Abstract
In recent years, various types of aerial work robots have been developed for the inspection and maintenance of the powerline. In this regard, the aerial manipulator system (AMS) has shown broad application potential because it combines the advantages of the UAVs and the manipulator. However, it is full of challenges for the AMS to perform close-range aerial interactive operations on the powerline, which requires stable and accurate detection and positioning information of the powerline. Therefore, a powerline detection and accurate localization method based on the depth image is proposed. Firstly, the depth image is converted into a grayscale image through background filtering and a designed special mapping operator. Then, detect the edges of the powerline in the grayscale image and identify different powerlines. Finally, the Kalman filter is used to track the position of the powerline and obtain the accurate localization information. The experimental results show that the proposed method can stably detect and accurately locate the powerline, which can provide stable and reliable relative position information for close-range aerial interactive operations.
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References
Song, Y., Wang, H., Zhang, J.: A vision-based broken strand detection method for a power-line maintenance robot. IEEE Trans. Power Delivery 29(5), 2154–2161 (2014)
Stuhne, D., et al.: Design of a wireless drone recharging station and a special robot end effector for installation on a power line. IEEE Access 10, 88719–88737 (2022)
Suarez, A., Salmoral, R., Zarco-Periñan, P.J., Ollero, A.: Experimental evaluation of aerial manipulation robot in contact with 15 kv power line: shielded and long reach configurations. IEEE Access 9, 94573–94585 (2021)
Li, Z., et al.: Vision-based autonomous landing of a hybrid robot on a powerline. IEEE Trans. Instrum. Meas. 72, 1–11 (2022)
Li, Z., Liu, Y., Hayward, R., Zhang, J., Cai, J.: Knowledge-based power line detection for UAV surveillance and inspection systems. In: 2008 23rd International Conference Image and Vision Computing New Zealand, pp. 1–6. IEEE (2008)
Chen, Y., Li, Y., Zhang, H., Tong, L., Cao, Y., Xue, Z.: Automatic power line extraction from high resolution remote sensing imagery based on an improved radon transform. Pattern Recogn. 49, 174–186 (2016)
Cao, W., Zhu, L., Han, J., Wang, T., Du, Y.: High voltage transmission line detection for UAV based routing inspection. In: 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 554–558. IEEE (2013)
Sarabandi, K., Park, M.: Extraction of power line maps from millimeter-wave polarimetric SAR images. IEEE Trans. Antennas Propag. 48(12), 1802–1809 (2000)
Yan, G., Li, C., Zhou, G., Zhang, W., Li, X.: Automatic extraction of power lines from aerial images. IEEE Geosci. Remote Sens. Lett. 4(3), 387–391 (2007)
Zhang, J., Liu, L., Wang, B., Chen, X., Wang, Q., Zheng, T.: High speed automatic power line detection and tracking for a UAV-based inspection. In: 2012 International Conference on Industrial Control and Electronics Engineering, pp. 266–269. IEEE (2012)
Gu, G., Ko, B., Go, S., Lee, S.H., Lee, J., Shin, M.: Towards light-weight and real-time line segment detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 726–734 (2022)
Pautrat, R., Lin, J.T., Larsson, V., Oswald, M.R., Pollefeys, M.: Sold2: self-supervised occlusion-aware line description and detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11368–11378 (2021)
Liu, Z., et al.: B-spline wavelet neural network-based adaptive control for linear motor-driven systems via a novel gradient descent algorithm. IEEE Trans. Ind. Electron. (2023)
Xu, Y., Xu, W., Cheung, D., Tu, Z.: Line segment detection using transformers without edges. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4257–4266 (2021)
Xu, C., Li, Q., Zhou, Q., Zhang, S., Yu, D., Ma, Y.: Power line-guided automatic electric transmission line inspection system. IEEE Trans. Instrum. Meas. 71, 1–18 (2022)
Chang, W., Yang, G., Li, E., Liang, Z.: Toward a cluttered environment for learning-based multi-scale overhead ground wire recognition. Neural Process. Lett. 48, 1789–1800 (2018)
Gao, H., An, H., Lin, W., Yu, X., Qiu, J.: Trajectory tracking of variable centroid objects based on fusion of vision and force perception. IEEE Trans. Cybern. (2023)
Gao, Z., Yang, G., Li, E., Liang, Z., Guo, R.: Efficient parallel branch network with multi-scale feature fusion for real-time overhead power line segmentation. IEEE Sens. J. 21(10), 12220–12227 (2021)
Liu, B., Li, J., Yang, Y., Zhou, Z.: Controller design for quad-rotor UAV based on variable aggregation model predictive control. Flight Control Detect. 4(3), 1–7 (2021)
Liu, B., Huang, J., Lin, S., Yang, Y., Qi, Y.: Improved YOLOX-S abnormal condition detection for power transmission line corridors. In: 2021 IEEE 3rd International Conference on Power Data Science (ICPDS), pp. 13–16. IEEE (2021)
Ma, Q.: The research on binocular ranging technology for transmission lines based on two-dimensional line matching. In: 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), pp. 98–103. IEEE (2020)
Mao, T., et al.: Development of power transmission line defects diagnosis system for UAV inspection based on binocular depth imaging technology. In: 2019 2nd International Conference on Electrical Materials and Power Equipment (ICEMPE), pp. 478–481. IEEE (2019)
Zhou, X., Zheng, X., Ou, K.: Power line detect system based on stereo vision and FPGA. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 715–719. IEEE (2017)
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This work was supported by the National Natural Science Foundation of China (Grant No. 62273122).
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Li, H., Li, Z., Wu, T., Song, F., Liu, J., Li, Z. (2023). Powerline Detection and Accurate Localization Method Based on the Depth Image. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14274. Springer, Singapore. https://doi.org/10.1007/978-981-99-6501-4_27
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DOI: https://doi.org/10.1007/978-981-99-6501-4_27
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