A Braitenberg Vehicle Based on Memristive Neuromorphic Circuits

• Main Authors: Cong Wang, Zaizheng Yang, Shuang Wang, Pengfei Wang, Chen-Yu Wang, Chen Pan, Bin Cheng, Shi-Jun Liang, Feng Miao
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: Advanced Intelligent Systems
• Subjects: Braitenberg vehicles; memristors; neuromorphic architectures; supervised learning
• Online Access: https://doi.org/10.1002/aisy.201900103

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.