[1] N. Mendes, J. Ferrer, J. Vitorino, M. Safeea, and P. Neto, “Human behavior and hand gesture classification for smart human-robot interaction,” 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Procedia Manufacturing, vol 11, pp. 91-98, 2017.
[2] R. Hendrix, P. Owan, J. Garbini, and S. Devasia, “Context-Specific Separable Gesture Selection for Control of a Robotic Manufacturing Assistant,” IFAC PapersOnline, Vol 51, No. 34, pp. 89-96, 2019.
[3] G. Canal, S. Escalera, and C. Angulo, “A real-time Human-Robot Interaction System based on Gestures for Assistive Scenarios,” Computer Vision and Image Understanding, Vol. 149, pp. 65-77, 2016.
[4] K. Nickel, and R. Stiefelhagen, “Visual Recognition of Pointing Gestures for Human-robot Interaction,” Image and Vision Computing, Vol 25, No 12, pp 1875-1884, 2007.
[5] O. Mazhar, B. Navarro, S. Ramdani, R. Passama, and A. Cherubini, “A Real-time Human-robot Interaction Framework with Robust Background Invariant Hand Gesture Detection,” Robotics and Computer-Integrated Manufacturing, Vol. 60, pp. 34-48, 2019.
[6] Cyganski, M. Heinrichs, A. von Schmidt, and D. Krajzewicz, “Simulation of automated transport offers for the city of Brunswick,” The 7th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS), Procedia Computer Science, Vol. 130, pp. 872–879, 2018.
[7] A. Tellaeche, J. Kildal, and I. Maurtua, “A Flexible System for Gesture based Human-robot Interaction”, 51st CIRP Conference on Manufacturing System, Procedia CIRP, Vol. 72, pp.57-62, 2018.
[8] K. Fujii, G. Gras, A. Salerno, and G. Yang, “Gaze Gesture based Human Robot Interaction for Laparoscopic Surgery”, Medical Image Analysis, No. 44, pp. 196-214, 2018.
[9] M. Chang, and J.Chou, “Establishing a Natural HRI System for Mobile Robot Through Human Hand Gestures,” IFAC Proceedings Volumes, Vol 42, No 16, pp. 723-728, 2009.
[10] A. Aly, and A. Tapus, “An Integrated Model of Speech to Arm Gestures Mapping in Human-Robot Interaction,” Proceeding of the 14th IFAC Symposium on Information Control Problems in Manufacturing, IFAC Proceedings Volumes, Vol 45, No 6, pp. 817-822, 2012.
[11] T. Ikai, M. Ohka, and H. Yussof, “Behavior Control of Robot by Human Finger Direction,” International Symposium of Robotics and Intelligent Sensors 2012, Procedia Engineering, Vol. 41, pp. 784-791, 2012.
[12] T. Cserteg, G. Erdos, and G. Horvath, “Assisted Assembly Process by Gesture Controlled Robots,” 51st CIRP Conference on Manufacturing System, Procedia CIRP, Vol. 72, pp. 51-56, 2018.
[13] G. Cicirelli, C. Attolico, C. Guaragnella, and T. D'Orazio, “A Kinect-based Gesture Recognition Approach for a Natural Human Robot Interface,” International Journal of Advanced Robotic Systems, Vol. 12, 22 pages, 2015.
[14] J. Donga, Z. Xia, W. Yan, and Q. Zhaoa, “Dynamic Gesture Recognition by Directional Pulse Coupled Neural Networks for Human-robot Interaction in Real Time,” Journal of Visual Communication and Image Representation, Vo. 63, 102583, 2019.
[15] P. Risma, T. Dewi, Y. Oktarina, and Y. Wijanarko, “Neural Network Controller Application on a Visual based Object Tracking and Following Robot,” Computer Engineering and Applications Journal, Vol. 8, No.1, pp. 31-40, 2019.
[16] T. Dewi, Y. Wijanarko, P. Risma, and Y. Oktarina, “Fuzzy Logic Controller Design for Leader-Follower Robot Navigation,” Proceeding of the Electrical Engineering Computer Science and Informatics, Vol. 5, No 5, pp. 298-303, 2018.
