Object Detection and Reinforcement Learning Approach for Intelligent Control of UAV

Abstract

In recent years, the development of deep learning models that can generate more accurate predictions and operate in real-time has brought both opportunities and challenges across the various domains of robotic vision. This breakthrough enables researchers to design and deploy more challenging tasks on intelligent mobile robots, which require emphasized abilities of learning and reasoning. In this paper, a new method for intelligent robot control, based on deep learning and reinforcement learning is proposed. The fundamental idea of this work is how the UAV equipped with a monocular camera can learn significant information about the object of interest in the context of its localization and navigation. For such purpose, the object detection system based on Tiny YOLOv2 architecture is employed. Furthermore, bounding box data generated by a convolution neural network is utilized for depth estimation and determining object boundaries. This information has shown how the state-space dimensions can be significantly reduced, which was essential for further implementation of the Q-learning algorithm. In order to test the proposed framework, a model is developed in MATLAB Simulink. The simulation, which covered different scenarios, was carried out on the UAV within the 3D scene rendered by Unreal Engine. The obtained results have demonstrated the applicability of the proposed methodology for depth estimation, gathering information about the object, object-driven navigation, and autonomous localization and navigation.