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 analog- digital 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 real- world situations by loading it on a motorized car. The system was confirmed to respond selectively to colliding objects even in complicated real-world situations.