NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been c...

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Bibliographic Details
Published in:arXiv.org 2019-04
Main Authors: Gutierrez-Galan, Daniel, Juan Pedro Dominguez-Morales, Perez-Pena, Fernando, Linares-Barranco, Alejandro
Format: Article
Language:English
Subjects:
Computer simulation
Insect mimicking robots
Insects
Locomotion
Neural networks
Real time
Reconfigurable hardware
Robot dynamics
Robotics
Robots
Spiking
Task complexity
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Summary:Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.
ISSN:2331-8422
DOI:10.48550/arxiv.1904.11243