[17] P. Risma, T. Dewi, Y. Oktarina, and M. Nawawi, “Kendali Pergerakan Robot Ball Follower dengan Metode Pengolahan Citra Sederhana,” Annual Research Seminar (ARS), Vol. 4, No. 1, pp. 51-54, 2019.
[18] E. Coupete, F. Moutarde, and S. Manitsaris, “Gesture Recognition using a Depth Camera for Human Robot Collaboration on Assembly Line,” 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, Procedia Manufacturing, Vol 3, pp. 518-525, 2015.
[19] R. Deepan, S. V. Rajavarman, and K. Narasimhan, “Hand Gesture Based Control of Robotic Hand Using Raspberry Pi Processor,” Asian Journal of Scientific Research, Vol. 8, No. 3, pp. 392-402, 2015.
[20] S. Bilgin, Y. User, and M. Mercan, “Robotic Hand Controlling Based on Flexible Sensor,” International Journal of Engineering & Applied Science (IJEAS), Vol. 8, No. 4, pp. 49-58, 2016.
[21] I. Ding, R. Lin, and Yin, “Service Robot System with Integration of Wearable Myo Armband for Specialized Hand Gesture Human-computer Interfaces for People with Disabilities with Mobility Problems,” Computers & Electrical Engineering, Vol. 69, pp. 815-827, 2018.
[2] R. Hendrix, P. Owan, J. Garbini, and S. Devasia, “Context-Specific Separable Gesture Selection for Control of a Robotic Manufacturing Assistant,” IFAC PapersOnline, Vol 51, No. 34, pp. 89-96, 2019.
[3] G. Canal, S. Escalera, and C. Angulo, “A real-time Human-Robot Interaction System based on Gestures for Assistive Scenarios,” Computer Vision and Image Understanding, Vol. 149, pp. 65-77, 2016.
[4] K. Nickel, and R. Stiefelhagen, “Visual Recognition of Pointing Gestures for Human-robot Interaction,” Image and Vision Computing, Vol 25, No 12, pp 1875-1884, 2007.
[5] O. Mazhar, B. Navarro, S. Ramdani, R. Passama, and A. Cherubini, “A Real-time Human-robot Interaction Framework with Robust Background Invariant Hand Gesture Detection,” Robotics and Computer-Integrated Manufacturing, Vol. 60, pp. 34-48, 2019.
[6] Cyganski, M. Heinrichs, A. von Schmidt, and D. Krajzewicz, “Simulation of automated transport offers for the city of Brunswick,” The 7th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS), Procedia Computer Science, Vol. 130, pp. 872–879, 2018.
[7] A. Tellaeche, J. Kildal, and I. Maurtua, “A Flexible System for Gesture based Human-robot Interaction”, 51st CIRP Conference on Manufacturing System, Procedia CIRP, Vol. 72, pp.57-62, 2018.
[8] K. Fujii, G. Gras, A. Salerno, and G. Yang, “Gaze Gesture based Human Robot Interaction for Laparoscopic Surgery”, Medical Image Analysis, No. 44, pp. 196-214, 2018.
[9] M. Chang, and J.Chou, “Establishing a Natural HRI System for Mobile Robot Through Human Hand Gestures,” IFAC Proceedings Volumes, Vol 42, No 16, pp. 723-728, 2009.
[10] A. Aly, and A. Tapus, “An Integrated Model of Speech to Arm Gestures Mapping in Human-Robot Interaction,” Proceeding of the 14th IFAC Symposium on Information Control Problems in Manufacturing, IFAC Proceedings Volumes, Vol 45, No 6, pp. 817-822, 2012.
[11] T. Ikai, M. Ohka, and H. Yussof, “Behavior Control of Robot by Human Finger Direction,” International Symposium of Robotics and Intelligent Sensors 2012, Procedia Engineering, Vol. 41, pp. 784-791, 2012.
