Real-time robot vision for collision avoidance inspired by neuronal circuits of insects

Abstract

A real-time vision sensor for collision avoidance was designed. To respond selectively to approaching objects on direct collision course, the sensor employs an algorithm inspired by the visual nervous system in a locust, which can avoid a collision robustly by using visual information. We implemented the architecture of the locust nervous system with a compact hardware system which contains mixed analogdigital integrated circuits consisting of an analog resistive network and field-programmable gate array (FPGA) circuits. The response properties of the system were examined by using simulated movie images, and the system was tested also in realworld situations by loading it on a motorized car. The system was confirmed to respond selectively to colliding objects even in complicated real-world situations.