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This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human\u2013computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100\u2009m is 99.80\u2009m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.<\/jats:p>","DOI":"10.1515\/comp-2022-0276","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T07:11:08Z","timestamp":1687245068000},"source":"Crossref","is-referenced-by-count":8,"title":["UAV patrol path planning based on machine vision and multi-sensor fusion"],"prefix":"10.1515","volume":"13","author":[{"given":"Xu","family":"Chen","sequence":"first","affiliation":[{"name":"Guangdong Energy Group Science and Technology Research Institute Co., Ltd , Guangzhou 510000, Guangdong , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"2023090110141823548_j_comp-2022-0276_ref_001","doi-asserted-by":"crossref","unstructured":"W. X. Wang, X. M. Li, L. F. Xie, H. B. Lv, and Z. H. Lv, \u201cUnmanned aircraft system airspace structure and safety measures based on spatial digital twins,\u201d IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2809\u20132818, 2021.","DOI":"10.1109\/TITS.2021.3108995"},{"key":"2023090110141823548_j_comp-2022-0276_ref_002","doi-asserted-by":"crossref","unstructured":"W. Yi, M. Jiang, R. Hoseinnezhad, and B. Wang, \u201cDistributed multi-sensor fusion using generalised multi-Bernoulli densities,\u201d Iet Radar Sonar Navig., vol. 11, no. 3, pp. 434\u2013443, 2017.","DOI":"10.1049\/iet-rsn.2016.0227"},{"key":"2023090110141823548_j_comp-2022-0276_ref_003","doi-asserted-by":"crossref","unstructured":"I. S. Weon and S. G. Lee, \u201cEnvironment recognition based on multi-sensor fusion for autonomous driving vehicles,\u201d J. Inst. Control., vol. 25, no. 2, pp. 125\u2013131, 2019.","DOI":"10.5302\/J.ICROS.2019.18.0128"},{"key":"2023090110141823548_j_comp-2022-0276_ref_004","doi-asserted-by":"crossref","unstructured":"M. Elgharbawy, A. Schwarzhaupt, M. Frey, and F. Gauterin, \u201cA real-time multisensor fusion verification framework for advanced driver assistance systems,\u201d Transp. Res. Part. F. Traffic Psychol. Behav., vol. 61, pp. 259\u2013267, 2019.","DOI":"10.1016\/j.trf.2016.12.002"},{"key":"2023090110141823548_j_comp-2022-0276_ref_005","doi-asserted-by":"crossref","unstructured":"X. Ding, Z. Wang, L. Zhang, and C. Wang, \u201cLongitudinal vehicle speed estimation for four-wheel-independently-actuated electric vehicles based on multi-sensor fusion,\u201d IEEE Trans. Veh. Technol., vol. 69, no. 11, pp. 12797\u201312806, 2020.","DOI":"10.1109\/TVT.2020.3026106"},{"key":"2023090110141823548_j_comp-2022-0276_ref_006","doi-asserted-by":"crossref","unstructured":"T. Dawood, Z. Zhu, and T. Zayed, \u201cMachine vision-based model for spalling detection and quantification in subway networks,\u201d Autom. Constr., vol. 81, pp. 149\u2013160, 2017.","DOI":"10.1016\/j.autcon.2017.06.008"},{"key":"2023090110141823548_j_comp-2022-0276_ref_007","doi-asserted-by":"crossref","unstructured":"S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh, and S. Sarkar, \u201cAn explainable deep machine vision framework for plant stress phenotyping,\u201d Proc. Natl. Acad. Sci., vol. 115, no. 18, pp. 4613\u20134618, 2018.","DOI":"10.1073\/pnas.1716999115"},{"key":"2023090110141823548_j_comp-2022-0276_ref_008","doi-asserted-by":"crossref","unstructured":"A. A. Robie, K. M. Seagraves, S. R. Egnor, and K. Branson, \u201cMachine vision methods for analyzing social interactions,\u201d J. Exp. Biol., vol. 220, no. 1, pp. 25\u201334, 2017.","DOI":"10.1242\/jeb.142281"},{"key":"2023090110141823548_j_comp-2022-0276_ref_009","doi-asserted-by":"crossref","unstructured":"L. Fernandez-Robles, G. Azzopardi, E. Alegre, and N. Petkov, \u201cMachine-vision-based identification of broken inserts in edge profile milling heads,\u201d Robot. Comput.