Call for Paper - June 2021 Edition
IJCA solicits original research papers for the June 2021 Edition. Last date of manuscript submission is May 20, 2021. Read More

Intelligent Low Cost Mobile Robot and Environmental Classification

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 12
Year of Publication: 2011
Siti Nurmaini

Siti Nurmaini. Article: Intelligent Low Cost Mobile Robot and Environmental Classification. International Journal of Computer Applications 35(12):1-7, December 2011. Full text available. BibTeX

	author = {Siti Nurmaini},
	title = {Article: Intelligent Low Cost Mobile Robot and Environmental Classification},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {12},
	pages = {1-7},
	month = {December},
	note = {Full text available}


In this paper low cost mobile robot is designed and developed. A tree diagram of material selection is used to help designer to determine the requirements of mobile robot process design. 5 pieces of low price infrared sensors and 8 bits low cost microcontroller-based system are utilized to process sensors signal and driving actuators to guide mobile robot movement. Fuzzy-Kohonen Network (FKN) method is embedded into the mobile robot as pattern recognition approach of 21 environmental classifications. We have fully implemented the system with a real mobile robot and made experiments for evaluating the mobile robot ability. As a result, we found out that the environment recognition is done well, that mobile robot successfully identified several environmental situations. Furthermore, our method is adaptive to noisy environments and produce satisfactory performance.


  • Bulusu, N.; Heidemann, J.; Estrin, D. 2002. GPS-less low-cost outdoor localization for very small devices. Personal Communication 5:28-34.
  • Agrawal, M., Konolige, K., and Iocchi, L. 2005. Real-time detection of independent motion using stereo. Proceeding IEEE workshop on Motion (WACV/MOTION).
  • Yamada, S., and Murota, M. 1998. Unsupervised Learning to Recognize : Environments from Behavior Sequences in a Mobile Robot. Proceedings on the IEEE International Conference on Robotics & Automation Leuven, Belgium. 1871-1876.
  • Cui, X., Hardin, T., Ragade, R.K., and Elmaghraby, A.S. 2004. A Swarm-based Fuzzy Logic Control Mobile Sensor Network for Hazardous Contaminants Localization. IEEE, 194-203.
  • Lazarus, S.B., Tsourdos, A., Zbikowski, R., and White, B.A. 2008. Unstructured environmental mapping using low cost sensors. Proceeding on IEEE International Conference on Networking, Sensing and Control, 1080-1085.
  • Yunfeng, W. and Gregory, S.C. 2000. A new potential field method for robot path planning. Proceedings of the IEEE International Conference on Robotics & Automation, San Francisco, CA, 977–982.
  • Liu, C., Marcelo, H.A.J., Krishnan, H., and Ser-Yong, L. 2000. Virtual obstacle concept for local-minimum-recovery in potential field based navigation. Proceedings of the 2000 IEEE International Conference on Robotics & Automation, San Francisco, CA, 2:983 –988.
  • Maravall, D., de Lope, J., and Serradilla, F. 2000. Combination of model-based and reactive methods in autonomous navigation. Proceedings of the IEEE Intern. Conf. Robotics & Automation, San Francisco, CA, 977–982.
  • Ryu, B.S., and Yang, H.S. 1999. Integration of reactive behaviors and enhanced topological map for robust mobile robot navigation. IEEE Transaction on System, Man, and Cybernetics—part A, 29(5):474–485.
  • Eduardo Kac, 2001a. The origin and development of robotic art. The Journal of research into New Media Technologies, 7(1):76-86.
  • Eduardo Kac, 2001b. Towards a chronology of robotic art. The Journal of research into New Media Technologies, 791:87-111.
  • Smith, C.W. 2002. Material design for a robotic arts studio. Master of science-Thesis-Massachusetts Institute of Technology.
  • Song, K.T., and Huang, S.Y. 2004. Mobile Robot Navigation Using Sonar Direction Weights. Proc. IEEE International Conference on Control Applications, Taiwan
  • Nwe, A.A., Aung, W.P., and Myint, Y.M. 2008. Software implementation of obstacle detection and avoidance system for wheeled mobile robot. World Academy of Science, Engineering and technology 42: 572-577.
  • Govers, E. 2007. Thesis – Fast local Mapping for mobile robots. Utrecht University and Philips Applied Technologies.
  • Song, K.T., and Sheen, L.W. 2000. Heuristic fuzzy-neuro network and its application to reactive navigation of a mobile robot. Fuzzy Sets and Systems, 110:331-340.
  • Kumar, M., and Garg. D.P. 2005. "Neuro-fuzzy control applied to multiple cooperating robots", Industrial Robot: An International Journal, 32(3):234 – 239.
  • Wang, H., Chen, C., dan Huang, Z. 2007. Ultrasonic Sensor Based Fuzzy-Neural Control Algorithm of Obstacle Avoidance for Mobile Robot. Springer-Verlag Berlin Heidelberg, 1:824-833.
  • Al Mutib, K. and Mattar, E. 2011. Neuro-fuzzy Controlled Autonomous Mobile Robotics System. Proceeding International conferences on computer modeling and simulation. Cambridge, March 30 - April 1.2011, 1-7.
  • Hoffmann, F. 2000. Soft computing techniques for the design of mobile robot behaviors. Information Science, 122: 241-258.
  • Tsai, C.C., Chen, C.C., Chan, C.K., and Li, Y.Y 2010. Behavior-Based Navigation Using Heuristic Fuzzy Kohonen Clustering Network for Mobile Service Robots. International Journal of Fuzzy Systems. 12(1): 25-32.