Open AccessA Braitenberg Vehicle Based on Memristive Neuromorphic Circuits
Author(s) -
Wang Cong,
Yang Zaizheng,
Wang Shuang,
Wang Pengfei,
Wang Chen-Yu,
Pan Chen,
Cheng Bin,
Liang Shi-Jun,
Miao Feng
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.201900103
Subject(s) - neuromorphic engineering , computer science , robot , robotics , electronic circuit , memristor , realization (probability) , electronic engineering , computer architecture , embedded system , artificial intelligence , engineering , artificial neural network , electrical engineering , statistics , mathematics
The Braitenberg vehicle as a simple conceptual model to characterize the response behaviors of animals or insects under a stimulus is widely used to develop autonomous vehicles able to adapt to the varying environments. Considerable effort has been devoted to building neuromorphic processors with the software in the vehicles; however, there has been no demonstration of Braitenberg vehicle with neuromorphic hardware so far. Herein, a Braitenberg vehicle with simple memristive neuromorphic circuits is built for the first time. This vehicle exhibits adaptive behaviors in the supervised learning process and is eventually trained to conduct the task of tracking path. Moreover, the memristive circuit in the vehicle demonstrates a very short response latency (≈56 ns) to input sensory information. Herein, an alternative promising solution to build self‐adaptive robots and pave the way for the realization of autonomous robots based on memristive neuromorphic circuits is offered.