大数据背景下机器人目标识别系统中的人工智能算法探究

Q4 Engineering
Xue Jiang
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引用次数: 0

摘要

在人类漫长的历史长河中,随着对自然现象和社会生活的不断探索和研究,出现了许多科学领域,机器人就是这种技术发展到一定阶段的产物。目前,世界上已有数百种不同类型的机器人应用于生产和日常生活中,取得了显著的经济效益。但是,其技术问题也逐渐显现出来。例如,视觉感知等方面的缺陷无法得到有效解决。物体识别不够精确,不能有效利用信息资源实现控制功能。这些都是制约机器人进一步进步和完善的主要因素。大数据和人工智能(AI)的出现给机器人带来了前所未有的机遇。特别是大数据分析在智能制造和智慧城市建设中的应用日益广泛,从而为机器人服务提供了新的解决方案。它们不仅能使人们快速、准确地掌握大量有价值的知识,还能更好地挖掘人类智能所蕴含的巨大潜能,在很大程度上推动了机器人产业向智能化方向发展。本文在总结现有研究成果的基础上,探讨了机器人物体识别系统的发展趋势,重点研究了其关键技术--基于特征匹配的模式识别和基于加速策略的检测效率提升。针对目前存在的问题,提出了相应的解决方案,并设计了对比实验。实验证明,基于大数据和人工智能算法的机器人物体识别系统的抗干扰检测精度提高了约12.48%,希望能为未来机器人系统的发展提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data
In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.
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来源期刊
International Journal of High Speed Electronics and Systems
International Journal of High Speed Electronics and Systems Engineering-Electrical and Electronic Engineering
CiteScore
0.60
自引率
0.00%
发文量
22
期刊介绍: Launched in 1990, the International Journal of High Speed Electronics and Systems (IJHSES) has served graduate students and those in R&D, managerial and marketing positions by giving state-of-the-art data, and the latest research trends. Its main charter is to promote engineering education by advancing interdisciplinary science between electronics and systems and to explore high speed technology in photonics and electronics. IJHSES, a quarterly journal, continues to feature a broad coverage of topics relating to high speed or high performance devices, circuits and systems.
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