Abstract
This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesian-inference-based OSL method.
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Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50, 174–188.
Consi, T. R., Atema, J., Goudey, C. A., Cho, J., & Chryssostomidis, C. (1994). AUV guidance with chemical signals. In Proceedings of 1994 symposium on autonomous underwater vehicle technology (pp. 450–455).
Farrell, J. A., Pang, S., & Li, W. (2005). Chemical plume tracing via an autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 30, 428–442.
Ferri, G., Caselli, E., Mattoli, V., Mondini, A., Mazzolai, B., & Dario, P. (2009). SPIRAL: a novel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow. Robotics and Autonomous Systems, 57, 393–402.
Grasso, F. W., Consi, T. R., Mountain, D. C., & Atema, J. (2000). Biomimetic robot lobster performs chemo-orientation in turbulence using a pair of spatially separated sensors: progress and challenges. Robotics and Autonomous Systems, 30(1), 115–131.
Hayes, A. T., Martinoli, A., & Goodman, R. M. (2003). Swarm robotic odor localization: off-line optimization and validation with real robots. Robotica, 21, 427–441.
Holland, O., & Melhuish, C. (1996). Some adaptive movements of animats with single symmetrical sensors. In Proceedings of 4th conference on simulation and adaptive behavior—from animals to animats (Vol. 4, pp. 55–64).
Ishida, H., Suetsugu, K., Nakamoto, T., & Moriizumi, T. (1994). Study of autonomous mobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sensors and Actuators. A, Physical, 45, 153–157.
Ishida, H., Nakamoto, T., & Moriizumi, T. (1998). Remote sensing of gas/odor source location and concentration distribution using mobile system. Sensors and Actuators. B, Chemical, 49, 52–57.
Kahn, H. (1956). Use of different Monte Carlo sampling techniques. In H. A. Meyer (Ed.), Symposium on Monte Carlo methods (pp. 146–190). New York: Wiley.
Kowadlo, G., & Russell, R. A. (2006). Using naïve physics for odor localization in a cluttered indoor environment. Autonomous Robots, 20(3), 215–230.
Kowadlo, G., & Russell, R. A. (2009). Improving the robustness of naïve physics airflow mapping using Bayesian reasoning on a multiple hypothesis tree. Robotics and Autonomous Systems, 57, 723–737.
Li, F., Meng, Q. H., Bai, S., Li, J. G., & Popescu, D. (2008). Probability-PSO algorithm for multi-robot based odor source localization in ventilated indoor environments. In Lecture notes in computer science: Vol. 5314. Lecture notes in artificial intelligence and lecture notes in bioinformatics (pp. 1206–1215).
Li, J. G., Meng, Q. H., Li, F., Zeng, M., & Popescu, D. (2009). Mobile robot based odor source localization via particle filter. In Proceedings of IEEE international conference on decision and control (pp. 2984–2989).
Li, W., Farrell, J. A., Pang, S., & Arrieta, R. M. (2006). Moth-inspired chemical plume tracing on an autonomous underwater vehicle. IEEE Transactions on Robotics, 22, 292–307.
Lilienthal, A. J., & Duckett, T. (2004). Building gas concentration gridmaps with a mobile robot. Robotics and Autonomous Systems, 48, 3–16.
Lilienthal, A. J., Loutfi, A., & Duckett, T. (2006). Airborne chemical sensing with mobile robots. Sensors, 6, 1616–1678.
Marques, L., Nunes, U., & de Almeida, A. T. (2006). Particle swarm-based olfactory guided search. Autonomous Robots, 20(3), 277–287.
Pang, S., & Farrell, J. A. (2006). Chemical plume source localization. IEEE Transactions on Systems, Man and Cybernetics. Part B. Cybernetics, 36, 1068–1080.
Russell, R. A., Thiel, D., & Mackay-Sim, A. (1994). Sensing odour trails for mobile robot navigation. In Proceedings of IEEE international conference on robotics and automation (pp. 2672–2677).
Russell, R. A., Thiel, D., Deveza, R., & Mackay-Sim, A. (1995). A robotic system to locate hazardous chemical leaks. In Proceedings of IEEE international conference on robotics and automation (pp. 556–561).
Russell, R. A., Bab-Hadiashar, A., Shepherd, R. L., & Wallace, G. G. (2003). A comparison of reactive chemotaxis algorithms. Robotics and Autonomous Systems, 45(2), 83–97.
Sandini, G., Lucarini, G., & Varoli, M. (1993). Gradient-driven self-organizing systems. In Proceedings of IEEE/RSJ international conference on intelligent robots systems (pp. 429–432).
Vergassola, M., Villermaux, E., & Shraiman, B. I. (2007). ‘Infotaxis’ as a strategy for searching without gradients. Nature, 445, 406–409.
Zarzhitsky, D., Spears, D. F., & Spears, W. M. (2005). Distributed robotics approach to chemical plume tracing. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 4034–4039).
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Li, JG., Meng, QH., Wang, Y. et al. Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton Robot 30, 281–292 (2011). https://doi.org/10.1007/s10514-011-9219-2
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DOI: https://doi.org/10.1007/s10514-011-9219-2