We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Autonomous Navigation and Teleoperation in Robots using Machine Learning

Published on February 2013 by Karthikeyan. D
National Conference on Future Computing 2013
Foundation of Computer Science USA
NCFC - Number 1
February 2013
Authors: Karthikeyan. D
075ef272-290b-4891-82ac-0438f0b9b756

Karthikeyan. D . Autonomous Navigation and Teleoperation in Robots using Machine Learning. National Conference on Future Computing 2013. NCFC, 1 (February 2013), 5-9.

@article{
author = { Karthikeyan. D },
title = { Autonomous Navigation and Teleoperation in Robots using Machine Learning },
journal = { National Conference on Future Computing 2013 },
issue_date = { February 2013 },
volume = { NCFC },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 5-9 },
numpages = 5,
url = { /proceedings/ncfc/number1/10401-1002/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Future Computing 2013
%A Karthikeyan. D
%T Autonomous Navigation and Teleoperation in Robots using Machine Learning
%J National Conference on Future Computing 2013
%@ 0975-8887
%V NCFC
%N 1
%P 5-9
%D 2013
%I International Journal of Computer Applications
Abstract

Human-robot interaction most challenging task such as control, monitoring and navigation. We explore the unique challenges posed by the remote operation of robots. Teleoperation widely make use of short messaging service, this method not efficient for robotic control, monitoring and navigation. Rapid development in robotic technology effective monitoring, control and automated navigation are need, this paper we developed a system for the remote operation in robots is based on GPRS for monitoring and control. Robot act as artificial intelligent agent to avoid obstacle by using artificial intelligence approach namely machine learning algorithm called decision tree learning for automated navigation during absence of remote operator.

References
  1. Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita"Teleoperations on multiple social robots" IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 42, NO. 3, MAY 2012.
  2. M. Shiomi, T. Kanda, H. Ishiguro, and N. Hagita, "Interactive humanoid robots for a science museum," IEEE Intell. Syst. , vol. 22, no. 2, pp. 25–32, Mar. /Apr. 2007.
  3. Boon Siew Han, Hong Yee Wong Alvin, Yeow Kee Tan, Haizhou Li" Using Design Methodology to Enhance Interaction for a Robotic Receptionist" 19th IEEE International Symposium on Robot and Human Interactive Communication Principe di Piemonte Viareggio, Italy, Sept. 12-15, 2010
  4. W. Burgard et al. : "The interactive museum tour-guide robot,"National Conference on Artificial Intelligence, pp. 11-18, 1998.
  5. Johann Borenstein and Yoram Koren" Obstacle A voidance with Ultrasonic Sensors" IEEE JOURNAL OF ROBOTICS AND AUTOMATION, VOL. 4, NO. 2, APRIL I988.
  6. T. Fong and C. Thorpe, "Vehicle teleoperation interfaces," Autonomous. Robots, vol. 11, no. 1, pp. 9–18, Jul. 2001.
  7. D. B. Kaber and M. R. Endsley, "The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task," Theor. Issues Ergonom. Sci. , vol. 5, no. 2, pp. 113–153, Mar. /Apr. 2004.
  8. S. G. Hill and B. Bodt, "A field experiment of autonomous mobility:Operator workload for one and two robots," in Proc. ACM/IEEE 2nd Annu. Conf. HRI, 2007, pp. 169–176.
  9. T. Shiwa, T. Kanda, M. Imai, H. Ishiguro, and N. Hagita, "How quickly should communication robots respond?," in Proc. ACM/IEEE 3rd Annu. Conf. HRI, Amsterdam, the Netherlands, 2008, pp. 153–160.
  10. B. Brunner, G. Hirzinger, K. Landzettel, and J. Heindl, "Multisensory shared autonomy and tele-sensor programming—Key issues in the space robot technology experiment ROTEX," in Proc. IEEE/RSJ Int. Conf. IROS, 1993, pp. 2123–2139.
  11. Erick Swere and David J Mulvaney" Robot navigation Using Decision Trees" ELECTRONIC SYSTEMS AND CONTROL DIVISION RESEARCH 2003.
  12. S. Veera Ragavan , V. Ganapathy" A General Telematics Framework for Autonomous Service Robots" Proceedings of the 3rd Annual IEEE Conference on Automation Science and Engineering Scottsdale, AZ, USA, Sept 22-25, 2007.
  13. Baik, S. Bala, J. (2004), A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection, Lecture Notes in Computer Science, Volume 3046, Pages 206 – 212.
  14. Bruha. I. (2000), From machine learning to knowledge discovery: Survey of preprocessing and post processing. , Intelligent Data Analysis, Vol. 4, pp. 363-374.
  15. Dietterich, T. G. (1998), Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7) 1895–1924.
  16. Mitchell, T. (1997). Machine Learning. McGraw Hill, 13th reprint,2010.
Index Terms

Computer Science
Information Sciences

Keywords

Human- Robot Interaction Gprs Autonomous Navigation Machine Learning Decision Tree Learning Teleoperation