Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T16:47:54.322Z Has data issue: false hasContentIssue false
  • English
  • Français

Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

Published online by Cambridge University Press:  27 July 2021

Denglong Ma Open the ORCID record for Denglong Ma [Opens in a new window] ,
Weigao Mao ,
Wei Tan ,
Jianmin Gao ,
Zaoxiao Zhang  and
Yunchuan Xie
Denglong Ma*
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, P.R. China
Weigao Mao
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, P.R. China
Wei Tan
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, P.R. China
Jianmin Gao
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, P.R. China
Zaoxiao Zhang
Affiliation:
School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
Yunchuan Xie
Affiliation:
School of Chemistry, Xi’an Jiaotong University, Xi’an, P.R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40–50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag–Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag–Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L0 has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag–Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag–Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.

Keywords

source tracingmobile sensorbionic algorithmsensor arrayAOS
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 976 - 996
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hutchinson, M., Oh, H. and Chen, W. H., “A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors Inform,” Fusion 36, 130–148 (2017). doi: 10.1016/j.inffus.2016.11.010.CrossRefGoogle Scholar
Haupt, S. E., “A demonstration of coupled receptor/dispersion modeling with a genetic algorithm,” Atmos. Environ. 39, 7181–7181 (2005). doi: 10.1016/j.atmosenv.2005.08.027.CrossRefGoogle Scholar
Ma, D. L., Gao, J. M., Zhang, Z. X and Wang, Q. S., “An improved firefly algorithm for gas emission source parameter estimation in atmosphere,” IEEE Access 7, 111923–111930 (2019). doi: 10.1109/ACCESS.2019.2935308.CrossRefGoogle Scholar
Ma, D. L., Deng, J. Q. and Zhang, Z. X., “Comparison and improvements of optimization methods for gas emission source identification,” Atmos. Environ. 81, 188–198 (2013). doi: 10.1016/j.atmosenv.2013.09.012.CrossRefGoogle Scholar
Hazart, A., Giovannelli, J. F., Dubost, S. and Chatellier, L., “Inverse transport problem of estimating point-like source using a Bayesian parametric method with MCMC,” Signal Process. 96, 346–361 (2014). doi: 10.1016/j.sigpro.2013.08.013.CrossRefGoogle Scholar
Kopka, P., Wawrzynczak, A. and Borysiewicz, M., “Application of the Approximate Bayesian Computation methods in the stochastic estimation of atmospheric contamination parameters for mobile sources,” Atmos. Environ. 145, 201–212 (2016). doi: 10.1016/j.atmosenv.2016.09.029.CrossRefGoogle Scholar
Wang, R. X., Chen, B., Qiu, S. H., Ma, L., Zhu, Z. Q., Wang, Y. P. and Qiu, X. G., “Hazardous source estimation using an artificial neural network, particle swarm optimization and a simulated annealing algorithm,” Atmosphere 9(20), 119 (2018). doi: 10.3390/atmos9040119.CrossRefGoogle Scholar
Ma, D. L., Wang, S. M. and Zhang, Z. X., “Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere,” Atmos. Environ. 94, 637–646 (2014). doi: 10.1016/j.atmosenv.2014.05.034.CrossRefGoogle Scholar
