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An Artificial Brain Mechanism to Develop a Learning Paradigm for Robot Navigation

[+] Author Affiliations
Mohammad Sarim, Thomas Schultz, Manish Kumar, Rashmi Jha

University of Cincinnati, Cincinnati, OH

Paper No. DSCC2016-9903, pp. V001T03A004; 10 pages
doi:10.1115/DSCC2016-9903
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

Advancement in solid state device technology has made it possible to replicate some learning behaviors on those devices as observed in biological neurons. A very widely used mechanism of learning is Spike Timing Dependent Plasticity (STDP) to realize an unsupervised learning process. In this work, we have developed a novel solution for learning using such networks in robots that can be implemented on novel resistive memory devices. This artificial brain mechanism is capable of learning by observing the environment and taking actions to achieve a desired goal. We have demonstrated this learning scheme using a mathematical model representing the reconfiguration of strengths in synapses. This model can be easily implemented on miniaturized microprocessors using resistive crossbar memories. The reconfigurable resistive memory devices in crossbar arrays are capable of mimicking the synapses in human brains by changing their resistances on application of appropriate voltage signal. In this work, we have demonstrated the potential of this learning scheme by applying it to navigate a two-wheeled differential drive robot in an environment cluttered with obstacles. It can be observed that the robot is able to navigate the environment autonomously, and can reach a given target while avoiding obstacles.

Copyright © 2016 by ASME

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