Abstract
To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.
Highlights
Academic Editors: Sunghun Jung, Kooktae Lee and Kartik B
Inspired by the idea of deep reinforcement learning (DRL), which integrates the feature representation ability of deep learning (DL) and the intelligent decision ability of reinforcement learning (RL), this paper proposes a Unmanned Aerial Vehicles (UAVs) autonomous control framework based on end-to-end DRL with the image as the only input
The results show that the end-to-end method allows the UAV to learn to land on the centerline of the runway even with large initial lateral and heading angle offsets
Summary
Academic Editors: Sunghun Jung, Kooktae Lee and Kartik B. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In recent years, Unmanned Aerial Vehicles (UAVs) have been widely applied in various military and civil situations [1,2,3]. Increasing attention has been paid to the autonomous control of UAVs [4]. UAVs are expected to fly safely on their own and make appropriate decisions without human intervention, completing their mission with higher efficiency and lower cost using entirely onboard sensing and computing. In missions of increasing complexity and uncertainty, it is becoming increasingly challenging for UAVs to react appropriately to dynamic environments in time
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