Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

Gao, Shibo; Chen, Yongmei; Song, Chi

doi:10.1016/j.infrared.2013.05.010

Cited by 26 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yin et al proposed a new drogue detection and target tracking method based on the approximate circle and inner dark feature of refueling port of drogue, in which the edge information is used to obtain image feature [19]. Gao et al developed a drogue detection method utilizing low rank and sparse matrix decomposition for local multifeatures, in which the sequences of drogue images are disintegrated into the low rank background and sparse maneuvering targets [20]. Qin et al proposed a coarse and fine two-level detection structure based on the characteristics of the inner refueling port, and the row and column scanning method for the local region of image was adopted to obtain high-precision shape parameters [21].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…1–7 Considering the economy and flexibility, much attention has been paid to UAV probe-drogue autonomous aerial refueling (UAV-PD-AAR). 8–24 The docking phase of UAV-PD-AAR will be considered exclusively in this research.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7] Considering the economy and flexibility, much attention has been paid to UAV probe-drogue autonomous aerial refueling (UAV-PD-AAR). [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] The docking phase of UAV-PD-AAR will be considered exclusively in this research. Docking safety, which is vitally important for successful implementation of AAR, is a key issue during the docking phase of UAV-PD-AAR.…”

Section: Introductionmentioning

confidence: 99%

“…And only when the estimated docking success-probability satisfies the requirements of AAR, the docking maneuver can be implemented, which can ensure a high docking success-rate. Though there are many UAV-PD-AAR researches focusing on virtual simulation set-up, 8 flight test demonstration, 9 drag calculation, 10 trajectory tracking, 11 hose-drogue modeling and simulation, 12,13 vision application, 14,15 drogue recognition and tracking, 16,17 drogue detection, [18][19][20][21][22][23][24] and so on, and it is still challenging to estimate success-probability quantitatively during the docking phase of UAV-PD-AAR.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards docking safety analysis for unmanned aerial vehicle probe-drogue autonomous aerial refueling based on docking success-probability and docking reachability

Wang

Xingwei

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Docking safety is a key problem during the docking phase of the unmanned aerial vehicle probe-drogue autonomous aerial refueling (UAV-PD-AAR). To solve this problem, a novel and effective method based on docking success-probability and docking reachability is presented in this paper. Firstly, by employing the location of the drogue with respect to the probe, a docking success-probability estimation algorithm is proposed to give the probe-drogue docking success-probability in real time, with UAV control and drogue attitude considered. Secondly, considering UAV longitudinal dynamics, a docking reachability calculation algorithm for docking reachable set is designed to ensure the UAV can safely reach the docking target set, by using Hamilton–Jacobi equation. Finally, the docking safety algorithm during the docking phase of UAV-PD-AAR is constructed, based on the estimated docking success-probability and the calculated docking reachability. The experiments of docking success-probability estimation and docking reachability calculation were conducted, which can be used towards docking safety analysis during the docking phase of UAV-PD-AAR.

show abstract

“…Yin developed drogue detection and tracking strategy based on the nearly circular and dark inner part of the drogue, and edge images are applied to extract image features [21]. Gao proposed a drogue detection algorithm based on low rank and sparse decomposition with local multiple features, and the drogue image sequences are decomposed into the low rank background and sparse moving object [22]. The challenge of passive vision systems is to improve the real-time performance and reliability.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision Navigation Sensor for Autonomous Aerial Refueling

Huang¹,

Sun²,

Ling³

et al. 2017

Preprint

View full text Add to dashboard Cite

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probeand-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

show abstract

Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

Cited by 26 publications

References 29 publications

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

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

Towards docking safety analysis for unmanned aerial vehicle probe-drogue autonomous aerial refueling based on docking success-probability and docking reachability

Monocular Vision Navigation Sensor for Autonomous Aerial Refueling

Contact Info

Product

Resources

About