Voice Controlled Robotic Arm

Adwait P Naik


Robotic arms are programmable mechanical manipulators with movable components which can cause relative motion between adjoining links. The aim of this project is developing a robotic arm capable of identifying the voice commands. The speech acts as an input responsible for triggering action. Apart from having diverse applications in industries, with advancement in technology and medical sciences, the robotic prosthesis is highly successful at restoring one's biological ability to perform daily chores comfortably. This approach commits to a goal to accomplish a well-built system with minimum faults. Computing coordinates to attain a Soft-home position is an essential task which is responsible for achieving the required speed, torque, and delivers optimum performance. Most challenging part in the whole procedure is to obtain high calibration for smooth working of the arm. We have employed a software-based calibration technique which is simple to implement and highly efficient.


adjoining, calibration, manipulators,optimum, robotic prosthesis, soft-home, synergy, triggering.


Khatib, O., "Real -Time Obstacle Avoidance for Manipulators and Mobile Robots." 1985 IEEE International Conference on Robotics and Automation, March 25-28, 1985, St. Louis, pp. 500-505.

Borenstein, J. and Koren, Y., 1989, "Real-time Obstacle Avoidance for Fast Mobile Robots." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 5, Sept./Oct., pp. 1179-1187.

J.J.Hopfield, “Neural networks and physical systems with emergent collective computational abilities”, Proceedings of the National Academy

of Sciences of the United States of America, 79:2554, 1982.

Grossberg, S. (1988). Nonlinear neural networks: principles, mechanisms, and architecture. Neural Networks, 1, 17–61.

J. Pineau, M. Montemerlo, M. Pollack, N Roy, and S. Thrun. Towards robotic assistants in nursing homes: Challenges and results. Robotics

and Autonomous Systems, 42(3-4):271–281, March 2003.

O. Kanoun, J-P. Laumond, and E. Yoshida. Planning foot placements for a humanoid robot: A problem of inverse kinematics. International

Journal of Robotics Research, 30(4):476–485, 2011.

M. Kearns, Y. Mansour, and A. Ng. A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Machine

Learning, 49:193–208, 2002.

J. Schnupp, I. Nelken, and A. J. King, Auditory Neuroscience: Making Sense of Sound. Cambridge, MA, USA: MIT Press, 2012.

DOI: http://doi.org/10.11591/ijra.v9i3.pp%25p
Total views : 61 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

IJRA Visitor Statistics

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.