{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T09:25:16Z","timestamp":1765445116051,"version":"3.46.0"},"reference-count":22,"publisher":"Emerald","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,25]]},"abstract":"\n Purpose<\/jats:title>\n This paper aims to propose a method for automated operation of electrical switchgear based on the integration of visual positioning guidance and force perception. The method includes a visual algorithm for locating targets and guiding the robotic arm, and a visual positioning and guidance error compensation strategy based on force sensing.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n This paper presents an algorithm for the recognition and localization of operational targets in electric power switchgear cabinets, employing a low-cost RGB-D camera. The algorithm applies Deep Convolutional Neural Networks (DCNN) for detection purposes and achieves stable and accurate identification and localization of operational targets through the integration of RGB images and depth data. Furthermore, a force perception prediction model based on a Backpropagation (BP) neural network is established in this paper to compensate for and correct errors in vision-based localization guidance, thereby facilitating automated operation of electrical switchgear.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n In the experiments, the relative pose between the operating platform and the electrical switchgear was altered to simulate navigation and positioning deviations, and guidance and positioning operations were tested using the methods outlined in this paper. The results indicate that within the scope of navigation and positioning deviation, the guided positioning accuracy of the proposed method in this paper is 2.6\u2009mm and 0.5\u00b0. Furthermore, a practical application was conducted in the Power distribution room, and the results demonstrate that this method can achieve automated operation of electrical switchgear.<\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n Currently, the method presented in this paper can be practically applied to the automated operation of specific types of electrical switchgear. In the future, further optimization of the algorithmic process will be pursued to enhance the universality of the algorithm, thereby meeting the automated operation requirements for various types of electrical switchgear.<\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n The method proposed in this paper can replace manual operators to perform high-risk switchgear operations and promote intelligent operation and maintenance in the power industry. Additionally, the camera and force sensor used in this method are low-cost and small, making it economically and practically beneficial.<\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n This paper proposes an automated operation method for electrical switchgear based on the integration of visual recognition and force perception. This method is applicable to the automated inspection of electric power switchgears, achieving a high operational accuracy of 2.6\u2009mm and 0.5\u00b0.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ir-12-2024-0566","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T08:12:33Z","timestamp":1742631153000},"page":"877-887","source":"Crossref","is-referenced-by-count":0,"title":["Automated operation method for electrical switchgear based on the integration of visual recognition and force perception"],"prefix":"10.1108","volume":"52","author":[{"given":"Wenwei","family":"Lin","sequence":"first","affiliation":[{"name":"Fujian (Quanzhou) Institute of Advanced Manufacturing Technology , Quanzhou,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peichao","family":"Hong","sequence":"additional","affiliation":[{"name":"Fujian (Quanzhou) Institute of Advanced Manufacturing Technology , 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