-Integr. Manuf., vol. 44, pp. 276\u2013283, 2017.","DOI":"10.1016\/j.rcim.2016.10.004"},{"key":"2023090110141823548_j_comp-2022-0276_ref_010","doi-asserted-by":"crossref","unstructured":"H. K. Lee, S. G. Shin, and D. S. Kwon, \u201cDesign of emergency braking algorithm for pedestrian protection based on multi-sensor fusion,\u201d Int. J. Automot. Technol., vol. 18, no. 6, pp. 1067\u20131076, 2017.","DOI":"10.1007\/s12239-017-0104-7"},{"key":"2023090110141823548_j_comp-2022-0276_ref_011","doi-asserted-by":"crossref","unstructured":"F. Sanfilippo, \u201cA multi-sensor fusion framework for improving situational awareness in demanding maritime training,\u201d Reliab. Eng. Syst. Saf., vol. 161, pp. 12\u201324, 2017.","DOI":"10.1016\/j.ress.2016.12.015"},{"key":"2023090110141823548_j_comp-2022-0276_ref_012","doi-asserted-by":"crossref","unstructured":"K. Lu and R. Zhou, \u201cMulti-sensor fusion for robust target tracking in the simultaneous presence of set-membership and stochastic Gaussian uncertainties,\u201d Iet Radar Sonar Navig., vol. 11, no. 4, pp. 621\u2013628, 2017.","DOI":"10.1049\/iet-rsn.2016.0198"},{"key":"2023090110141823548_j_comp-2022-0276_ref_013","doi-asserted-by":"crossref","unstructured":"D. Jung, M. Kim, and J. Cheong, \u201cMomentum based collision detection algorithm for robot manipulators using multi-sensor fusion,\u201d J. Inst. Control., vol. 26, no. 12, pp. 1054\u20131061, 2020.","DOI":"10.5302\/J.ICROS.2020.20.0154"},{"key":"2023090110141823548_j_comp-2022-0276_ref_014","doi-asserted-by":"crossref","unstructured":"Z. Lv and L. Qiao, \u201cDeep belief network and linear perceptron based cognitive computing for collaborative robots,\u201d Appl. 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Eng., vol. 233, no. 9, pp. 2293\u20132300, 2019.","DOI":"10.1177\/0954407019867492"},{"key":"2023090110141823548_j_comp-2022-0276_ref_020","doi-asserted-by":"crossref","unstructured":"A. N. Kamaev, V. A. Sukhenko, and D. A. Karmanov, \u201cConstructing and visualizing three-dimensional sea bottom models to test AUV machine vision systems,\u201d Program. Comput. Softw., vol. 43, no. 3, pp. 184\u2013195, 2017.","DOI":"10.1134\/S0361768817030070"},{"key":"2023090110141823548_j_comp-2022-0276_ref_021","unstructured":"Z. H. Lv, Q. Liang, C. Ken, Q. J. Wang, Big data analysis technology for electric vehicle networks in smart cities, IEEE Transactions on Intelligent Transportation Systems, 2020."},{"key":"2023090110141823548_j_comp-2022-0276_ref_022","doi-asserted-by":"crossref","unstructured":"H.Nouri-Ahmadabadi, M.Omid, S. S.Mohtasebi, and M. S.Firouz, \u201cDesign, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine,\u201d Inf. Process Agric., vol. 4, no. 4, pp. 333\u2013341, 2017.","DOI":"10.1016\/j.inpa.2017.06.002"},{"key":"2023090110141823548_j_comp-2022-0276_ref_023","doi-asserted-by":"crossref","unstructured":"A. R. Mohamed, G. M. El Masry, S. A. Radwan, and R. A. ElGamal, \u201cDevelopment of a real-time machine vision prototype to detect external defects in some agricultural products,\u201d J. Soil. Sci. Agric. Eng., vol. 12, no. 5, pp. 317\u2013325, 2021.","DOI":"10.21608\/jssae.2021.178987"},{"key":"2023090110141823548_j_comp-2022-0276_ref_024","doi-asserted-by":"crossref","unstructured":"M. Rick, J. Clemens, L. Sommer, A. Folkers, K. Schill, and C. B\u00fcskens, \u201c Autonomous driving based on nonlinear model predictive control and multi-sensor fusion. - sciencedirect,\u201d IFAC-PapersOnLine, vol. 52, no. 8, pp. 182\u2013187, 2019.","DOI":"10.1016\/j.ifacol.2019.08.068"},{"key":"2023090110141823548_j_comp-2022-0276_ref_025","unstructured":"S. Wang, W. Yu, and X. Yao, \u201cA new regression modeling method for thermal error of numerical control machine tool based on multi-sensor fusion,\u201d Chin. J. Sens. Actuators, vol. 31, no. 12, pp. 1869\u20131875, 2018."},{"key":"2023090110141823548_j_comp-2022-0276_ref_026","doi-asserted-by":"crossref","unstructured":"X. Cheng, W. Liu , M. Guo, and Z. Zhang, \u201cMobile robot self-localization based on multi-sensor fusion using limited memory Kalman filter with exponential fading factor,\u201d J. Eng. Sci. Technol. Rev., vol. 11, no. 6, pp. 187\u2013196, 2018.","DOI":"10.25103\/jestr.116.24"},{"key":"2023090110141823548_j_comp-2022-0276_ref_027","unstructured":"N. Habeeb, S. Hasson , and P. Picton, \u201cMulti-sensor fusion based on DWT, fuzzy histogram equalization for video sequence,\u201d Int. Arab. J. Inf. Technol., vol. 15, no. 5, pp. 825\u2013830, 2018."},{"key":"2023090110141823548_j_comp-2022-0276_ref_028","doi-asserted-by":"crossref","unstructured":"I. Aydin, S. B. Celebi, S. Barmada, M. Tucci, Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system, Proc. Inst. Mech. Eng., Part F. J. 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