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This paper presents a new framework to use images as the inputs for thecontroller to have autonomous flight, considering the noisy indoor environmentand uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA)control with a derivative filter is proposed to improves drone/quadcopterflight stability within a noisy environment and enables autonomous flight usingobject and depth detection techniques. The mathematical model is derived froman accurate model with a high level of fidelity by addressing the problems ofnon-linearity, uncertainties, and coupling. The proposed PIDA controller istuned by Stochastic Dual Simplex Algorithm (SDSA) to support autonomous flight.The simulation results show that adapting the deep learning-based imageunderstanding techniques (RetinaNet ant colony detection and PSMNet) to theproposed controller can enable the generation and tracking of the desired pointin the presence of environmental disturbances.

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