Volume 8 Number 3
August 2011
Article Contents
Qiang Lu and Ping Luo. A Learning Particle Swarm Optimization Algorithm for Odor Source Localization. International Journal of Automation and Computing, vol. 8, no. 3, pp. 371-380, 2011. doi: 10.1007/s11633-011-0594-0
Cite as: Qiang Lu and Ping Luo. A Learning Particle Swarm Optimization Algorithm for Odor Source Localization. International Journal of Automation and Computing, vol. 8, no. 3, pp. 371-380, 2011. doi: 10.1007/s11633-011-0594-0

A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

  • Received: 2010-06-08
Fund Project:

This work was supported by National Natural Science Foundation of China (No.60675043), Natural Science Foundation of Zhejiang Province of China (No.Y1090426, No.Y1090956), and Technical Project of Zhejiang Province of China (No.2009C33045).

  • This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.
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  • [1] R. A. Russell, A. B. Hadiashar, R. L. Shepherd, G. G. Wallace. A comparison of reactive robot chemotaxis algorithms. Robotics and Autonomous Systems, vol.45, no.2, pp.83-97, 2003.
    [2] W. Li, J. A. Farrell, S. Pang, R. M. Arrieta. Moth-inspired chemical plume tracing on an autonomous underwater vehicle. IEEE Transactions on Robotics, vol.22, no.2, pp.292-307, 2006.
    [3] S. Edwards, A. J. Rutkowski, R. D. Quinn, M. A. Willis. Moth-inspired plume tracking strategies in three-dimensions. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Barcelona, Spain, pp.1669-1674, 2005.
    [4] G. Ferri, E. Caselli, V. Mattoli, A. Mondini, B. Mazzolai, P. Dario. Spiral: A novel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow. Robotics and Autonomous Systems, vol.57, no.4, pp.393-402, 2009.
    [5] G. Ferri, E. Caselli, V. Mattoli, A. Mondini, B. Mazzolai, P. Dario. A biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow: First experimental results. In Proceedings of IEEE International Conference on Robotics and Automation, Rome, Italy, pp.566-571, 2007.
    [6] A. T. Hayes, A. Martinoli, R. M. Goodman. Distributed odor source localization. IEEE Sensors Journal, vol.2, no.3, pp.260-271, 2002.
    [7] A. T. Hayes, A. Martinoli, R. M. Goodman. Swarm robotic odor localization: Off-line optimization and validation with real robots. Robotica, vol.21, no.4, pp.427-441, 2003.
    [8] J. D. Liu, H. S. Hu. Biologically inspired behaviour design for autonomous robotic fish. International Journal of Automation and Computing, vol.3, no.4, pp.336-347, 2006.
    [9] C. D. Wu, Y. Zhang, M. X. Li, Y. Yue. A rough set GA-based hybrid method for robot path planning. International Journal of Automation and Computing, vol.3, no.1, pp.29-34, 2006.
    [10] L. Marques, U. Nunes, A. T. Almeida. Particle swarm-based olfactory guided search. Autonomous Robots, vol.20, no.3, pp.277-287, 2006.
    [11] W. Jatmiko, K. Sekiyama, T. Fukuda. A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: Theory, simulation and measurement. IEEE Computational Intelligence Magazine, vol.2, no.2, pp.37-51, 2007.
    [12] W. Jatmiko, P. Mursanto, B. Kusumoputro, K. Sekiyama, T. Fukuda. Modified PSO algorithm based on flow wind for odor source localization problems in dynamic environments. WSEAS Transactions on Systems, vol.7, no.2, pp.106-113, 2008.
    [13] F. Li, Q. H. Meng, J. G. Li, S. Bai, M. Zeng. Multi-robot based chemical plume tracing with virtual odor-source-probability sensor. In Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, Tianjin, PRC, vol.1, pp.246-250, 2009.
    [14] J. A. Farrell, S. Pang, W. Li. Plume mapping via hidden Markov methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol.33, no.6, pp.850-863, 2003.
    [15] S. Pang, J. A. Farrell. Chemical plume source localization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol.36, no.5, pp.1068-1080, 2006.
    [16] J. A. Farrell, J. Murlis, X. Z. Long, W. Li, R. T. Carde. Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes. Environment Fluid Mechanics, vol.2, no.1-2, pp.143-169, 2002.
    [17] Q. Lu, S. R. Liu, X. N. Qiu. A distributed architecture with two layers for odor source localization in multi-robot systems. In Proceedings of IEEE World Congress on Evolutionary Computation, Barcelona, Spain, pp.153-159, 2010.
    [18] Q. Lu, Q. L. Han. A distributed coordination control scheme for odor source localization. In Proceedings of the 36th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Phoenix, USA, pp.1413-1418 2010.
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A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

Fund Project:

This work was supported by National Natural Science Foundation of China (No.60675043), Natural Science Foundation of Zhejiang Province of China (No.Y1090426, No.Y1090956), and Technical Project of Zhejiang Province of China (No.2009C33045).

Abstract: This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

Qiang Lu and Ping Luo. A Learning Particle Swarm Optimization Algorithm for Odor Source Localization. International Journal of Automation and Computing, vol. 8, no. 3, pp. 371-380, 2011. doi: 10.1007/s11633-011-0594-0
Citation: Qiang Lu and Ping Luo. A Learning Particle Swarm Optimization Algorithm for Odor Source Localization. International Journal of Automation and Computing, vol. 8, no. 3, pp. 371-380, 2011. doi: 10.1007/s11633-011-0594-0
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