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Portland State University

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Materials Science and Engineering

Locomotion -- Computer simulation

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Articles 1 - 2 of 2

Full-Text Articles in Engineering

Neuromechanical Model Of Rat Hindlimb Walking With Two-Layer Cpgs, Kaiyu Deng, Nicholas Szczecinski, Dirk Arnold, Emanuel Andrada, Martin Fischer, Roger Quinn, Alexander J. Hunt Jan 2019

Neuromechanical Model Of Rat Hindlimb Walking With Two-Layer Cpgs, Kaiyu Deng, Nicholas Szczecinski, Dirk Arnold, Emanuel Andrada, Martin Fischer, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

This work demonstrates a neuromechanical model of rat hindlimb locomotion undergoing nominal walking with perturbations. In the animal, two types of responses to perturbations are observed: resetting and non-resetting deletions. This suggests that the animal locomotor system contains a memory-like organization. To model this phenomenon, we built a synthetic nervous system that uses separate rhythm generator and pattern formation layers to activate antagonistic muscle pairs about each joint in the sagittal plane. Our model replicates the resetting and non-resetting deletions observed in the animal. In addition, in the intact (i.e., fully afferented) rat walking simulation, we observe slower recovery after …


Development And Training Of A Neural Controller For Hind Leg Walking In A Dog Robot, Alexander J. Hunt, Nicholas Szczecinski, Roger Quinn Apr 2017

Development And Training Of A Neural Controller For Hind Leg Walking In A Dog Robot, Alexander J. Hunt, Nicholas Szczecinski, Roger Quinn

Mechanical and Materials Engineering Faculty Publications and Presentations

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal’s body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even …