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A memristor-based hybrid analog-digital computing platform for mobile robotics. 2020

Buyun Chen, and Hao Yang, and Boxiang Song, and Deming Meng, and Xiaodong Yan, and Yuanrui Li, and Yunxiang Wang, and Pan Hu, and Tse-Hsien Ou, and Mark Barnell, and Qing Wu, and Han Wang, and Wei Wu
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.

Algorithms for mobile robotic systems are generally implemented on purely digital computing platforms. Developing alternative computational platforms may lead to more energy-efficient and responsive mobile robotics. Here, we report a hybrid analog-digital computing platform enabled by memristors on a mobile inverted pendulum robot. Our mobile robotic system can tune the conductance states of memristors adaptively using a model-free optimization method to achieve optimal control performance. We implement sensor fusion and the motion control algorithms on our hybrid analog-digital computing platform and demonstrate more than one order of magnitude enhancement of speed and energy efficiency over traditional digital platforms.

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