基于深度学习和机器学习的家庭服务机器人导航

IF 1.4 Q4 ROBOTICS
Yupei Yan, Weimin Ma, S. Wong, Xuemei Yin, Qiang Pan, Zhiwen Liao, Xiaoxin Lin
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引用次数: 0

摘要

本文讨论了如何基于深度学习和机器学习提高家庭服务机器人的导航精度。首先,应用爬虫编程在网络上收集足够多的冰箱和洗衣机图片,并提出了一个深度学习框架,可以更准确地区分冰箱和洗衣机。随后,收集并清洗来自机器人操作系统主题的数据,应用线性回归、决策树和线性 SVR 算法,对比预测机器人的耗电量,得出衬垫运动耗电量较大的结论,为机器人的路径规划提供参考。最后,提出了一种新颖的方法来区分不同的家用电器,有利于机器人的精确导航;衬垫运动比左转或右转消耗更多的电能,为机器人的优化路径规划提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Navigation of Home Service Robot Based on Deep Learning and Machine Learning
This paper discusses how to improve the accuracy of navigation for home service robot based on the deep learning and machine learning. First, the crawling programing is applied to collect enough images of fridge and washing machine on the web; a deep learning framework is proposed that can distinguish fridge and washing machine more accurately. Following, the data come from the robot operating system topics are collected and cleaned, the linear regression, decision tree, and linear SVR algorithms are applied and compared to predict the power consumption of the robot, and a conclusion is obtained that liner movement will consume more power, which provides a reference for the path planning of the robot. Lastly, the conclusions are proposed that a novel methodology is applied to distinguish different home appliances, which is useful for the accurate navigation of the robot; the liner movement will consume more power compared to turning left or right, which supplies a reference for the optimized path planning for the robot.
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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