Design of Tan Sheep intelligent feeding navigation system based on RTK and deep learning

被引：0
|
作者：
Mao Jiandong [1 ,2 ]
Chen Xiongjie [1 ,2 ]
Yu Zetao [1 ,2 ]
机构：
[1] North Minzu Univ, Sch Elect & Informat Engn, Yinchuan 750021, Ningxia, Peoples R China
[2] Key Lab Atmospher Environm Remote Sensing Ningx, Yinchuan 750021, Ningxia, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
关键词：
RTK navigation; deep learning; YOLO; obstacle detection; multi-sensor fusion;
D O I：
10.1109/CCDC58219.2023.10327154
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An driverless feeding cart is designed for the roblems of high cost and low efficiency of manual feeding in m gxia Tan sheep breeding process. In this paper, GPS-BeiDou RTK navigation technology is used. The feeding cart mobile station receives GPS satellite signals and reference station signals, and locates the current position of the feeding cart with centimeter -level accuracy through real-time carrier phase difference technology, while combining deep learning YOLO vision algorithm to assist RTK navigation. By training a large number of relevant obstacle datasets from Tan sheep farms, the vision system achieves the effect of being able to recognize specific catepries of obstacles, thus achieving obstacle avoidance of the feed cart. In addition, if the RTK navigates the feed cart in an area where the GPS positioning signal is unstable, deep learning can be used to navigate through visual label localization combined with multi -sensor fusion techniques.
引用
收藏
页码：1425 / 1430
页数：6
相关论文
共 50 条
  • [21] EduPort: An Intelligent Learning System Based on Deep Learning - A Preliminary Evaluation
    Cuneo, Alessandro
    D'Urso, Stefano
    Sciarrone, Filippo
    Temperini, Marco
    EMERGING TECHNOLOGIES FOR EDUCATION, PT I, SETE 2023, 2024, 14606 : 79 - 93
  • [22] Design of knowledge question-answering system for mechanical intelligent manufacturing based on deep learning
    Zhu J.
    Liang Y.
    Gu F.
    Guo J.
    Gu X.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2019, 25 (05): : 1161 - 1168
  • [23] Design of Underground Space Intelligent Disaster Prevention System Based on Multisource Data Deep Learning
    Yu, Hairuo
    Guo, Zhongqiang
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [24] Design of the Family Intelligent Wheelchair Navigation System
    Yang, Kaisi
    Zhao, Lianyu
    2018 IEEE 4TH INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE 2018), 2018, : 342 - 346
  • [25] Design of deep learning on intelligent levelling system for industry 4.0 technology
    Tsai, Sung-Yu
    Chang, Jen-Yuan
    2018 3RD INTERNATIONAL CONFERENCE ON PRECISION MACHINERY AND MANUFACTURING TECHNOLOGY (ICPMMT 2018), 2018, 185
  • [26] Deep Learning Aided System Design Method for Intelligent Reimbursement Rebot
    Yang, Jie
    Gao, Yue
    Ding, Ying
    Sun, Yingyi
    Meng, Yang
    Zhang, Wei
    IEEE ACCESS, 2019, 7 : 96232 - 96239
  • [27] Design of integrated navigation system based on information fusion technology for the intelligent transportation system
    Cao Jie
    Chen Shaoshan
    Li Yu
    Ren Chongyu
    2006 6TH INTERNATIONAL CONFERENCE ON ITS TELECOMMUNICATIONS PROCEEDINGS, 2006, : 1248 - +
  • [28] Image classification system based on Deep Learning applied to the recognition of traffic signs for intelligent robotic vehicle navigation purposes
    Bruno, Diego Renan
    Osorio, Fernando Santos
    2017 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS) AND 2017 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR), 2017,
  • [29] Intelligent adjustment system of indoor lighting based on deep learning
    Chen H.
    Yu C.
    Liu Z.
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2022, 51 (07):
  • [30] Football match intelligent editing system based on deep learning
    Wang, Bin
    Shen, Wei
    Chen, FanSheng
    Zeng, Dan
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019, 13 (10) : 5130 - 5143
12345