Abstract
Odor source localization is a problem of great importance. Two mainstream methods among numerous proposed ones are probabilistic algorithms and bio-inspired algorithms. Compared to probabilistic algorithms, biomimetic approaches are much less intensive in term of computational cost. Thus, despite their slightly worse performance, biomimetic approaches have received much more attention. In this paper, a novel method based on a bio-inspired algorithm - Particle Swarm Optimization (PSO) - is proposed for a multi-robot system (MRS). The proposed algorithm makes use of wind information and immediate odor gradient to enhance the performance of the MRS. A mechanism based on Artificial Potential Field (APF) is utilized to ensure non-collision movement of the robots. This method is tested by simulation on Matlab. Data for the test scenarios, all in large scales, are generated using Fluent. Nearly 2000 runs are carried out and the simulation results confirm the proposed algorithm’s effectiveness.
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References
Marques, L., Nunes, U., de Almeida, A.T.: Particle swarm-based olfactory guided search. Auton. Robots 20(3), 277–287 (2006)
Jatmiko, W., Sekiyama, K., Fukuda, T.: A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement. IEEE Comput. Intell. Mag. 2(2), 37–51 (2007)
Nakamoto, T. (ed.): Essentials of Machine Olfaction and Taste. Wiley, Chichester (2016)
Larcombe, M.H.E.: Robotics in nuclear engineering: computer-assisted teleoperation in hazardous environments with particular reference to radiation fields (1984)
Kowadlo, G.: Andrew Russell, R.: Robot odor localization: a taxonomy and survey. Int. J. Robot. Res. 27(8), 869–894 (2008)
Lochmatter, T., Martinoli, A.: Simulation experiments with bio-inspired algorithms for odor source localization in laminar wind flow. In: Seventh International Conference on Machine Learning and Applications, ICMLA 2008. IEEE (2008)
Marques, L., de Almeida, A.T.: Finding odours across large search spaces: a particle swarm-based approach. In: Armada, M.A., de González Santos, P. (eds.) Climbing and Walking Robots, pp. 419–426. Springer, Heidelberg (2005)
Gong, D.-W., et al.: Modified particle swarm optimization for odor source localization of multi-robot. In: 2011 IEEE Congress of Evolutionary Computation (CEC). IEEE (2011)
Gong, D.-W., Zhang, Y., Qi, C.-L.: Localising odour source using multi-robot and anemotaxis-based particle swarm optimisation. IET Control Theory Appl. 6(11), 1661–1670 (2012)
Vergassola, M., Villermaux, E., Shraiman, B.I.: Infotaxis as a strategy for searching without gradients. Nature 445(7126), 406–409 (2007)
Li, J.-G., et al.: Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton. Robots 30(3), 281–292 (2011)
Li, J.-G., et al.: Odor-source searching using a mobile robot in time-variant airflow environments with obstacles. In: 2014 33rd Chinese Control Conference (CCC). IEEE (2014)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986)
Acknowledgments
This work has been supported by Vietnam National University, Hanoi (VNU), under Project No. QG.15.25.
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Hoang, AQ., Pham, MT. (2017). Swarm Intelligence-Based Approach for Macroscopic Scale Odor Source Localization Using Multi-robot System. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_63
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DOI: https://doi.org/10.1007/978-3-319-49073-1_63
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