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Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms

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Abstract

In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.

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Notes

  1. In the current work, only the information provided by a Figaro TGS2620 metal oxide gas sensor was employed.

  2. Results obtained in Matlab are not shown.

  3. More information about surfer can be found in the Surfer manual.

  4. The same coding—bold stars = source locations, colored triangles = final robot positions—are used in all the plots in this section.

  5. The distances in Tables 45, and 6 are in meters.

References

  1. Oyekan, J., Hu, H., & Gu, D. (2009, December). Exploiting bacteria swarms for pollution mapping. In IEEE International Conference on Robotics and Biomimetics (pp. 39–44). Guilin, China.

  2. Sierakowsk, C. A., & Coelho, L. S. (2006). Path planning optimization for mobile robots based on bacteria colony approach. Applied soft computing technologies: The challenge of complexity, 34, 187–198.

    Article  Google Scholar 

  3. Wu, C., Zhang, N., Jiang, J., Yang, J., & Liang, Y. (2007, April). Improved bacterial foraging algorithms and their applications to job shop sheduling problems. In International Conference on Adaptive and Natural Computing Algorithms. Series: Lecture notes in computer science, Vol. 4431 (pp. 562–569). Warsaw, Poland.

  4. Dorigo, M., Birattari, M., & Stützle, T. (2006). Ant colony optimization artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Google Scholar 

  5. Hayes, A. T., Martinoli, A., & Goodman, R. M. (2002). Distributed odor source localization. IEEE Sensor Journal, 2(3), 260–271.

    Article  Google Scholar 

  6. Farrell, J. A., Pang, S., & Li, W. (2003). Plume mapping via hidden markov methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 33(6), 850–863.

    Article  Google Scholar 

  7. Pang, S., & Farrell, J. A. (2006). Chemical plume source localization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(5), 1068–1080.

    Article  Google Scholar 

  8. Lilienthal, A., & Ducket, T. (2004). Building gas concentration gridmaps with mobile robot. Robotics and Autonomous Systems, 48(1), 3–16.

    Article  Google Scholar 

  9. Loutfi, A., Coradeschi, S., Lilienthal, A. J., & Gonzalez, J. (2009). Gas distribution mapping of multiple odour sources using a mobile robot. Robotica, 27(2), 311–319.

    Article  Google Scholar 

  10. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks (pp. 1942–1948).

  11. Marques, L., Nunes, U., & de Almedia, A.T. (2004, September) Finding odours across large search spaces: A particle swarm-based approach. In International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR2004) (pp. 419–426). Madrid, Spain: Springer.

  12. Marques, L., Nunes, U., & de Almedia, A. T. (2006). Particle swarm-based olfactory guided search. Autonomous Robots, 20(3), 277–287.

    Article  Google Scholar 

  13. Pugh, J., & Martinoli, A. (2007). Inspiring and modeling multi-robot seach with particle swarm optimization. In IEEE Swarm Intelligence Symposium (SIS 2007). Honolulu, USA.

  14. Pugh, J., & Martinoli, A. (2008). Distributed adaptation in multi-robot search using particle swarm optimization. In 10th International Conference on the Simulation of Adaptive Behavior (pp. 393–402). Osaka, Japan: Springer.

  15. Hereford, J. M. (2006). A distributed particle swarm optimization algorithm for swarm robotics application. In Congress on Evolutionary Computation (pp. 6143–6149). Vancouver, Canada.

  16. Hereford, J. M., & Siebold, M. (2008). Multi-robot search using a physically-embedded particle swarm optimization. International Journal of Computational Intelligence Research, 4(2), 179–209.

    Article  Google Scholar 

  17. Doctor, S., Venayagamoorthy, G. K., & Gudise, V. G. (2004). Optimal PSO for collective robotics search applications. In IEEE Congress on Evolutionary Computation (pp. 1390–1395). Portland, USA.

  18. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22(3), 52–67.

    Article  MathSciNet  Google Scholar 

  19. Berg, H. (2004). E. coli in motion. New York: Springer.

    Google Scholar 

  20. Dhariwal, A., Sukhatme, G. S., & Requicha, A. A. (2004, April). Bacterium-inspired robots for environmental monitoring. In IEEE International Conference on Robotics and Automation (pp. 1436–1443). Louisiana, USA.

  21. Meng, Q.-H., Li, J.-C., Li, F., & Zeng, M. (2006, December). Mobile robots odor localization with an improved ant colony algorithm. In IEEE International Conference on Robotics and Biomimetics (pp. 959–964). Kunming, China.

