Autonomous Driving Of A Rover-Like Robot Using Neuromorphic Computing

Autonomous driving solutions are based on artificial vision and machine learning for understanding the environment and facilitate decision making tasks. Similar techniques are used for indoor robot navigation. Deep learning architectures, which are usually computationally expensive, are impacting our daily lives. This technology is evolving with a notable improvement of cost-efficiency in terms of energy consumption, enabling AI-edge computing. However, these architectures are usually trained on powerful GPUs, what represents the limit for edge computing. Nevertheless, after this training, efficient edge computing devices can process these architectures locally. Neuromorphic engineering shows off on solving the energy bottleneck problem through bio-inspired sensors, processors and spike-based computation techniques. This work presents a mobile robotic platform commanded through the Robotic Operating System (ROS), which obeys the classification output of an AI-edge CNN accelerator for FPGA connected to a neuromorphic dynamic vision sensor. The classification system is able to process up to 200 fps for 64 x 64 histograms collected with 2k events per frame and executing a 5 layer CNN with 18MOPs for indoor robot navigation. A traffic sign dataset has been used for training achieving a measured accuracy of 97.62% and 99.96% in the validation and test datasets respectively.