Visual Motion Sensors and Neuromorphic Obstacle Avoidance System

In this work, we present motion detection algorithms that are based not only on biological models but also on the computational properties of motion perception. First, we describe monolithic implementations of hysteretic winner-take-all and nonlinear differentiator based algorithms. Second, we explain multi-chip implementations of biomimetic intensity-based models, namely Adelson-Bergen model. In addition, we describe an obstacle avoidance algorithm that is realized by incorporating a multi-chip version of the Adelson-Bergen algorithm with centering behavior and time-to-collision computation. In this way, the overall system can successfully acquire clues about the structure of its environment so that collisions can be effectively avoided. This system might be employed in building a robot that can navigate in complex cluttered environments.