J. Parker Mitchell, Grant Bruer, Mark E. Dean, James S. Plank, Garrett S. Rose and Catherine D. Schuman

October, 2017

2017 IEEE 5th International Symposium on Robotics and Intelligent Sensors

https://www.ieee-iris2017.com/program

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Abstract

In this paper we describe the use of a new neuromorphic computing framework to implement the navigation system for a roaming, obstacle avoidance robot. Using a Dynamic Adaptive Neural Network Array (DANNA) structure, our TENNLab (Laboratory of Tennesseans Exploring Neural Networks) hardware/software co-design framework and evolutionary optimization (EO) as the training algorithm, we create, train, implement, and test a spiking neural network autonomous robot control system using an array of neuromorphic computing elements built on an FPGA. The simplicity and flexibility of the DANNA neuromorphic computing elements allow for sufficient scale and connectivity on a Xilinx Kintex-7 FPGA to support sensory input and motor control for a mobile robot to navigate a dynamically changing environment. We further describe how more complex capabilities can be added using the same platform, e.g. object identification and tracking.

Citation Information

Text

author        J. P. Mitchell and G. Bruer and M. E. Dean and J. S. Plank and
              G. S. Rose and C. D. Schuman
title         {NeoN}: Neuromorphic Control for Autonomous Robotic Navigation
booktitle     IEEE 5th International Symposium on Robotics and Intelligent Sensors
month         October
year          2017
address       Ottawa, Canada
pages         136-142

Bibtex

@INPROCEEDINGS{mbd:17:neon,
    title = "{NeoN}: Neuromorphic Control for Autonomous Robotic Navigation",
    author = "J. P. Mitchell and G. Bruer and M. E. Dean and J. S. Plank and
                  G. S. Rose and C. D. Schuman",
    booktitle = "IEEE 5th International Symposium on Robotics and Intelligent Sensors",
    month = "October",
    year = "2017",
    address = "Ottawa, Canada",
    pages = "136-142"
}