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The integration of deep learning and control techniques has created robotic systems capable of implementing visual servoing and navigating autonomously in unknown environments. However, analyzing the effect that timing interactions between controllers and deep neural networks have, has received little attention in the literature. In this paper we describe a novel model that includes the effects that detection loss and inference latency have on the controller of a visual servoing system. To test our model we created a target tracking system consisting of a video camera mounted on a moving platform that tracks objects using deep neural networks.

This paper presents a novel approach based on deep neu-ral networks for autonomous navigation of a quad-copter UAV in asparsely populated environment. Images from the video camera mountedon the UAV are split, emulating a compounded eye, and processed bydeep neural networks that calculate the probability that each image con-tains a wind turbine object. Then, these probabilities are used as inputsto a vision servoing system that controls the drone's navigation move-ments. Our experiments show that our approach produces relatively sta-ble movements in the UAV, allowing it to nd and navigate autonomouslytowards a wind turbine. This paper presents a novel approach based on deep neural networks for autonomous navigation of a quad-copter UAV in a sparsely populated environment. Images from the video camera mounted on the UAV are split, emulating a compounded eye, and processed by deep neural networks that calculate the probability that each image contains a wind turbine object. Then, these probabilities are used as inputs to a vision servoing system that controls the drone’s navigation movements. Our experiments show that our approach produces relatively stable movements in the UAV, allowing it to find and navigate autonomously towards a wind turbine.