Ma, D. L., Tan, W., Zhang, Z. X and Hu, J., “Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm,” J. Hazard. Mater. 325, 239–250 (2017). doi: 10.1016/j.jhazmat.2016.11.071.CrossRefGoogle ScholarPubMed
Flesch, T. K., Wilson, J. D. and Harper, L. A., “Deducing ground-to-air emissions from observed trace gas concentrations: A field trial with wind disturbance,” J. Appl. Meteorol. 44, 475–484 (2005). doi: 10.1175/JAM2214.1.CrossRefGoogle Scholar
Yang, Z. Y., Fumihiro, S. and Kenshi, H., “A robot equipped with a high-speed LSPR gas sensor module for collecting spatial odor information from on-ground invisible odor sources,” ACS Sens. 6, 1174–1181 (2018). doi: 10.1021/acssensors.8b00214.CrossRefGoogle Scholar
Kowadlo, G. and Russell, R. A., “Robot odor localization: A taxonomy and survey,” Int. J. Robot. Res. 27, 869–894 (2008). doi: 10.1177/0278364908095118.CrossRefGoogle Scholar
Lilienthal, A., Reimann, D. and Zell, A., “Gas Source Tracing With a Mobile Robot Using an Adapted Moth Strategy,” In: Autonome Mobile Systeme. Informatikaktuell (Dillmann, R., Wörn, H. and Gockel, T., eds.) (Springer, Berlin, Heidelberg, 2003) pp. 150–160. doi: 10.1007/978-3-642-18986-9_16.Google Scholar
Holland, O. and Melhuish, C. “Some Adaptive Movements of Animats with Single Symmetrical Sensors,” Proceedings of 4th Conference on Simulation and Adaptive Behavior from Animals to Animats, vol. 4 (1996) pp. 55–64.Google Scholar
Ishida, H., Nakamoto, T., Moriizumi, T., Kikas, T. and Janata, J., “Plume-tracking robots: A new application of chemical sensors,” Biol. Bull. 200, 222–226 (2001). doi: 10.2307/1543320.CrossRefGoogle Scholar
Ishida, H., Hayashi, K., Takakusaki, M., Nakamoto, T., Moriizumi, T. and Kanzaki, R., “Odour-source localization system mimicking behaviour of silkworm moth,” Sens. Actuators A (Phys.) A51, 225–230 (1995). doi: 10.1016/0924-4247(95)01220-6.CrossRefGoogle Scholar
Russell, R. A., Bab-Hadiashar, A., Shepherd, R. L. and Wallace, G. G., “A comparison of reactive robot chemotaxis algorithms,” Rob. Auto. Syst. 45, 83–97 (2003). doi: 10.1016/S0921-8890(03)00120-9.CrossRefGoogle Scholar
Russell, R. A., “Locating Underground Chemical Sources by Tracking Chemical Gradients in 3 Dimensions,” Proceedings of the IEEE/RSJ International Conference on Intelli-gent Robots and Systems, Sendai, Japan, vol. 1 (2004) pp. 325–330. doi: 10.1109/IROS.2004.1389372.CrossRefGoogle Scholar
Lytridis, C., Virk, G. S., Rebour, Y. and Kadar, E. E., “Odor-based navigational strategies for mobile agents,” Adapt. Behav. 9, 171–187 (2001). doi: 10.1177/10597123010093004.CrossRefGoogle Scholar
Pasternak, Z., Bartumeus, F. and Grasso, F. W., “Levy-taxis: A novel search strategy for finding odor plumes in turbulent flow-dominated environments,” J. Phys. Math. Theor. 42 (2009). doi: 10.1088/1751-8113/42/43/434010.CrossRefGoogle Scholar
Ishida, H., Kagawa, Y., Nakamoto, T. and Moriizumi, T., “Odor-source localization in the clean room by an autonomous mobile sensing system,” Sens. Actuators B-Chem. 33, 115–121 (1996). doi: 10.1016/0925-4005(96)01907-7.CrossRefGoogle Scholar
Dusenbery, D. B., “Upwind searching for an odor plume is sometimes optimal,” J. Chem. Ecol. 16, 1971–1976 (1990). doi: 10.1007/BF01020509.CrossRefGoogle Scholar
