Intelligent sensor modeling and data fusion via neural network and maximum likelihood estimation

American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC

Volumn 74 DSC, Issue 2 PART B, 2005, Pages 1759-1768

  Kumar, Manish ^a   Garg, Devendra P ^a   Zachery, Randy ^b  

^a DUKE UNIVERSITY   (United States)

^b U S ARMY RESEARCH OFFICE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION ALGORITHM; INFRA-RED (IR) PROXIMITY SENSOR; ROBOTIC WORKSPACE; SENSOR MODEL;

ALGORITHMS; FEATURE EXTRACTION; MAXIMUM LIKELIHOOD ESTIMATION; SENSORS; STATISTICS;

ARTIFICIAL INTELLIGENCE;

EID: 33645677978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/IMECE2005-80972     Document Type: Conference Paper

References (14)

1. Manyika, J. and Durrant-Whyte, H., Data Fusion and Sensor Management: a Decentralized Information-Theoretic Approach, Ellis Howard Limited, 1994.
2. Durrant-Whyte, H.F., Integration, Coordination and Control of Multi-Sensor Robot Systems, Kluwer Academic Publishers, Norwell, MA, 1988.
3. Porrill, J., "Optimal Combination and Constraints for Geometrical Sensor Data", The International Journal of Robotics Research, 1988, pp. 66-77.
4. Rumelhart, D. E. and McClelland, J. L., Explorations in Parallel Distributed Processing: a Handbook of Models, Programs, and Exercises (MIT Press), 1988.
5. Haykin, S. S., Neural Networks: A Comprehensive Foundation (Prentice Hall Press), 1998.
6. Garg, D., Ananthraman, S., and Prabhu, S., "Neural Network Applications", Wiley Encyclopedia of Electrical and Electronic Engineering, John G. Webster (ed.), Vol. 14 (John Wiley, New York, NY), 1999, pp. 255-265.
7. Hornik, K., Stinchombe, M., and White, H., "Multilayer Feedforward Network are Universal Approximators", Neural Networks, Vol. 2, 1989, pp. 359-366.
8. Irie, B. and Miyake, S., "Capabilities of Three-Layered Perceptrons", Proceedings of IEEE International Conference on Neural Networks, California, USA, 24-27 July, 1988, pp. 641-648.
9. th International Conference on Mechatronics and Machine Vision in Practice, Perth, Western Australia, December 9-11, 2003.
10. Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.-T., "A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry", Artificial Intelligence Journal, Vol.78, October 1995, pp. 87-119.
11. Elfes, A., "Multi-Source Spatial Data Fusion Using Bayesian Reasoning", Data Fusion in Robotics and Machine Intelligence, Abidi, M. A. and Gonzalez, R. A. (eds.), Academic Press, 1992.
12. Kumar, M. and Garg, D., "Intelligent Multi Sensor Fusion Techniques in Flexible Manufacturing Workcells", Proceedings of American Control Conference, 2004, Boston, MA, pp. 5375-5380.
13. HoseinNezhad, R., Moshiri, B., Asharif, M.R., "Sensor Fusion for Ultrasonic and Laser Arrays in Mobile Robotics: a Comparative Study of Fuzzy, Dempster and Bayesian Approaches", Proceedings of IEEE Conference on Sensors, Vol. 2, 12-14 June 2002, pp. 1682-1689.
14. Kumar, M. and Garg, D., "Three-Dimensional Occupancy Grid with the Use of Vision and Proximity Sensors in a Robotic Workcell", Paper Number IMECE2004-59593, Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Anaheim, CA, November 14-19, 2004, 8p.