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IMAGE QUALITY-DRIVEN OCTOROTOR FLIGHT CONTROL VIA REINFORCEMENT LEARNING

机译:图像质量驱动的八大号飞行控制通过加固学习

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This article presents the design of a reinforcement learning method based flight controller to enhance the qualities of image taken from an octorotor platform. Concerning the effect of a low resolution and a high blur rate of target images on feature extraction and target detection, we started by analyzing the relationship between these two kinds of image qualities and altitude and velocity of the octorotor. This leads to the generation of corresponding control commands. We then applied a reinforcement learning technique to automatically design the altitude and velocity controllers of the octorotor. The image analysis and the control command generation algorithms are successfully tested on the octorotor platform, and the controllers demonstrate a satisfactory performance in simulations.
机译:本文介绍了基于加强学习方法的飞行控制器的设计,以增强从八大号平台拍摄的图像的质量。关于低分辨率的效果和目标图像对特征提取和目标检测的高模糊率,我们通过分析这两种图像质量与八大峰的高度和速度之间的关系。这导致生成相应的控制命令。然后,我们应用了一种强化学习技术来自动设计八大号的高度和速度控制器。图像分析和控制命令生成算法在八大号平台上成功测试,控制器在仿真中展示了令人满意的性能。

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