Geier, M., Bosch, O. J. and Boeckh, J., “Influence of odour plume structure on upwind flight of mosquitoes towards hosts,” J. Exp. Biol. 202, 1639–1648 (1999). doi: 10.2139/ssrn.1434825.CrossRefGoogle ScholarPubMed
Rodriguez, A. B., Ramirez, A. R. G., De Pieri, E. R., A. L. Lopez and D. C. de AlbornozA, “An approach for robot-based odor navigation,” J. Med. Biol. Eng. 32, 453–456 (2012). doi: 10.5405/jmbe.924.CrossRefGoogle Scholar
Kuwana, Y., Nagasawa, S., I. Shimoyama and R. Kanzaki “Synthesis of the pheromone-oriented behaviour of silkworm moths by a mobile robot with moth antennae as pheromone sensors,” Biosens. Bioelectr. 14, 195–202 (1999). doi: 10.1016/S0956-5663(98)00106-7.CrossRefGoogle Scholar
Pyk, P., Badia, S. B. I., Bernardet, U., Knusel, P., Carlsson, M., Gu, J., Chanie, E., Hansson, B. S., Pearce, T. C. and Verschure, P., “An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search,” Auton. Robot. 20, 197–213 (2006). doi: 10.1007/s10514-006-7101-4.CrossRefGoogle Scholar
Zarzhitsky, D., Spears, D., Spears, W. and Thayer, D., “A Fluid Dynamics Approach to Multi-Robot Chemical Plume Tracing,” Proceedings of the Third International Joint Conference on Au-tonomous Agents and Multiagent Systems, New York, NY, USA, vol. 3 (2004) pp. 1476–1477. doi: 10.1109/AAMAS.2004.14.CrossRefGoogle Scholar
Hernandez Bennetts, V., Lilienthal, A. J., Neumann, P. P. and Trincavelli, M., “Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?,” Front. Neuroeng. 4, 20 (2011). doi: 10.3389/fneng.2011.00020.Google ScholarPubMed
Yu, Q. Y., Cheng, L., Wang, X., Shang, C., Peng, R. and Zhu, Q. M., “Gas Plume Tracking of Micro-aerial Vehicle in Tunnel Environment,” 9th International Conference on Modelling, Identification and Control (ICMIC) 2017 in Kunming, Peoples R China, vol. 467 (2018) pp. 31–51. doi: 10.1007/978-981-10-7212-3_3.CrossRefGoogle Scholar
Kowadlo, G. and Russell, R. A., “Improving the robustness of naive physics airflow mapping, using Bayesian reasoning on a multiple hypothesis tree,” Rob. Auto. Syst. 57, 723–737 (2009). doi: 10.1016/j.robot.2008.10.019.CrossRefGoogle Scholar
Jiang, X. B., Meng, Q. H., Wang, Y., Zeng, M. and Li, W., “Numerical Simulation of Odor Plume in Indoor Ventilated Environments for Studying Odor Source Localization with Mobile Robots,” 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China (2012) pp. 1029–1033. doi: 10.1109/ROBIO.2012.6491104.Google Scholar
Ristic, B., Skvortsov, A. and Gunatilaka, A., “A study of cognitive strategies for an autonomous search,” Inf. Fusion 28, 1–9 (2016). doi: 10.1016/j.inffus.2015.06.008.CrossRefGoogle Scholar
Farrell, J. A., Li, W., Pang, S., Arrieta, R. and Ieee, I., “Chemical Plume Tracing Experimental Results with a REMUS AUV,” MTS/IEEE Conference on Celebrating the Past - Teaming Toward the Future, San Diego, CA (2003) pp. 962–968. doi: 10.1109/OCEANS.2003.178458.Google Scholar
Pang, S. and Farrell, J. A., “Chemical plume source localization,” IEEE Trans. Syst. Man Cybern. Part B-Cybern. 36, 1068–1080 (2006). doi: 10.1109/TSMCB.2006.874689.CrossRefGoogle Scholar
