairs stance stability. Most humanoid robots do not use a vestibular system, but stabilize upright body posture by means of center of pressure (COP) control. We here suggest using in addition a vestibular sensor and present a biologically inspired vestibular sensor along with a human-inspired stance control mechanism. We proceed in two steps. First, in an introductory review part, we report on relevant human sensors and their role in stance control, focusing on own models of transmitter fusion in the vestibular sensor and sensor fusion in stance control. In a second, experimental part, the models are used to construct an artificial vestibular system and to embed it into the stance control of a humanoid. The robot's performance is investigated using tilts of the support surface. The results are compared to those of humans. Functional significance of the vestibular sensor is highlighted by comparing vestibular-able with vestibular-loss states in robot and humans. We show that a kinematic body-space sensory feedback (vestibular) is advantageous over a kinetic one (force cues) for dynamic body-space balancing. Our embodiment of human sensorimotor control principles into a robot is more than just bionics. It inspired our biological work (neurorobotics: 'learning by building', proof of principle, and more). We envisage a future clinical use in the form of hardware-in-the-loop simulations of neurological symptoms for improving diagnosis and therapy and designing medical assistive devices.

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Year:  2009        PMID: 19665555     DOI: 10.1016/j.jphysparis.2009.08.002

Source DB:  PubMed          Journal:  J Physiol Paris        ISSN: 0928-4257


  17 in total

1.  Stance width changes how sensory feedback is used for multisegmental balance control.

Authors:  Adam D Goodworth; Patricia Mellodge; Robert J Peterka
Journal:  J Neurophysiol       Date:  2014-04-23       Impact factor: 2.714

2.  Sensory reweighting dynamics in human postural control.

Authors:  Lorenz Assländer; Robert J Peterka
Journal:  J Neurophysiol       Date:  2014-02-05       Impact factor: 2.714

3.  Sensory reweighting dynamics following removal and addition of visual and proprioceptive cues.

Authors:  Lorenz Assländer; Robert J Peterka
Journal:  J Neurophysiol       Date:  2016-04-13       Impact factor: 2.714

4.  Contribution of visual velocity and displacement cues to human balancing of support surface tilt.

Authors:  Lorenz Assländer; Georg Hettich; Albert Gollhofer; Thomas Mergner
Journal:  Exp Brain Res       Date:  2013-05-18       Impact factor: 1.972

5.  Visual contribution to human standing balance during support surface tilts.

Authors:  Lorenz Assländer; Georg Hettich; Thomas Mergner
Journal:  Hum Mov Sci       Date:  2015-03-25       Impact factor: 2.161

Review 6.  Time-interval for integration of stabilizing haptic and visual information in subjects balancing under static and dynamic conditions.

Authors:  Jean-Louis Honeine; Marco Schieppati
Journal:  Front Syst Neurosci       Date:  2014-10-06

7.  Haptic Cues for Balance: Use of a Cane Provides Immediate Body Stabilization.

Authors:  Stefania Sozzi; Oscar Crisafulli; Marco Schieppati
Journal:  Front Neurosci       Date:  2017-12-14       Impact factor: 4.677

8.  Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot.

Authors:  Vittorio Lippi; Thomas Mergner
Journal:  Front Neurorobot       Date:  2017-09-08       Impact factor: 2.650

9.  Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot.

Authors:  Alexei V Alexandrov; Vittorio Lippi; Thomas Mergner; Alexander A Frolov; Georg Hettich; Dusan Husek
Journal:  Front Neurorobot       Date:  2017-04-25       Impact factor: 2.650

10.  Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture.

Authors:  Cai Li; Robert Lowe; Tom Ziemke
Journal:  Front Neurorobot       Date:  2013-04-08       Impact factor: 2.650

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