Visual aided control of a MAV inspired by insect vision
Small unmanned aerial vehicles (UAVs) are expected to fly and work automatically in situations where human activities are limited as shown in the figure. However, it is difficult to implement an automatic control system into a small UAV because the size and the power are limited.
On the other hand, flying insects can recognize their own state, such as self-motion, travel distance, and danger of collision, from visual information that is processed by their very tiny visual nervous system. The goal of this project is to develop a system that recognizes the self state inspired by the insect's visual nervous system, and to develop an automatic small UAV that can explore unknown indoor environment, where the GPS is unavailable.
Studies on neuromorphic active vision using a binocular robot
Visual signal processing is a heavy task due to its high computational cost. Its difficulty becomes severe under dynamic conditions where robotic vision should operate. On the other hand, animals can understand surrounding situations and act appropriately even under strong influence of motions of the body, head, and eyes. Even when we gaze something, our eyes move constantly due to fixational eye movement, and therefore, almost always our sights are unstable.
Learning from the visual nervous system, which works appropriately under unstable situations, this project aims to develop noble vision-based control methods by demonstrating them with robot implementation.
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