  22. Zou, Y., Luo, D., & Chen, W. (2009, December) Swarm robotic odor source localization using ant colony algorithm. In IEEE International Conference on Control and Automation (pp. 792–796). Christchurch, New Zealand.

  23. Turduev, M., Atas, Y., Sousa, P., Gazi, V., & Marques, L. (2010, October). Cooperative chemical concentration map building using decentralized asynchronous particle swarm optimization based search algorithm by mobile robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010) (pp. 4175–4180). Taipei, Taiwan.

  24. Turduev, M., Kırtay, M., Sousa, P., Gazi, V., & Marques, L. (2010, October) Chemical concentration map building through bacterial foraging optimization based search algorithm by mobile robots. In IEEE International Conference on Systems, Man, and Cybernetics (SMC 2010) (pp. 3242–3249). Istanbul, Turkey.

  25. Kırtay, M., Turduev, M., Sousa, P., Gazi, V., & Marques, L. (2010, September). Chemical concentration map building through ant colony optimization based search algorithm. In Turkish National Conference on Automotic Control (TOK 2010) (pp. 180–186). Kocaeli, Turkey (in Turkish).

  26. Pascoal, J., Sousa, P., & Marques, L. (2008, September). Khenose—a smart transducer for gas sensing. In International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR2008) (pp. 993–1000). Coimbra, Portugal: World Scientific Press.

  27. Borenstein, J., & Feng, L. (1995, August). Correction of systematic dead-reckoning errors in mobile robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 95) (pp. 569–574). Pittsburgh, USA.

  28. Borenstein, J., & Feng, L. (1995, October). Umbmark: A benchmark test for measuring deadreckoning errors in mobile robots. In SPIE Conference on Mobile Robots. Pennsylvania, USA.

  29. Atas, Y. (2010). Localization and mapping in mobile robot systems. Unpublished master’s thesis, TOBB University of Economics and Technology. Turkey (in Turkish).

  30. Marques, L., Almeida, N., & de Almeida, A. T. (2003, October). Olfactory sensory system for odour-plume tracking and localization. In IEEE International Conferance on Sensors (pp. 418–423). Toronto, Canada.

  31. Almeida, N., Marques, L., & de Almeida, A. T. (2003). Fast identification of gas mixtures through the processing of transient responses of an electronic nose. In EuroSensors. Guimaraes, Portugal.

  32. Barsan, N., & Tomescu, A. (1995). Calibration procedure for \(\rm {SnO_2}\)-based gas sensors. Thin Solid Films, 259(1), 91–95.

    Article  Google Scholar 

  33. Samiloglu, A. T., Gazi, V., & Koku, A. B. (2008). Comparison of three orientation agreement strategies in self-propelled particle systems with turn angle restrictions in synchronous and asynchronous settings. Asian Journal of Control, 10(2), 212–232.

    Article  MathSciNet  Google Scholar 

  34. Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.

    Article  Google Scholar 

  35. Akat, S. B., Gazi, V., & Marques, L. (2010). Asynchronous particle swarm optimization based search with a multi-robot system: Simulation and implementation on real robotic system. Turkish Journal of Electrical Engineering and Computer Sciences, 18(5), 749–764.

    Google Scholar 

  36. Akat, S. B., & Gazi, V. (2008, September). Particle swarm optimization with dynamic neighbourhood topology:three neighborhood strategies and preliminary results. In IEEE Swarm Intelligence Symposium (SIS-2008). St. Louis, USA.

  37. Akat, S. B., & Gazi, V. (2008, September). Decentralized asynchronous particle swarm optimization. In IEEE Swarm Intelligence Symposium (SIS-2008). St.Louis, USA.

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Acknowledgments

Part of this work was performed at the time V. Gazi was affiliated with and M. Kırtay was a visiting intern at TOBB ETU. The authors would like to thank Yunus Ataş, Pedro Sousa, and Bruno Antunes for their help in software and hardware development during various stages of this study.

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Correspondence to Veysel Gazi.

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This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 106E122 and by the European Commission under the GUARDIANS Project (FP6 contract No. 045269).

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Turduev, M., Cabrita, G., Kırtay, M. et al. Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms. Auton Agent Multi-Agent Syst 28, 72–100 (2014). https://doi.org/10.1007/s10458-012-9213-x

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