Zhao, Y., Chen, B., Zhu, Z. Q., Chen, F. R., Wang, Y. D. and Ma, D. L., “Entrotaxis-Jump as a hybrid search algorithm for seeking an unknown emission source in a large-scale area with road network constraint,” Exp. Syst. Appl. 157, 11 (2020). doi: 10.1016/j.eswa.2020.113484.CrossRefGoogle Scholar
Hutchinson, M., Oh, H. and Chen, W. H., “Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions,” Inf. Fusion 42, 179–189 (2018). doi: 10.1016/j.inffus.2017.10.009.CrossRefGoogle Scholar
Vergassola, M., Villermaux, E. and Shraiman, B. I., “‘Infotaxis’ as a strategy for searching without gradients,” Nature 445, 406–409 (2007). doi: 10.1038/nature05464.CrossRefGoogle ScholarPubMed
Moraud, E. M. and Martinez, D., “Effectiveness and robustness of robot infotaxis for searching in dilute conditions,” Front. Neurorobot. 4, 1 (2010). doi: 10.3389/fnbot.2010.00001.Google ScholarPubMed
Subchan, S., White, B. A., Tsourdos, A., Shanmugavel, M. and Żbikowski, R., “Dubins path planning of multiple UAVs for tracking contaminant cloud,” IFAC Proc. Vol. 41, 5718–5723 (2008). doi: 10.3182/20080706-5-KR-1001.2152.CrossRefGoogle Scholar
Cui, X., Hardin, C. T., Ragade, R. K. and Elmaghraby, A. S., “A Swarm Approach for Emission Sources Localization,” ICTAI 2004 : 16th IEEE International Conference on Tools with Artificial Intelligence, Proceedings (Khoshgoftaar, T. M., ed.) (2004) pp. 424–430. doi: 10.1109/ICTAI.2004.20.CrossRefGoogle Scholar
Yeon, A. S. A., Visvanathan, R., Mamduh, S. M., Kamarudin, K., Kamarudin, L. M. and Zakaria, A., “Implementation of Behaviour Based Robot with Sense of Smell and Sight,” 2015 IEEE International Symposium on Robotics and Intelligent Sensors (H. Yussof and M. F. Miskon, eds.) vol. 76 (2015) pp. 119–125. doi: 10.1016/j.procs.2015.12.300.Google Scholar
Chang, C. C. and Lin, C. J., “LIBSVM: A library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2, 27 (2011). doi: 10.1145/1961189.1961199.CrossRefGoogle Scholar
Fan, R. E., Chen, P. H. and Lin, C. J., “Working set selection using second order information for training support vector machines,” J. Mach. Learn. Res. 6, 1889–1918 (2005). doi: 10.1115/1.1898234.CrossRefGoogle Scholar
Ishida, H., Kagawa, Y., Nakamoto, T. and Moriizumi, T., “Odor-Source Localization in Clean Room by Autonomous Mobile Sensing System,” Proceedings of the 8th International Conference on Solid-State Sensors and Actuators, and Eurosensors IX, vol. 1 (1995) pp. 783–786. doi: 10.1109/SENSOR.1995.717349.CrossRefGoogle Scholar
Russell, R. A., “Chemical Source Location and the Robomole Project,” Proceedings of the Australasian Conference on Robotics and Automation (2003) pp. 1–6. doi:10.1.1.10.2433.Google Scholar
Farrell, J. A., Murlis, J., Long, X., Li, W. and Cardé, R. T., “Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes,” Environ. Fluid Mech. 2(1–2), 143–169 (2002).CrossRefGoogle Scholar
Farrell, J., Pang, S., Li, W. and Arrieta, R., “Biologically Inspired Chemical Plume Tracing on an Autonomous Underwater Vehicle,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 7 (2004) pp. 5991–5996.Google Scholar
Farrell, J. A., “Chemical plume tracing via an autonomous underwater vehicle,” IEEE J. Oceanic Eng. 30(1), 428–442 (2005).CrossRefGoogle Scholar
Marques, L., Nunes, U., and de Almeida, A., “Olfaction based mobile robot navigation,” Thin Solid Films 418(1), 51–58 (2002).CrossRefGoogle Scholar