Detection and removal of excess materials in aircraft wings using continuum robot end-effectors

Xiujie CAO, Jingjun YU, Siqi TANG, Junhao SUI, Xu PEI

PDF(8726 KB)
PDF(8726 KB)
Front. Mech. Eng. ›› 2024, Vol. 19 ›› Issue (5) : 36. DOI: 10.1007/s11465-024-0806-2
RESEARCH ARTICLE

Detection and removal of excess materials in aircraft wings using continuum robot end-effectors

Author information +
History +

Abstract

Excess materials are left inside aircraft wings due to manual operation errors, and the removal of excess materials is very crucial. To increase removal efficiency, a continuum robot (CR) with a removal end-effector and a stereo camera is used to remove excess objects. The size and weight characteristics of excess materials in aircraft wings are analyzed. A novel negative pressure end-effector and a two-finger gripper are designed based on the CR. The negative pressure end-effector aims to remove nuts, small rivets, and small volumes of aluminum shavings. A two-finger gripper is designed to remove large volumes of aluminum shavings. A stereo camera is used to achieve automatic detection and localization of excess materials. Due to poor lighting conditions in the aircraft wing compartment, supplementary lighting devices are used to improve environmental lighting. Then, You Only Look Once (YOLO) v5 is used to classify and detect excess objects, and two training data sets of excess objects in two wings are constructed. Due to the limited texture features inside the aircraft wings, this paper adopts an image-matching method based on the results of YOLO v5 detection. This matching method avoids the performance instability problem based on Oriented Fast and Rotated BRIEF feature point matching. Experimental verification reveals that the detection accuracy of each type of excess exceeds 90%, and the visual localization error is less than 2 mm for four types of excess objects. Results show the two end-effectors can work well for the task of removing excess material from the aircraft wings using a CR.

Graphical abstract

Keywords

end-effectors / continuum robot / visual detection and localization / removal of excess materials / gripper

Cite this article

Download citation ▾
Xiujie CAO, Jingjun YU, Siqi TANG, Junhao SUI, Xu PEI. Detection and removal of excess materials in aircraft wings using continuum robot end-effectors. Front. Mech. Eng., 2024, 19(5): 36 https://doi.org/10.1007/s11465-024-0806-2

