Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators
A neuromuscular model (NMC) presented by H. Geyer and extended by S. Song shows very interesting similarities with real human locomotion. The model uses a combination of reflex loops to generate stable locomotion and is able to cope with external disturbances and adapt to different conditions. However, to our knowledge no works exist on the capability of the model to handle sensory noise. In this paper, we present a method for designing Central Pattern Generators (CPG) as feedback predictors, which can be used to handle large amount of sensory noise. We show that the whole system (NMC + CPG) is able to cope with a very large amount of noise, much larger than what the original system (NMC) could handle.
KeywordsKalman Filter Noise Variance Central Pattern Generator Robotic Device Velocity Sensor
The work presented here was performed as part of the SYMBITRON project which is supported by EU research program FP7, FET-Proactive initiative “Symbiotic human-machine interaction” (ICT-2013-10) under project contract #611626.
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