{"title":"基于多传感器数据融合的人工智能检测系统应用","authors":"Meifang Han","doi":"10.3991/ijoe.v14i06.8696","DOIUrl":null,"url":null,"abstract":"<p class=\"0abstract\"><span lang=\"EN-US\">Aiming at <a name=\"_Hlk508710819\"></a>solving </span><span lang=\"EN-US\">the navigation and obstacle avoidance of the unmanned vehicle</span><span lang=\"EN-US\">,</span><span lang=\"EN-US\">the multi sensor data fusion technology</span><span lang=\"EN-US\"> and</span><span lang=\"EN-US\"> unmanned vehicle obstacle avoidance navigation algorithm </span><span lang=\"EN-US\">were</span><span lang=\"EN-US\"> studied</span><span lang=\"EN-US\"> profoundly. A</span><span lang=\"EN-US\">ccording to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multi</span><span lang=\"EN-US\">sensor data fusion technology </span><span lang=\"EN-US\">wa</span><span lang=\"EN-US\">s applied to unmanned vehicle navigation and obstacle avoidance control system</span><span lang=\"EN-US\">. In addition,</span><span lang=\"EN-US\"> A*VFF navigation and obstacle avoidance algorithm </span><span lang=\"EN-US\">based on</span><span lang=\"EN-US\"> fuzzy neural network</span><span lang=\"EN-US\"> was</span><span lang=\"EN-US\"> improved</span><span lang=\"EN-US\">. F</span><span lang=\"EN-US\">inally</span><span lang=\"EN-US\">,</span><span lang=\"EN-US\"> through the construction of the simulation platform, simulation experiment</span><span lang=\"EN-US\"> of</span><span lang=\"EN-US\"> the unmanned vehicle obstacle avoidance navigation </span><span lang=\"EN-US\">was </span><span lang=\"EN-US\">completed, </span><span lang=\"EN-US\">and</span><span lang=\"EN-US\"> a better route</span><span lang=\"EN-US\"> was planned for </span><span lang=\"EN-US\">unmanned vehicl</span><span lang=\"EN-US\">e</span><span lang=\"EN-US\"> in a more complex environment</span><span lang=\"EN-US\">. The results showed that it</span><span lang=\"EN-US\"> realize</span><span lang=\"EN-US\">d</span><span lang=\"EN-US\"> the autonomous navigation of unmanned vehicle and obstacle avoidance function. </span><span lang=\"EN-US\">Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.</span></p>","PeriodicalId":387853,"journal":{"name":"Int. J. Online Eng.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Application of Artificial Intelligence Detection System Based on Multi-sensor Data Fusion\",\"authors\":\"Meifang Han\",\"doi\":\"10.3991/ijoe.v14i06.8696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p class=\\\"0abstract\\\"><span lang=\\\"EN-US\\\">Aiming at <a name=\\\"_Hlk508710819\\\"></a>solving </span><span lang=\\\"EN-US\\\">the navigation and obstacle avoidance of the unmanned vehicle</span><span lang=\\\"EN-US\\\">,</span><span lang=\\\"EN-US\\\">the multi sensor data fusion technology</span><span lang=\\\"EN-US\\\"> and</span><span lang=\\\"EN-US\\\"> unmanned vehicle obstacle avoidance navigation algorithm </span><span lang=\\\"EN-US\\\">were</span><span lang=\\\"EN-US\\\"> studied</span><span lang=\\\"EN-US\\\"> profoundly. A</span><span lang=\\\"EN-US\\\">ccording to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multi</span><span lang=\\\"EN-US\\\">sensor data fusion technology </span><span lang=\\\"EN-US\\\">wa</span><span lang=\\\"EN-US\\\">s applied to unmanned vehicle navigation and obstacle avoidance control system</span><span lang=\\\"EN-US\\\">. In addition,</span><span lang=\\\"EN-US\\\"> A*VFF navigation and obstacle avoidance algorithm </span><span lang=\\\"EN-US\\\">based on</span><span lang=\\\"EN-US\\\"> fuzzy neural network</span><span lang=\\\"EN-US\\\"> was</span><span lang=\\\"EN-US\\\"> improved</span><span lang=\\\"EN-US\\\">. F</span><span lang=\\\"EN-US\\\">inally</span><span lang=\\\"EN-US\\\">,</span><span lang=\\\"EN-US\\\"> through the construction of the simulation platform, simulation experiment</span><span lang=\\\"EN-US\\\"> of</span><span lang=\\\"EN-US\\\"> the unmanned vehicle obstacle avoidance navigation </span><span lang=\\\"EN-US\\\">was </span><span lang=\\\"EN-US\\\">completed, </span><span lang=\\\"EN-US\\\">and</span><span lang=\\\"EN-US\\\"> a better route</span><span lang=\\\"EN-US\\\"> was planned for </span><span lang=\\\"EN-US\\\">unmanned vehicl</span><span lang=\\\"EN-US\\\">e</span><span lang=\\\"EN-US\\\"> in a more complex environment</span><span lang=\\\"EN-US\\\">. The results showed that it</span><span lang=\\\"EN-US\\\"> realize</span><span lang=\\\"EN-US\\\">d</span><span lang=\\\"EN-US\\\"> the autonomous navigation of unmanned vehicle and obstacle avoidance function. </span><span lang=\\\"EN-US\\\">Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.</span></p>\",\"PeriodicalId\":387853,\"journal\":{\"name\":\"Int. J. Online Eng.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Online Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3991/ijoe.v14i06.8696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Online Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3991/ijoe.v14i06.8696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Artificial Intelligence Detection System Based on Multi-sensor Data Fusion
Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.
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