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Related Experiment Video

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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

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Published on: July 22, 2014

FPGA implementation of a configurable neuromorphic CPG-based locomotion controller.

Jose Hugo Barron-Zambrano¹, Cesar Torres-Huitzil

¹Information Technology Laboratory, CINVESTAV-Tamaulipas, Tamaulipas, Mexico. jhbarronz@tamps.cinvestav.mx

Neural Networks : the Official Journal of the International Neural Network Society

|May 2, 2013

Summary

This study introduces a neuromorphic-like system on a chip using Field-Programmable Gate Arrays (FPGAs) to generate adaptable locomotion patterns for legged robots. The central pattern generator (CPG) based controller offers flexible, real-time gait generation for diverse robot designs.

Keywords:

Central pattern generators FPGA Legged robots Neuromorphic engineering

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Published on: July 22, 2014

Area of Science:

Neuromorphic Engineering
Robotics
Computational Neuroscience

Background:

Neuromorphic hardware mimics biological nervous systems.
Hybrid digital-neuromorphic systems offer flexibility but reduce power efficiency and biological realism.
Central Pattern Generators (CPGs) are neural circuits that produce rhythmic motor patterns.

Purpose of the Study:

To propose an FPGA-based neuromorphic-like embedded system on a chip.
To generate locomotion patterns inspired by CPGs for legged robots.
To create a configurable and scalable architecture for diverse robot morphologies.

Main Methods:

A top-down design approach emphasizing modularity and hierarchy.
Implementation of a locomotion controller based on CPG models.
Development of a Field-Programmable Gate Array (FPGA)-based system on a chip.

Main Results:

The proposed system successfully generates periodic rhythmic movements for locomotion.
The CPG-based controller demonstrated flexibility in producing different rhythmic patterns at run-time.
The architecture proved configurable and scalable for various robot morphologies and degrees of freedom.

Conclusions:

The FPGA-based neuromorphic-like system effectively generates adaptable locomotion patterns for legged robots.
CPG models are suitable for creating flexible and real-time gait generation in robotics.
The developed architecture supports diverse robotic applications requiring adaptable locomotion.