RANGED SUBGROUP PARTICLE SWARM OPTIMIZATION FOR LOCALIZING MULTIPLE ODOR SOURCES

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

0
Reader(s)
0
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 3 , ISSUE 3 (September 2010) > List of articles

RANGED SUBGROUP PARTICLE SWARM OPTIMIZATION FOR LOCALIZING MULTIPLE ODOR SOURCES

W. Jatmiko * / W. Pambuko / A. Febrian / P. Mursanto / A. Muis / B. Kusumoputro / K. Sekiyama / T. Fukuda

Keywords : Modified Particle Swarm Optimization, Multiple Odor Sources Localization, Parallel Search, Subgroup, Open Dynamic Engine, Real Life Scenario

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 3, Issue 3, Pages 411-442, DOI: https://doi.org/10.21307/ijssis-2017-401

License : (CC BY-NC-ND 4.0)

Published Online: 13-December-2017

ARTICLE

ABSTRACT

A new algorithm based on Modified Particle Swarm Optimization (MPSO) that follows is a local gradient of a chemical concentration within a plume and follows the direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one subgroup locates one odor sources. This is inefficient because other subgroups locate other source, then we proposed a ranged subgroup method for coping for that problem, then the searching performance will increase. Finally ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and others so that the simulation adequate to accurately address the real life scenario.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1]M. Wandel, A. Lilienthal, T. Duckett, U. Weimar, and A. Zell. Gas distribution in unventilated indoor environments inspected by a mobile robot. In Proceedings of the IEEE International Conference on Advanced Robotics (ICAR’03), 2003.
[2]T. C. Pearce, S. S. Schiffman, H. T. Nagle, and J. W. Gardner, Handbook of Machine Olfaction: Electronic Nose Technology. Weinheim, Germany: Wiley VCH, 2002.
[3]J.W. Gardner and P. N. Bartlett, Electronic Nose: Principles and Applications. New York: Oxford Univ. Press, 1999.
[4]R. A. Russell, Odor Detection by Mobile Robots, World Scientific, Singapore, 1999.
[5]Ishida, H. Nakayama, G. Nakamoto and T. Moriizumi, T. Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors”, IEEE Sensors Journal, Vol. 5. No.3. June 2005.
[6]Adam T. Hayes, A. Martinoli and R. M. Goodman, “Distributed Odor Source Localization,” IEEE Sensors Journal, Vol. 2. No.3. June 2002.
[7]D. Zarzhitsky, D. Spears, and W. Spears. Distributed Robotics Approach to Chemical Plume Tracing. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05), 2005.
[8]Ishida, H. Nakayama, G. Nakamoto and T. Moriizumi, T. Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors”, IEEE Sensors Journal, Vol. 5. No.3. June 2005.
[9] Achim Lilienthal and Tom Duckett,” Building Gas Concentration Gridmaps with a Mobile Robot”, Robotics and Autonomous Systems, Volume 48, Issue 1, 31 August 2004, pp. 3-16
[10] Achim Lilienthal and Tom Duckett,” Experimental Analysis of Gas-Sensitive Braitenberg Vehicles”, Advanced Robotics, Volume 18, Number 8, 1 December 2004, pp. 817-834
[11]Wisnu Jatmiko, K. Sekiyama and T. Fukuda, "Modified Particle Swarm Robotic for Odor Source Localization in Dynamic Environment ", The International Journal of Intelligent Control and Systems: Special Issue on Swarm Robotic, Vol. 11, No 3, pp.176-184, September 2006.
[12]Wisnu Jatmiko, K. Sekiyama and T. Fukuda," A PSO-based Mobile Robot for Odor Source Localization in Extreme Dynamic Advection-Diffusion Environment with Obstacle: Theory, Simulation and Measurement ". IEEE Computational Intelligence Magazine: Special Issue on Biometric. Vol. 2, Issue 2, pp. 37-51, May 2007.
[13]Wisnu Jatmiko, Petrus Mursanto, Benyamin Kusumoputro, K. Sekiyama and T. Fukuda,” Modified PSO Algorithm Based on Flow of Wind for Odor Source Localization Problems in Dynamic Environments “, WSEAS Transaction on System, Issue 3, Volume 7, pp. 106-113, March 2008.
[14]R. Brits, A. P. Engelbrecht and F. van den Bergh," Locating multiple optima using particle swarm optimization". Applied Mathematics and Computation, vol. 189 (2007), pp.1859-1883.
[15]Konstantinos E. Parsopoulus and Michael N. Vrahatis," On the computation of all global minimizers through using particle swarm optimization". IEEE Trans. on Evolutionary Computation, vol. 8, no. 3, June 2004, pp.211-224.
[16]Jay A. Farrel et all, "Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes," Environment Fluid Mechanics, vol. 2, pp. 143-169, 2002.
[17]Wisnu Jatmiko, T. Fukuda, F. Arai, and B. Kusumoputro, “Artificial Odor Discrimination System Using Multiple Quartz Resonator Sensor and Various Neural Networks for Recognizing Fragrance Mixtures, IEEE Sensors Journal, vol. 6. no. 1, pp. 223–233, Feb. 2006.
[18]Wisnu Jatmiko, T. Fukuda, T. Matsuno, F. Arai and B. Kusumoputro,” Robotic Applications for Odor-Sensing Technology: Progress and Challenge, “WSEAS Transaction on System, Issue 7, Volume 4, pp. 1134-1141, July 2005.
[19]T. Fukuda and T. Ueyama, Cellular Robotics and Micro Robotic Systems, World Scientist, Series in Robotic and Automated Systems Vol. 10, World Scientific (1994).
[20]K. Sekiyama and T. Fukuda,”Hierarchical prediction model for intelligent communication in multiple robotics systems”, Robotics and Autonomous Systems, Elsevier, Vol. 17, pp. 87-98, 1996.

EXTRA FILES

COMMENTS