References

[1]
Long T F, Li Y, Chen J. Productivity prediction in aircraft final assembly lines: comparisons and insights in different productivity ranges. Journal of Manufacturing Systems, 2022, 62: 377–389
CrossRef Google scholar
[2]
Zhao Q J, Kong Y H, Sheng S J, Zhu J J. Redundant object detection method for civil aircraft assembly based on machine vision and smart glasses. Measurement Science & Technology, 2022, 33(10): 105011
CrossRef Google scholar
[3]
Yasuda Y D V, Cappabianco F A M, Martins L E G, Gripp J A B. Aircraft visual inspection: a systematic literature review. Computers in Industry, 2022, 141: 103695
CrossRef Google scholar
[4]
Tzitzilonis V, Malandrakis K, Zanotti Fragonara L, Gonzalez Domingo J A, Avdelidis N P, Tsourdos A, Forster K. Inspection of aircraft wing panels using unmanned aerial vehicles. Sensors, 2019, 19(8): 1824
CrossRef Google scholar
[5]
Axinte D, Dong X, Palmer D, Rushworth A, Guzman S C, Olarra A, Arizaga I, Gomez-Acedo E, Txoperena K, Pfeiffer K, Messmer F, Gruhler M, Kell J. MiRoR—miniaturized robotic systems for holistic in-situ repair and maintenance works in restrained and hazardous environments. IEEE/ASME Transactions on Mechatronics, 2018, 23(2): 978–981
CrossRef Google scholar
[6]
Guo J M, Zhang J W, Wu D, Gai Y H, Chen K. An algorithm based on bidirectional searching and geometric constrained sampling for automatic manipulation planning in aircraft cable assembly. Journal of Manufacturing Systems, 2020, 57: 158–168
CrossRef Google scholar
[7]
Mei B, Zhu W D. Accurate positioning of a drilling and riveting cell for aircraft assembly. Robotics and Computer-Integrated Manufacturing, 2021, 69: 102112
CrossRef Google scholar
[8]
Shafi I, Mazhar M F, Fatima A, Alvarez R M, Miró Y, Espinosa J C M, Ashraf I. Deep learning-based real time defect detection for optimization of aircraft manufacturing and control performance. Drones, 2023, 7(1): 31
CrossRef Google scholar
[9]
WangM F, Palmer D, DongX, AlatorreD, AxinteD, NortonA. Design and development of a slender dual-structure continuum robot for in-situ aeroengine repair. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid: IEEE, 2018, 5648–5653
[10]
Li G X, Yu J J, Tang Y C, Pan J, Cao S G, Pei X. Design and modeling of continuum robot based on virtual-center of motion mechanism. Frontiers of Mechanical Engineering, 2023, 18(2): 23
CrossRef Google scholar
[11]
Jha M, Chauhan N R. A review on snake-like continuum robots for medical surgeries. IOP Conference Series: Materials Science and Engineering, 2019, 691(1): 012093
CrossRef Google scholar
[12]
Russo M, Sadati S M H, Dong X, Mohammad A, Walker I D, Bergeles C, Xu K, Axinte D A. Continuum robots: an overview. Advanced Intelligent Systems, 2023, 5(5): 2370020
CrossRef Google scholar
[13]
Wang M F, Dong X, Ba W M, Mohammad A, Axinte D, Norton A. Design, modelling and validation of a novel extra slender continuum robot for in-situ inspection and repair in aeroengine. Robotics and Computer-Integrated Manufacturing, 2021, 67: 102054
CrossRef Google scholar
[14]
NiuG C, Wang J K, XuK L. Model analysis for a continuum aircraft fuel tank inspection robot based on the Rzeppa universal joint. Advances in Mechanical Engineering, 2018, 10(5): 1687814018778229
[15]
Russo M, Raimondi L, Dong X, Axinte D, Kell J. Task-oriented optimal dimensional synthesis of robotic manipulators with limited mobility. Robotics and Computer-Integrated Manufacturing, 2021, 69: 102096
CrossRef Google scholar
[16]
Dong X, Wang M F, Mohammad A, Ba W M, Russo M, Norton A, Kell J, Axinte D. Continuum robots collaborate for safe manipulation of high-temperature flame to enable repairs in challenging environments. IEEE/ASME Transactions on Mechatronics, 2022, 27(5): 4217–4220
CrossRef Google scholar
[17]
Troncoso D A, Robles-Linares J A, Russo M, Elbanna M A, Wild S, Dong X, Mohammad A, Kell J, Norton A D, Axinte D. A continuum robot for remote applications: from industrial to medical surgery with slender continuum robots. IEEE Robotics & Automation Magazine, 2023, 30(3): 94–105
CrossRef Google scholar
[18]
Norouzi-Ghazbi S, Mehrkish A. H. Fallah M, Janabi-Sharifi F. Constrained visual predictive control of tendon-driven continuum robots. Robotics and Autonomous Systems, 2021, 145: 103856
CrossRef Google scholar
[19]
