A Braitenberg Vehicle Based on Memristive Neuromorphic Circuits. (4th November 2019)

• Record Type: Journal Article
• Authors: Wang, Cong; Yang, Zaizheng; Wang, Shuang; Wang, Pengfei; Wang, Chen-Yu; Pan, Chen; Cheng, Bin; Liang, Shi-Jun; Miao, Feng
• Is Part Of: Advanced intelligent systems. Volume 2:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-04
• Subjects: Braitenberg vehicles -- memristors -- neuromorphic architectures -- supervised learning; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.201900103
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : 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. Abstract : A smart vehicle based on memristive neuromorphic circuits, which shows excellent self‐adaptive learning ability, is demonstrated as a verification of the Braitenberg vehicle model. The vehicle shows very short response latency to input signals and is successfully trained to track around the pathway.