Human-inspired robot as a platform for comparing between human stance control models
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1
Freiburg University, Neurology, Germany
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2
University of Freiburg, Institute of Sports Science, Germany
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3
Russian Academy of Science, Institute of Higher Nervous Activity and Neurophysiology, Russia
System identification recently enabled several laboratories to model human stance control. These models use sensory feedback control for coping with unforeseen external disturbances (support surface tilt, push against body, etc.), this despite biological ‘complications’ such as noise, inaccurate sensor signals, considerable time delays, etc. There still exist considerable differences between the models. Our laboratory, for example, focused on on-line inter-sensory interactions, by which humans extract estimates of external disturbances and use these in a direct disturbance rejection (DEC model, disturbance estimation and compensation; overview, Mergner 2010). Using model simulations, we found that it describes and predicts human balancing behavior across a broad range of experimental situations. We then went one step further and re-embodied the model into a biped robot (1 DOF, ankle joints) for hardware-in-the-loop simulations in the human laboratory, considering this a more realistic testing with respect to internal noise, inaccuracies such as leaky instead of ideal integrators, etc. Most recently, we developed a 2 DOF robot (ankle and hip joints; PostuRob II). In this we successfully tested the DEC model (see Hettich et al. 2011 on www.posturob.uniklini-freiburg.de). We then offered PostuRob II to other laboratories for testing their models in comparison to human data.
Posturob II was constructed with human-like anthropometric parameters (see above, Hettich et al. 2011). The robot’s trunk, leg and feet segments consist of aluminum frames interconnected by hip and ankle joints. Signals from human-inspired mechatronic vestibular, joint angle, and joint torque sensor components are input to, and signals for the actuator control (commanding pneumatic ‘muscles’; Festo, Germany) are output from a real time PC. There, the control model is executed as a compiled Simulink model (Real-Time Windows Target; The MathWorks Inc., USA). The robot is freely standing on a motion platform and the same experimental procedures are applied as in the human subjects.
In a cooperation with A.V. Alexandrov and A.A. Frolov (Moscow) we implemented another stance control method based on a controller of ankle and hip eigen-movements (or ‘natural synergies’; see Alexandrov and Frolov 2011) in a Simulink model and downloaded it on PostuRob II. After some model adjustments, Posturob II was able to balance. Responses to transient, steady state sinusoidal and pseudorandom tilts and translations of the support surface were recorded in terms of trunk-space (TS) and leg-space (LS) angular excursions as well as center of pressure (COP) shifts. These data are currently compared to human data. Furthermore, they are compared to those obtained with the DEC model. Thus, PostuRob II represents a valuable tool in this field of research in that it allows such comparisons within the same testbed. Further laboratories are invited to also make use of PostuRob II.
Acknowledgements
We like to thank F. Huethe and T. Günter from our neurocenter workshop for building the robot
References
Mergner, T. (2010)A neurological view on reactive stance control. Annual Reviews in Control 34, 177-198.
Alexandrov, A.V., and Frolov, A.A. (2011) Closed-loop and open-loop control of posture and movement during trunk bending. Biological Cybernetics 104, 425-438.
Keywords:
model of stance control,
posture control,
robot
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
motor control (please use "motor control" as keyword)
Citation:
Mergner
T,
Hettich
G,
Gollhofer
A,
Weiller
C,
Alexandrov
A and
Frolov
A
(2011). Human-inspired robot as a platform for comparing between human stance control models.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00101
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Prof. Thomas Mergner, Freiburg University, Neurology, Freiburg, 79106, Germany, Thomas.Mergner@uniklinik-freiburg.de