Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques

• URL: http://arxiv.org/abs/2004.12886v3
• Date: Wed, 16 Sep 2020 01:23:04 GMT
• Title: Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques
• Abstract summary: 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 improve drone/quadcopter flight stability within a noisy environment.
• Score: 1.0953917735844645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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