single-jc.php

JACIII Vol.10 No.2 pp. 181-186
doi: 10.20965/jaciii.2006.p0181
(2006)

Paper:

Neural Network Vision-Guided Mobile Robot for Retrieving Driving-Range Golf Balls

Elmer P. Dadios*, Kaoru Hirota**, Michelle L. Catigum***,
Albert C. Gutierrez****, Danison R. Rodrigo***,
Christopher Allan G. San Juan*****, and Jeffrey T. Tan******

*Department of Manufacturing Engineering and Management, De La Salle University, 2401 Taft Avenue, De La Salle University, Manila 1004, Philippines

**Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

***ROHM LSI Design Philippines Inc.

****Accenture Philippines

*****Intel Technology Philippines Inc.

******Lexmark R&D Corp. Philippines

Received:
January 8, 2005
Accepted:
August 25, 2005
Published:
March 20, 2006
Keywords:
neural network vision, autonomous mobile robot, driving-range golf ball retrieval robot
Abstract

We developed an autonomous mobile robot with neural network (NN) vision that searches for and collects golf balls on an open or an indoor golf driving range. The robot recognizes range borderlines by red stripes. Scattered golf balls are collected using mechanically designed rotating blades. The NN vision identifies objects that are not golf balls and prevents the robot from picking them. The vision system is robust enough to navigate an open field and pick up the golf balls any time of day. Results of the experiments showed that our proposal operates accurately and reliably.

Cite this article as:
Elmer P. Dadios, Kaoru Hirota, Michelle L. Catigum,
Albert C. Gutierrez, Danison R. Rodrigo,
Christopher Allan G. San Juan, and Jeffrey T. Tan, “Neural Network Vision-Guided Mobile Robot for Retrieving Driving-Range Golf Balls,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.2, pp. 181-186, 2006.
Data files:
References
  1. [1] S. Haykin, “Neural Networks, a Comprehensive Foundation,” MacMillan Publishing, Englewood Cliffs, NJ, 1994.
  2. [2] M. L. Minsky, and S. Papert, “Perceptrons: An Introduction to Computational Geometry,” MIT Press, Cambridge, MA, USA, 1969.
  3. [3] F. Rosenblatt, “Principles of Neurodynamics,” Spartan Books, Washington DC, USA, 1962.
  4. [4] F. O. Karray, and C. De Silva, “Soft Computing and Intelligent Systems Design,” Pearson Addison Wesley, 2004.
  5. [5] E. Aarts, “Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing,” J. Wiley and Sons, New York, USA, 1989.
  6. [6] N. Lacar, S. Olano, and E. P. Dadios, “Application of Artificial Neural Network to Ternary Distillation in a Packed Column,” Proceedings on the Regional Symposium on Chemical Engineering 1999 and The 9th National Chemical Engineering and Applied Chemistry Conference, B. P. Samila Beach Hotel, Songkhla, Thailand, B25-1 to B25-7, November 22-24, 1999.
  7. [7] D. Hoang, “Neural Networks for Network Topological Design,” Proceedings of the Int. Workshop on Applications of Neural Networks to Telecommunications, pp. 207-214, June, 1997.
  8. [8] Y. Park, V. Cherkassky, and G. Lee, “Omega Network-Based ATM switch with neural network controlled bypass queueing and multiplexing,” IEEE Journal selected areas in communication, Vol.12, No.9, pp. 1471-1480, December, 1994.
  9. [9] W. Farag, V. Quintina, and T. Lambert, “Enhancing the transient stability of multi machine power systems using intelligent techniques,” Bulk Power Systems Dynamics and Control IV Restructuring Symposium Proceedings, Athens, Greece, pp. 117-125, 1998.
  10. [10] Y. Zhang, G. P. Chen, O. P. Malik, and G. S. Hope, “An artificial neural network-based adaptive power system stabilizer,” IEEE Transactions on Energy Conversion, Vol 8, No.1, pp. 71-79, March, 1993.
  11. [11] L. Palomar, T. Fukuda, and E. P. Dadios, “A comparative Analysis of the Topological Structures of LPC-Based Feature Parameters of Speech,” Proceedings of the IEEE 1999 International Joint Conference on Neural Networks (IJCNN’99), Renaissance Hotel, Washington D.C., USA, July 10-16, 1999.
  12. [12] E. P. Dadios, et al. “Vision Guided Gantry Robot Using Neural Networks,” Proceedings of the managing Enterprises-Stakeholders, Engineering, Logistics and Achievement (MESELA’97) International Conference, Loughborough University, Loughborough, United Kingdom, pp. 663-675, July 22-24, 1997.
  13. [13] G. Auda, and M. Kamel, “EVOL: ensemble voting on line,” International Conference on Neural Networks, Vol.4, pp. 1356-1360, 1998.
  14. [14] H. Drucke, R. Schapire, and P. Simard, “Boosting performance in neural network,” Journal of Pattern Recognition and Artificial Intelligence, Vol.7, No.4, pp. 704-709, 1993.
  15. [15] P. Janakiraman, “Robotics and Image Processing: An Introduction,” New Delhi : McGraw-Hill Pub., 1995.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2021