Implementation of Gesture Control Robotic Arm for Automation of Industrial Application
DOI:
https://doi.org/10.32628/IJSRST207442Keywords:
DOF, Robotic Arm.Abstract
In today’s world, in almost all sectors, most of the work is done by robots or robotic arm having different number of degree of freedoms (DOF’s) as per the requirement. This project deals with the Design and Implementation of a “Wireless Gesture Controlled Robotic Arm with Vision”. The system design is divided into 3 parts namely: Accelerometer Part, Robotic Arm and Platform. It is fundamentally an Accelerometer based framework which controls a Robotic Arm remotely utilizing a, little and minimal effort, 3-pivot (DOF's) accelerometer by means of RF signals. The Robotic Arm is mounted over a versatile stage which is likewise controlled remotely by another accelerometer. One accelerometer is mounted/joined on the human hand, catching its conduct (motions and stances) and hence the mechanical arm moves in like manner and the other accelerometer is mounted on any of the leg of the client/administrator, catching its motions and stances and in this way the stage moves as needs be. In a nutshell, the robotic arm and platform is synchronised with the gestures and postures of the hand and leg of the user / operator, respectively. The different motions performed by robotic arm are: PICK and PLACE / DROP, RAISING and LOWERING the objects. Also, the motions performed by the platform are: FORWARD, BACKWARD, RIGHT and LEFT.
References
- M. A. Jose and R. de Deus Lopes, “Human–computer interface controlled by the lip,” IEEE J. Biomed. Health Inf., vol. 19, no. 1, pp. 302–308, Jan. 2015.
- C. L. Fall et al., “Wireless sEMG-based body–machine interface for assistive technology devices,” IEEE J. Biomed. Health Informat., vol. 21, no. 4, pp. 967–977, Jul. 2017.
- U. Cˆot´e-Allard, C. L. Fall, A. Campeau-Lecours, C. Gosselin, F. Laviolette, and B. Gosselin, “Transfer learning forsEMG hand gestures recognition using convolutional neural networks,” in Proc. 2017 IEEE Int. Conf. Syst., Man,Cybern., 2017, pp. 1663–1668.
- H. Park et al., “A wireless magnetoresistive sensing system for an intraoral tongue-computer interface,” IEEE Trans. Biomed. Circuits Syst., vol. 6, no. 6, pp. 571–585, Dec. 2012.
- L. N. A. Struijk, L. L. Egsgaard, R. Lontis, M. Gaihede, and B. Bentsen, “Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia,” J. NeuroEng. Rehab., vol. 14, no. 1, p. 110, 2017
- M. N. Sahadat, A. Alreja, and M. Ghovanloo, “Simultaneous multimodal PC access for people with disabilities by integrating head tracking, speech recognition, and tongue motion,” IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 1, pp.192–201, Feb. 2018.
- B. Hudgins, P. Parker, and R. N. Scott, “A new strategy for multifunction myoelectric control,” IEEE Trans. Biomed. Eng., vol. 40, no. 1, pp. 82–94, Jan. 1993.
- Pedro Neto, J. Norberto Pires, A. Paulo Moreira, “Accelerometer-Based Control of an Industrial Robotic Arm” Available at: http://arxiv.org/ftp/arxiv/projects/1309/1309.2090.pdf
- Dr. R. V. Dharaskar, S. A. Chhabria, Sandeep Ganorkar, “Robotic Arm Control Using Gesture and Voice”, In International Journal of Computer, Information Technology & Bioinformatics (IJCITB), Vol. 1, Issue 1, pp. 41-46 Available at: http://www.ijcitb.com/issues/project_9.pdf
- S. Waldherr, R. Romero and S. Thrun, 2000, “A gesture based interface for human-robot interaction”, In Autonomous Robots in Springer, vol. 9, Issue 2, pp. 151-173.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST
This work is licensed under a Creative Commons Attribution 4.0 International License.