Bioinspired object motion filters as the basis of obstacle negotiation in micro aerial systems

Abstract

All animals and robots that move in the world must navigate to a goal while clearing obstacles. Using vision to accomplish such task has several advantages in cost and payload, which explains the prevalence of biological visual guidance. However, the computational overhead has been an obvious concern when increasing number of pixels and frames that need to be analyzed in real-time for a machine vision system. The use of motion vision and optic flow has been a popular bio-inspired solution for this problem. However, many early-stage motion detection approaches rely on special hardware (e.g. event-cameras) or extensive computation (e.g. dense optic flow map). Here we demonstrate a method to combine an insect vision inspired object motion filter model with simple visual guidance rules to fly through a cluttered environment. We have implemented a complete feedback control loop in a micro racing drone and achieved proximaldistal object separation through only two object motion filters. We discuss the key constraints and the scalability of this approach for future development.