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>
Highlights
With the development of modern science and technology, the development of computer technology, electronic technology, control technology and artificial intelligence (AI) technology, as well as automobile industry has undergone rapid changes
According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multi sensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. the purpose is to solve the navigation and obstacle avoidance of the unmanned vehicle
The following conclusions are summarized: First, with the development of science and technology and the continuous improvement of sensor performance, the application of multi sensor data fusion technology in various fields is the inevitable trend of development
Summary
With the development of modern science and technology, the development of computer technology, electronic technology, control technology and artificial intelligence (AI) technology, as well as automobile industry has undergone rapid changes. Improve transportation efficiency, and reduce the workload of drivers, the intelligent driving technology of unmanned vehicles came into being. The unmanned vehicle, known as intelligent vehicle, can reduce traffic accidents and improve transportation efficiency while reducing driver's labor load, which is favored by scientists from all over the world. The unmanned vehicle system can perform various types of driving tasks, such as road environment identification, vehicle driving state detection, vehicle driving state prediction, vehicle tracking driving, cruise control, lateral driving control, longitudinal driving control, overtaking control and so on These auxiliary driving systems can significantly improve the safety performance of the vehicle, and make up for the fault caused by the driver's lack of technology or the error of judgment. Using the camera, infrared sensors and ultrasonic sensors and observation information fused from several sensors, data integration and analysis is conducted, decision-making and evaluation are completed, and the purpose of obstacle avoidance and navigation is achieved
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