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GESTURE-BASED CONTROL SYSTEM FOR MOBILE ROBOT IN ROS2 ENVIRONMENT

https://doi.org/10.26583/vestnik.2024.317

EDN: SIMQQP

Abstract

The article presents the implementation of a control system for a mobile robot in ROS2 environment using static hand gestures recognized using electromyogram (EMG) signals. The key component of this system is the algorithm for converting raw EMG signal into discrete control commands. In this implementation, the principle of generating control commands to handle mobile robot movement is considered. Also, various characheristics of gestures, such as their complexity of execution and recognition, as well as the degree of physical fatigue of the operator when performing a gesture for a long period of time, have been considered in the design of the command system. Gesture recognition based on data from 2 EMG sensors is implemented using a neural network. The developed control system was integrated with the software interface of the mobile robot in the ROS2 environment. The presented system has shown a high degree of reliability in testing, as well as the convenience of its use by test subjects.

About the Authors

V. A. Babanina
Institute of Cyber Intelligence Systems, National Research Nuclear University «MEPhI»
Russian Federation


A. I. Petrova
Institute of Cyber Intelligence Systems, National Research Nuclear University «MEPhI»
Russian Federation


T. I. Voznenko
Institute of Cyber Intelligence Systems, National Research Nuclear University «MEPhI»
Russian Federation


References

1. Gopal P., Gesta A., Mohebbi A. A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models. Sensors, 2022. Vol. 22. No. 10, 3650. DOI: 10.3390/s22103650.

2. Voznenko T.I., Gridnev A.A., Kudryavtsev K.Y., Chepin E.V. The Decomposition Method of Multi-channel Control System Based on Extended BCI for a Robotic Wheelchair. Advances in Intelligent Systems and Computing, 2020, Vol. 948. Pp. 562–567. DOI: 10.1007/978-3-030-25719-4_73.

3. Han J.S., Song W.K., Kim J.S., Bang W.C., Lee H., Bien Z. New EMG pattern recognition based on soft computing techniques and its application to control a rehabilitation robotic arm. Proc. of 6th international conference on soft computing (IIZUKA2000), 2000. Pp. 890–897.

4. Lobov S.A., Mironov V.I., Kastal'skij I.A., Kazancev V.B. Sovmestnoe ispol'zovanie komandnogo i proporcional'nogo upravleniya vneshnimi robototekhnicheskimi ustrojstvami na osnove elektromiograficheskih signalov [Sharing command and proportional control of external robotic devices based on electromyographic signals]. Sovremennye tekhnologii v medicine, 2015. Vol. 7. No. 15. Pp. 30–38 (in Russian).

5. Reifinger S., Wallhoff F., Ablassmeier M., Poitschke, T., Rigoll, G. Static and Dynamic Hand-Gesture Recognition for Augmented Reality Applications. Human-Computer Interaction. HCI Intelligent Multimodal Interaction Environments, 2007. Vol. 4552. Pp. 728–737. DOI: 10.1007/978-3-540-73110-8_79.

6. Ismajylova K.Sh. Faktory, vliyayushchie na iskazhenie izmeritel'noj informacii v elektromiografii [Factors influencing distortion of measurement information in electromyography]. Nauka, tekhnika i obrazovanie, 2017. No. 10. Pp. 21–23 (in Russian).

7. Zhang X., Huang H. A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition. Journal of neuroengineering and rehabilitation, 2015. Vol. 12. Pp. 1–16. DOI: 10.1186/s12984-015-0011-y.

8. Phinyomark A., Phukpattaranont P., Limsakul C. Feature reduction and selection for EMG signal classification. Expert systems with applications, 2012. Vol. 39. No. 8. Pp. 7420–7431. DOI: 10.1016/j.eswa.2012.01. 102.

9. Gridnev A. A., Voznenko T. I., Chepin E. V. The decision-making system for a multi-channel robotic device control. Procedia computer science, 2018. Vol. 123. Pp. 149–154. DOI: 10.1016/j.procs.2018.01.024.

10. Kim D., Jung H., Shin S. System and method of controlling mobile robot using inertia measurement unit and electromyogram sensor-based gesture recognition. Patent KR. No. 20170030139, 2015.

11. Petrova A.I., Voznenko T.I., Chepin E.V. The Impact of Artifacts on the BCI Control Channel for a Robotic Wheelchair. Mechanisms and Machine Science (book series), 2020. Vol. 80. Pp. 105–111. DOI: 10.1007/978-3-030-33491-8_12.


Review

For citations:


Babanina V.A., Petrova A.I., Voznenko T.I. GESTURE-BASED CONTROL SYSTEM FOR MOBILE ROBOT IN ROS2 ENVIRONMENT. Vestnik natsional'nogo issledovatel'skogo yadernogo universiteta "MIFI". 2024;13(3):176-183. (In Russ.) https://doi.org/10.26583/vestnik.2024.317. EDN: SIMQQP

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