Design of robot automatic navigation under computer intelligent algorithm and machine vision
This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively res...
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| Veröffentlicht in: | Journal of industrial information integration Jg. 28; S. 100366 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.07.2022
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| ISSN: | 2452-414X, 2452-414X |
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| Abstract | This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields. |
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| AbstractList | This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields. |
| ArticleNumber | 100366 |
| Author | Zeng, Yushan Yu, Xianping Wang, Bo Wei, Pengcheng Di, Zhenpeng Dai, Xiaojun |
| Author_xml | – sequence: 1 givenname: Pengcheng surname: Wei fullname: Wei, Pengcheng organization: School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing 400065, China – sequence: 2 givenname: Xianping surname: Yu fullname: Yu, Xianping organization: School of Arts and communication, Beijing Normal University, Beijing , Beijing, 100875 China – sequence: 3 givenname: Zhenpeng surname: Di fullname: Di, Zhenpeng email: 2018110034@gxufe.edu.cn organization: School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning, Guangxi, 530003 China – sequence: 4 givenname: Xiaojun surname: Dai fullname: Dai, Xiaojun organization: School of Automation, Chongqing University of Posts And Telecommunications, Chongqing 400065, China – sequence: 5 givenname: Bo surname: Wang fullname: Wang, Bo organization: School of Automation, Chongqing University of Posts And Telecommunications, Chongqing 400065, China – sequence: 6 givenname: Yushan surname: Zeng fullname: Zeng, Yushan organization: School of Automation, Chongqing University of Posts And Telecommunications, Chongqing 400065, China |
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| Keywords | Automatic navigation Computer intelligence algorithm Machine vision Robot Ant colony algorithm |
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| SubjectTerms | Ant colony algorithm Automatic navigation Computer intelligence algorithm Machine vision Robot |
| Title | Design of robot automatic navigation under computer intelligent algorithm and machine vision |
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