基于云服务器的无人机图形控制策略识别方法

IF 1.1 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhengyu Liu, Zhenbang Cheng, Yu Liu, Qing Jiang
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引用次数: 0

摘要

随着无人机(UAV)技术的快速发展,无人机已广泛应用于农业植保、电力巡检、安全巡逻等领域。然而,无人机的控制系统是一个复杂的人机交互系统,在实际应用中要求较高。由于不同厂家在硬件设计、软件开发等方面存在差异,这些无人机控制系统对硬件要求较高,导致开发周期较长。同时,在实际应用中,由于各种原因,存在设备故障难以及时发现和排除的情况。本文采用基于云服务器技术的无人机图形化控制策略识别方法,利用支持向量机(SVM)算法对其识别精度进行分析。研究结果表明,在其他条件相同的情况下,对云服务器的无人机试用效果表示满意的科研人员和专家分别为42人和10人,占比分别为84%和100%。这说明他们认为云服务器能有效提高无人机图形化控制策略识别方法的效果,表明二者之间存在正相关关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification Method of Unmanned Aerial Vehicle Graphical Control Strategy Based on Cloud Server

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development, and other aspects among different manufacturers, these UAV control systems require high hardware requirements, resulting in a long development cycle. At the same time, in practical applications, due to various reasons, there are equipment failures that are difficult to detect and eliminate in a timely manner. This paper used the UAV graphical control strategy identification method based on cloud server technology, and used the support vector machine (SVM) algorithm to analyze its identification accuracy. The research results showed that when other conditions were the same, the number of researchers and experts who were satisfied with the drone trial effect of the cloud server was 42 and 10, respectively, accounting for 84% and 100%. It indicates that they believe that the cloud server can effectively improve the effectiveness of the drone graphical control strategy recognition method, indicating a positive relationship between the two.

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来源期刊
CiteScore
2.90
自引率
13.30%
发文量
201
审稿时长
15.8 months
期刊介绍: The International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) welcomes both theory-oriented and innovative applications articles on new developments and is of interest to both researchers in academia and industry. The current scope of this journal includes: • Pattern Recognition • Machine Learning • Deep Learning • Document Analysis • Image Processing • Signal Processing • Computer Vision • Biometrics • Biomedical Image Analysis • Artificial Intelligence In addition to regular papers describing original research work, survey articles on timely and important research topics are highly welcome. Special issues with focused topics within the scope of this journal are also published.
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