Relative navigation for autonomous aerial refueling rendezvous phase

Zhu, Youwen; Sun, Yongrong; Zhao, Wei; Huang, Bin; Wu, Ling

doi:10.1016/j.ijleo.2018.08.049

Cited by 10 publications

(11 citation statements)

References 9 publications

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“…The fitting function of the ellipse is based on the least squares ellipse fitting algorithm [47]. Equation (7) of the ellipse in the image coordinate system is obtained using the fitting function.…”

Section: ) Arc Extraction and Constraintmentioning

confidence: 99%

“…Additionally, the ellipse fitting quality metric function is established based on the fitting errors, the ratio of the circumference and the ratio of the semi-minor axis to the semi-major axis of the ellipse to evaluate the reliability of the fitted ellipse. We simplify (7) to obtain (8).…”

Section: ) Arc Extraction and Constraintmentioning

confidence: 99%

“…And Paulson et al [6] presented a novel algorithm for mitigating the negative effects of boom occlusion in stereo-based aerial environments. Zhu et al [7] and Johnson et al [8] attempted to combine different sensors system for AAR, such as the inertial navigation system (INS), GPS, stereo vision system and infrared search and track system (IRST). According to the mathematical modeling, Xufeng et al [9] and Tsukerman et al [12] discussed the ability and success probability of AAR, which have great significance for the study of AAR control methods.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Liu

et al. 2019

IEEE Access

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The key to the docking phase of autonomous aerial refueling missions is the relative pose measurement between the drogue and unmanned aerial vehicles. A novel measurement method for drogue-UAV (unmanned aerial vehicle) relative poses based on monocular vision is proposed in this paper. An adaptive arc-level structural feature extraction algorithm is applied to obtain the features that can be used for the measurements. In this algorithm, the projection of the end plane of the drogue in the image plane is accurately extracted based on an extraction quality metric. The projection contour and centroid of the internal black part of the drogue form the structural feature for the measurements. A robust pose estimation algorithm is presented to estimate the relative pose between the drogue and the UAV. The pose can be solved using a cone that is composed of the optical center of the camera and the structural feature. A Kalman filter is then applied to robustly estimate the pose of the drogue. A simulation and ground experiment are used to verify the robustness and effectiveness of the proposed method, which achieves good performance compared with other methods.

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Section: ) Arc Extraction and Constraintmentioning

confidence: 99%

Section: ) Arc Extraction and Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Liu

et al. 2019

IEEE Access

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“…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]. In order to improve the reliability and robustness for AAR, relative navigation architecture based on a multisensor combination system, such as inertial navigation system (INS), vision-based navigation (VisNav), global position system (GPS), and infrared search and track system (IRST), is studied [14][15][16], which is mainly concentrated on vision-based navigation in close range or relative inertial navigation algorithm with low accuracy. Few studies have been done for the precise relative navigation algorithm in the rendezvous and following phases, which is essential for AAR as well.…”

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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“…It has always been favored by the experts in the military and national defense fields. Compared with manned aerial refueling technology, which has a higher risk factor and greater difficulty in operation, the UAV technology has greatly reduced operational costs and risks [1][2][3]. There are two major methods for the AAR: the boom-and-receptacle refueling system and the probeand-drogue refueling system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Vision‐Based PRPL Multistage Image Processing Algorithm for Autonomous Aerial Refueling

Sun

Zhao

et al. 2021

Wireless Communications and Mobile Computing

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Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe-drogue refueling system in the docking stage. This paper proposes a novel vision-based multistage image processing algorithm of drogue detection and target tracking for AAR. This algorithm divides the whole task into four stages: preprocessor, recognizer, predictor, and locker (PRPL). The adaptive threshold segmentation (ATS) algorithm and support vector machine (SVM) classifier are utilized in preprocessor and recognizer for drogue detection. An improved kernelized correlation filter (IKCF) tracking algorithm and scale adaptive method by window position as well as image resolution adjusted are adopted in predictor and locker for target tracking in complex dynamic environments. Finally, the proposed PRPL multistage image processing strategy is tested using an autonomous aerial refueling testbed. The results indicate that the proposed algorithm achieves high precision, good reliability, and real-time capability compared with conventional algorithms. The average processing time is within 11 ms in various environments, which can meet the requirement for drogue detection and tracking in AAR.

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Relative navigation for autonomous aerial refueling rendezvous phase

Cited by 10 publications

References 9 publications

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

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

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

A Novel Vision‐Based PRPL Multistage Image Processing Algorithm for Autonomous Aerial Refueling

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