论文标题
使用基于深度学习的图像理解技术的自动无人机的控制设计
Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques
论文作者
论文摘要
本文提出了一个新框架，以考虑到嘈杂的室内环境和不确定性，将图像用作控制器具有自动飞行的输入。提出了一个新的比例综合衍生成加速器（PIDA）控制，并提出了衍生过滤器，以改善嘈杂环境中的无人机/四肢飞行稳定性，并使用对象和深度检测技术启用自动飞行。数学模型来自具有高水平忠诚度的精确模型，通过解决非线性，不确定性和耦合的问题。提出的PIDA控制器由随机双单纯形算法（SDSA）调整，以支持自主飞行。模拟结果表明，将基于深度学习的图像理解技术（视网膜蚂蚁菌落检测和PSMNET）适应所提出的控制器可以在存在环境干扰的情况下产生和跟踪所需点的生成和跟踪。
This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter is proposed to improves drone/quadcopter flight stability within a noisy environment and enables autonomous flight using object and depth detection techniques. The mathematical model is derived from an accurate model with a high level of fidelity by addressing the problems of non-linearity, uncertainties, and coupling. The proposed PIDA controller is tuned by Stochastic Dual Simplex Algorithm (SDSA) to support autonomous flight. The simulation results show that adapting the deep learning-based image understanding techniques (RetinaNet ant colony detection and PSMNet) to the proposed controller can enable the generation and tracking of the desired point in the presence of environmental disturbances.