[12] T. Cserteg, G. Erdos, and G. Horvath, “Assisted Assembly Process by Gesture Controlled Robots,” 51st CIRP Conference on Manufacturing System, Procedia CIRP, Vol. 72, pp. 51-56, 2018.
[13] G. Cicirelli, C. Attolico, C. Guaragnella, and T. D'Orazio, “A Kinect-based Gesture Recognition Approach for a Natural Human Robot Interface,” International Journal of Advanced Robotic Systems, Vol. 12, 22 pages, 2015.
[14] J. Donga, Z. Xia, W. Yan, and Q. Zhaoa, “Dynamic Gesture Recognition by Directional Pulse Coupled Neural Networks for Human-robot Interaction in Real Time,” Journal of Visual Communication and Image Representation, Vo. 63, 102583, 2019.
[15] P. Risma, T. Dewi, Y. Oktarina, and Y. Wijanarko, “Neural Network Controller Application on a Visual based Object Tracking and Following Robot,” Computer Engineering and Applications Journal, Vol. 8, No.1, pp. 31-40, 2019.
[16] T. Dewi, Y. Wijanarko, P. Risma, and Y. Oktarina, “Fuzzy Logic Controller Design for Leader-Follower Robot Navigation,” Proceeding of the Electrical Engineering Computer Science and Informatics, Vol. 5, No 5, pp. 298-303, 2018.
[17] P. Risma, T. Dewi, Y. Oktarina, and M. Nawawi, “Kendali Pergerakan Robot Ball Follower dengan Metode Pengolahan Citra Sederhana,” Annual Research Seminar (ARS), Vol. 4, No. 1, pp. 51-54, 2019.
[18] E. Coupete, F. Moutarde, and S. Manitsaris, “Gesture Recognition using a Depth Camera for Human Robot Collaboration on Assembly Line,” 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, Procedia Manufacturing, Vol 3, pp. 518-525, 2015.
[19] R. Deepan, S. V. Rajavarman, and K. Narasimhan, “Hand Gesture Based Control of Robotic Hand Using Raspberry Pi Processor,” Asian Journal of Scientific Research, Vol. 8, No. 3, pp. 392-402, 2015.
[20] S. Bilgin, Y. User, and M. Mercan, “Robotic Hand Controlling Based on Flexible Sensor,” International Journal of Engineering & Applied Science (IJEAS), Vol. 8, No. 4, pp. 49-58, 2016.
[21] I. Ding, R. Lin, and Yin, “Service Robot System with Integration of Wearable Myo Armband for Specialized Hand Gesture Human-computer Interfaces for People with Disabilities with Mobility Problems,” Computers & Electrical Engineering, Vol. 69, pp. 815-827, 2018.
- Abstract viewed - 1195 times
- PDF downloaded - 1086 times
Affiliations
Tresna Dewi
Politeknik Negeri Sriwijaya
Amperawan Amperawan
Politeknik Negeri Sriwijaya
Pola Risma
Politeknik Negeri Sriwijaya
Yurni Oktarina
Politeknik Negeri Sriwijaya
Dicky Astra Yudha
Politeknik Negeri Sriwijaya
Dicky Astra Yudha
Politeknik Negeri Sriwijaya
Finger Cue for Mobile Robot Motion Control
Abstract
The current technology enables automation using a robot to help or substitute humans in industry and domestic applications. This robot invasion to human life emerges a new requirement to set a method of communication between a human and a robot. One of the oldest languages is finger gesture, and this is easy to be applied method by implementing image detection that connected to the actuators of the robot to respond to human orders. This paper presents a method to navigate robots based on human fingers cue, including "Forward," "Backward," "Turn right," "Turn left," and "Stop" to generate the forward, backward, turn right, turn left, and stop motion. The finger detection is facilitated by a camera module (NFR2401L) with the image plane of 640 x 480 and 30 fps speed. The detection in coordinates x <43 and y <100, robot moves forward, in x <29 and y <100-coordinates , robot turns left, and in x <19 and y <100-coordinates , robot turns right. The experiment was conducted to show the effectiveness of the proposed method, and to some extent robot can follow human cues to navigate in its assigned location.