A Novel Method for Measuring Drogue-UAV Relative Pose in Autonomous Aerial Refueling Based on Monocular Vision

Ma, Yuebo; Zhao, Rujin; Liu, Enhai; Zhang, Zhuang; Yan, Kun

doi:10.1109/access.2019.2943514

Cited by 6 publications

(7 citation statements)

References 53 publications

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“…As shown in Figure 2, when the UAV is moving in a straight line, the equation of the straight line can be obtained using Equation (3).…”

Section: Collision Point Between Uavsmentioning

confidence: 99%

“…For example, drone battery explosions, fires, injuries caused by collisions with drones, and safety accidents such as falls and collisions due to deviation from the adjustment distance are rapidly increasing. For this reason, a Collision Avoidance System(CAS) is essential in drones or UAV(Unmanned Aerial Vehicle) [1][2][3][4].…”

mentioning

confidence: 99%

“…At this time, the slope of the straight line can be described• (( , ) − ( , )). Using the slope obtained above, the equation for the straight line passing through two points can be expressed as Equation(3). In Equation (3), x can be calculated as = ( − ) + , and y can be calculated as = ( − ) + .…”

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confidence: 99%

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UAV Collision Detection Algorithm based on Formulafor Fast Calculation

et.al¹

2021

TURCOMAT

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Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.

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“…As shown in Figure 2, when the UAV is moving in a straight line, the equation of the straight line can be obtained using Equation (3).…”

Section: Collision Point Between Uavsmentioning

confidence: 99%

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confidence: 99%

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UAV Collision Detection Algorithm based on Formulafor Fast Calculation

et.al¹

2021

TURCOMAT

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“…Most of the previous research in AAR focused on active or passive visual sensors in close docking stage, which are mainly interested in two aspects. One is the detection and tracking of the refueling drogue by the near circular feature of the inner refueling port [8,9]; the other is the relative pose measurement between the UAV and refueling drogue in close range [10,11]. The active vision system introduces obvious features, such as installation of special light sources or marking points on the drogue to assist visual navigation [12], while the passive vision system makes no changes to the drogue, only relying on the characteristics of itself for detection and positioning [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Two-Stage Adaptive Filtering Model-Based GNSS/INS Tightly Coupled Precise Relative Navigation Algorithm for Autonomous Aerial Refueling

Sun

et al. 2022

International Journal of Aerospace Engineering

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Autonomous aerial refueling (AAR) has generated great interest in recent years. However, much research has focused on the vision-based close docking stage; few studies have been conducted on the navigation algorithm for the rendezvous and following stages. High-precision relative navigation in following stage can provide favourable conditions for successful docking. Aiming at precise relative navigation in the complex high dynamic environment of aerial refueling rendezvous and following stages, a two-stage adaptive filtering architecture is exploited in this paper. An adaptive main Kalman filter (AKF) is realized for ambiguity eliminated GNSS/INS tightly coupled integrated system, and a robust adaptive subfilter is developed for GNSS individually. Particularly, aiming at the influence of pseudorange observation multipath outliers and state abnormal disturbances in unmanned air vehicle- (UAV-) tanker proximity, an INS-aided bifactor robust and classified factor adaptive filtering (IBRCAF) algorithm for single-frequency ambiguity resolution is proposed. Finally, the effectiveness of the algorithm is verified by the simulation experiments for UAV-tanker. The results indicate that the IBRCAF algorithm can efficiently suppress the influence of pseudorange multipath gross errors and abnormal state disturbances and greatly raise the success rate of ambiguity resolution, and the two-stage adaptive filtering algorithm of IBRCAF-AKF can significantly improve relative navigation performance and achieve centimeter-level accuracy.

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“…The PDR method includes five phases. Docking phase is the most critical and difficult one, which directly affects the success of the whole AAR operation [5], [6]. PDR docking controller design aims at achieving automatic maneuvers of the receiver aircraft (manned or unmanned) to send the probe to the close The associate editor coordinating the review of this manuscript and approving it for publication was Ming Xu .…”

Section: Introductionmentioning

confidence: 99%

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

Luo

2020

IEEE Access

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The docking controller for autonomous aerial refueling (AAR) is intractable considering the high precision requirement and the complex disturbances of multiple environment flows. To solve the problems in the docking phase of AAR, such as the uncertainties of the aerodynamic parameters of receiver aircraft and the disturbances acting on the receiver aircraft, an adaptive dynamic surface control (ADSC) scheme based on radical basis function neural network (RBF-NN) is presented in this paper. Firstly, a nonlinear model of longitudinal dynamics of the receiver aircraft relative to the tanker aircraft is established, which incorporates the tanker vortex term. Secondly, a nonlinear strict-feedback form is introduced to design an adaptive dynamic surface controller with RBF-NN. Thirdly, the upper bounds of the ''total disturbances'' are estimated with the adaptive law, and the uncertain aerodynamic parameters of receiver aircraft are estimated with RBF-NN. It is proved that the proposed controller can guarantee the uniform boundedness of all the signals in the closed-loop system using Lyapunov theory. Finally, simulation results demonstrate the effectiveness of the proposed controller for the docking control of AAR. INDEX TERMS Autonomous aerial refueling, docking controller, adaptive dynamic surface control, RBF neural network.

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A Novel Method for Measuring Drogue-UAV Relative Pose in Autonomous Aerial Refueling Based on Monocular Vision

Cited by 6 publications

References 53 publications

UAV Collision Detection Algorithm based on Formulafor Fast Calculation

UAV Collision Detection Algorithm based on Formulafor Fast Calculation

A Novel Two-Stage Adaptive Filtering Model-Based GNSS/INS Tightly Coupled Precise Relative Navigation Algorithm for Autonomous Aerial Refueling

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

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