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
- Authors: Seid Miad Zandavi, Vera Chung, Ali Anaissi
- 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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