Design of a dedicated CNN chip for autonomous robot navigation

Obstacle avoidance is the main issue in autonomous robotics. It requires a three-dimensional effective environment sensing in real time. Among the others, the stereo vision approach to environmental information extraction seems to be very appealing, even if it leads an extremely high computational cost. However, a high performance implementation of this algorithm on a cellular neural network is able to overcome these difficulties. In the paper, the design of a CNN chip well suited for this algorithm is presented. This chip, performing a real time processing of the stereo vision data, will improve the cruising speed of a robotic platform.