Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets

Shirzadeh, Masoud; Asl, Hamed Jabbari; Amirkhani, Abdollah; Jalali, Ali

doi:10.1016/j.engappai.2016.10.016

Cited by 57 publications

(20 citation statements)

References 37 publications

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“…Generally speaking, UAVs have attracted much attention during recent years and they are able to carry out various particular atmospheric skills, for instance, exploration of moving objects [1], [2], accumulating of traffic data [3], large-scale systems [4], examination of power transition lines [5], organizing military operations' supplies [6] and supplying first-aid kit in natural disasters [7]. On the whole, unmanned aerial vehicles can be applied in military and civilian operations [8][9][10][11]. In fact, there exist three problems in the controlling of unmanned aerial vehicles: (I) UAVs are multi-input multi-output systems; (II) UAVs have unknown parameters; (III) UAVs have timevarying states and delays [12].…”

Section: Background and Motivationsmentioning

confidence: 99%

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

2021

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Section: Background and Motivationsmentioning

confidence: 99%

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

2021

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“…Visual navigation based on cooperative target is a reliable method for autonomous landing of UAV [74][75][76][77][78][79][80][81][82][83]. Accurate positioning of the cooperative target is the basis of autonomous landing system.…”

Section: Compare With the Relevant Qr Location Algorithmmentioning

confidence: 99%

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Yuan

Li²,

Jiang

et al. 2019

Future Internet

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In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high-and low-quality images, and thus more suitable for Industry 4.0.

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“…In [14], visual feedback was exploited to estimate the position and attitude of a UAV based on the triangulation method, and a classical PID control was designed for indoor UAV flight. In [15], a vision-based control scheme was proposed for a quadrotor, in order to track a moving target. To compensate for the image dynamic uncertainties, a neural network controller based on a radial basis function was introduced into the closed-loop system.…”

Section: Introductionmentioning

confidence: 99%

Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

Zhang

Zhao

Zhou

2020

Sensors

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This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.

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Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets

Cited by 57 publications

References 37 publications

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

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