Norouzi-GhazbiS, MehrkishA, Janabi-Sharifi F. Jacobian formulation for two classes of cooperative continuum robots. In: Proceedings of the Canadian Society for Mechanical Engineering International Congress 2020. Charlottetown: Progress in Canadian Mechanical Engineering, 2020, 3–8
[20]
Dong X, Raffles M, Cobos-Guzman S, Axinte D, Kell J. A novel continuum robot using twin-pivot compliant joints: design, modeling, and validation. Journal of Mechanisms and Robotics, 2016, 8(2): 021010
CrossRef Google scholar
[21]
Oliver-Butler K, Till J, Rucker C. Continuum robot stiffness under external loads and prescribed tendon displacements. IEEE Transactions on Robotics, 2019, 35(2): 403–419
CrossRef Google scholar
[22]
Hernandez J, Sunny M S H, Sanjuan J, Rulik I, Zarif M I I, Ahamed S I, Ahmed H U, Rahman M H. Current designs of robotic arm grippers: a comprehensive systematic review. Robotics, 2023, 12(1): 5
CrossRef Google scholar
[23]
Zhang B H, Xie Y X, Zhou J, Wang K, Zhang Z. State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: a review. Computers and Electronics in Agriculture, 2020, 177: 105694
CrossRef Google scholar
[24]
SamadikhoshkhoZ, ZareiniaK, Janabi-Sharifi F. A brief review on robotic grippers classifications. In: Proceedings of the IEEE Canadian Conference of Electrical and Computer Engineering. Edmonton: IEEE, 2019, 1–4
[25]
Firth C, Dunn K, Haeusler M H, Sun Y. Anthropomorphic soft robotic end-effector for use with collaborative robots in the construction industry. Automation in Construction, 2022, 138: 104218
CrossRef Google scholar
[26]
CorreaF. Integrating industry 4.0 associated technologies into automated and traditional construction. In: Proceedings of the 37th International Symposium on Automation and Robotics in Construction. Kitakyushu: The International Association for Automation And Robotics in Construction, 2020, 285–292
[27]
Ramon Soria P, Arrue B C, Ollero A. Detection, location and grasping objects using a stereo sensor on UAV in outdoor environments. Sensors, 2017, 17(1): 103
CrossRef Google scholar
[28]
AlhammadM, Avdelidis N P, DeaneS, Ibarra-CastanedoC, Pant S, NooralishahiP, AhmadiM, GenestM, ZolotasA, Zanotti-Fragonara L, Valdes J, Maldague X P V. Diagnosis of composite materials in aircraft applications: towards a UAV-based active thermography inspection approach. In: Zalameda J N, Mendioroz A, eds. Thermosense: Thermal Infrared Applications XLIII. Florida: SPIE, 2021, 1174306
[29]
Zou Z X, Chen K Y, Shi Z W, Guo Y H, Ye J P. Object detection in 20 years: a survey. Proceedings of the IEEE, 2023, 111(3): 257–276
CrossRef Google scholar
[30]
RedmonJ, Divvala S, GirshickR, FarhadiA. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016, 779–788
[31]
NazirA, Wani M A. You only look once - object detection models: a review. In: Proceedings of the 10th International Conference on Computing for Sustainable Global Development. New Delhi: IEEE, 2023, 1088–1095
[32]
ChangJ R, Chen Y S. Pyramid stereo matching network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018, 5410–5418
[33]
Mathew M P, Mahesh T Y. Leaf-based disease detection in bell pepper plant using YOLO v5. Signal, Image and Video Processing, 2022, 16(3): 841–847
CrossRef Google scholar
[34]
Fusiello A, Trucco E, Verri A. A compact algorithm for rectification of stereo pairs. Machine Vision and Applications, 2000, 12(1): 16–22
CrossRef Google scholar
[35]
GranaC, Borghesani D, ManfrediM, CucchiaraR. A fast approach for integrating ORB descriptors in the bag of words model. In: Proceedings of the Multimedia Content and Mobile Devices. Burlingame: SPIE, 2013, 866709

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. U1813221). The authors are grateful to Xuewen Liu for assisting in the design of the end-effector, SIASUN Robot & Automation Co., Ltd., China for providing the continuum robot, Shanghai Shangfei Airplane Manufacturing Co., Ltd., China for supplying the aircraft wings, and AVIC Chengdu Aircraft Industrial Co., Ltd., China and Shanghai Jiao Tong University, China for their extending assistance.

Conflict of Interest

Jingjun Yu is a member of the Editorial Board of Frontiers of Mechanical Engineering, who was excluded from the peer review process and all editorial decisions related to the acceptance and publication of this article. Peer review was handled independently by the other editors to minimize bias.

RIGHTS & PERMISSIONS

2024 Higher Education Press
AI Summary AI Mindmap
PDF(8726 KB)

Accesses

Citations

Detail

Sections
Recommended

/