Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Frontiers in Neurorobotics
Beck StrohmerLeon Bonde Larsen

Abstract

Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion re...Continue Reading

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Citations

Mar 23, 2021·Frontiers in Neuroscience·Beck StrohmerLeon Bonde Larsen

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Methods Mentioned

BETA
force measurement

Software Mentioned

REP
AdEx
GitLab
MORF
Dynamixel
Walknet
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