Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

被引:0
|
作者
Hsu, Roy Chaoming [1 ]
Jian, Jhih-Wei [2 ]
Lin, Chih-Chuan [2 ]
Lai, Chien-Hung [2 ]
Liu, Cheng-Ting [2 ]
机构
[1] Dept Elect Engn, 300 Syuefu Rd, Chiayi 60004, Taiwan
[2] Dept Comp Sci Informat Engn, Chiayi 60004, Taiwan
关键词
Robot remote control; Kinect sensor; gesture recognition; back propagation neural network;
D O I
10.1117/12.2008456
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Pattern formation of multi mobile robots using arm gesture action
    Hsia, K.-H. (khhsia@cc.feu.edu.tw), 1600, ICIC Express Letters Office, Tokai University, Kumamoto Campus, 9-1-1, Toroku, Kumamoto, 862-8652, Japan (05):
  • [22] Localization System For Autonomous Mobile Robots Using Machine Learning Methods And Omnidirectional Sonar
    Almeida, J. S.
    Marinho, L. B.
    Mendes Souza, J. W.
    Assis, E. A.
    Reboucas Filho, P. P.
    IEEE LATIN AMERICA TRANSACTIONS, 2018, 16 (02) : 368 - 374
  • [23] Machine learning approach to self-localization of mobile robots using RFID tag
    Senta, Yosuke
    Kimuro, Yoshihiko
    Takarabet, Syuhei
    Hasegawa, Tsutomu
    2007 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, VOLS 1-3, 2007, : 476 - +
  • [24] Machine translation method using inductive learning for mobile terminal
    Matsuhara, M
    Araki, K
    Tochinai, K
    ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE, 2004, 87 (09): : 33 - 47
  • [25] CROWDSOURCING RECOGNIZED IMAGE OBJECTS IN MOBILE DEVICES THROUGH MACHINE LEARNING
    Giannikis, Athanasios
    Alepis, Efthimios
    Virvou, Maria
    2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021), 2021, : 560 - 567
  • [26] Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey
    Wesselhoft, Mike
    Hinckeldeyn, Johannes
    Kreutzfeldt, Jochen
    ROBOTICS, 2022, 11 (05)
  • [27] A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models
    Gopal, Pranesh
    Gesta, Amandine
    Mohebbi, Abolfazl
    SENSORS, 2022, 22 (10)
  • [28] Gesture Controlled Mobile Robotic Arm for Elderly and Wheelchair People Assistance Using Kinect Sensor
    Ababneh, M.
    Sha'ban, H.
    AiShalabe, D.
    Khader, D.
    Mahameed, H.
    AlQudimat, M.
    2018 15TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS AND DEVICES (SSD), 2018, : 636 - 641
  • [29] EMG based Gesture Recognition using Machine Learning
    Anil, Nikitha
    Sreeletha, S. H.
    PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, : 1560 - 1564
  • [30] Sign Gesture Classification and Recognition Using Machine Learning
    Amin, Muhammad Saad
    Rizvi, Syed Tahir Hussain
    CYBERNETICS AND SYSTEMS, 2023, 54 (05) : 604 - 618