Vision-based anticipatory controller for the autonomous navigation of an UAV using artificial neural networks

Abstract

A vision-based anticipatory controller for the autonomous indoor navigation of an unmanned aerial vehicle (UAV) is the topic of this paper. A dual Feedforward/Feedback architecture has been used as the UAV׳s controller and the K-NN classifier using the gray level image histogram as discriminant variables has been applied for landmarks recognition. After a brief description of the UAV, we first identify the two main components of its autonomous navigation, namely, the landmark recognition and the dual controller based on cerebellar system of living beings, then we focus on the anticipatory module that has been implemented by an artificial neural network. Afterwards, the paper describes the experimental setup and discusses the experimental results centered mainly on the basic UAV׳s behavior of landmark approximation maneuver, which in topological navigation is known as the beaconing or homing problem.