Abstract
Mobile robot-based odor source localization (OSL) has broad applications in industrial and daily-life scenarios. However, subject to the limited sensing capacity of common metal oxide semiconductor (MOS) sensors, the OSL robots still lag far behind their biological counterparts. In this paper, we rethink the odor-source direction estimation paradigm of odor compass and propose a deep neural network (DNN) based method to improve both the accuracy and the generalization ability. The odor compass is composed of four wireless MOS sensors, and a DNN model, which contains a convolutional neural network (CNN) module and a long short-term memory (LSTM) module. An OSL strategy is further designed based on the proposed odor compass. Experimental results validate the feasibility of the proposed method.
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References
Baetz, W., Kroll, A., Bonow G.: Mobile robots with active IR-optical sensing for remote gas detection and source localization. In: 2009 IEEE International Conference on Robotics and Automation, pp. 2773–2778. IEEE (2009)
Ishida, H., Suetsugu, K., Nakamoto, T., et al.: Study of autonomous mobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sens. Actuators A 45(2), 153–157 (1994)
Russell, R.A., Thiel, D., Deveza, R., et al.: A robotic system to locate hazardous chemical leaks. In: Proceedings of International Conference on Robotics and Automation, pp. 556–561, May 1995
Consi, T.R., Atema, J., Goudey, C.A., et al.: AUV guidance with chemical signals. In: Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV 1994), pp. 450–455. IEEE (1994)
Sandini, G., Lucarini, G., Varoli, M.: Gradient driven self-organizing systems. In: Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1993), vol. 1, pp. 429–432. IEEE (1993)
Nakamoto, T., Ishida, H., Moriizumi, T.: An odor compass for localizing an odor source. Sens. Actuators B Chem. 35(1–3), 32–36 (1996)
Ishida, H., Kobayashi, A., Nakamoto, T., et al.: Three-dimensional odor compass. IEEE Trans. Robot. Autom. 15(2), 251–257 (1999)
Wei, Y.T., Meng, Q.H., Jing, Y.Q., et al.: A portable odor-tracing instrument. IEEE Trans. Instrum. Meas. 65(3), 631–642 (2015)
Luo, B., Meng, Q.H., Wang, J.Y., et al.: A flying odor compass to autonomously locate the gas source. IEEE Trans. Instrum. Meas. 67(1), 137–149 (2017)
Jing, T., Meng, Q.H., Ishida, H.: Recent progress and trend of robot odor source localization. IEEJ Trans. Electr. Electron. Eng. 16(7), 938–953 (2021)
Malik, M., Malik, M.K., Mehmood, K., Makhdoom, I.: Automatic speech recognition: a survey. Multimedia Tools Appl. 80(6), 9411–9457 (2020). https://doi.org/10.1007/s11042-020-10073-7
Le, T.H.M., Chen, H., Babar, M.A.: Deep learning for source code modeling and generation: Models, applications, and challenges. ACM Comput. Surv. (CSUR) 53(3), 1–38 (2020)
Datta, D., David, P.E., Mittal, D., et al.: Neural machine translation using recurrent neural network. Int. J. Eng. Adv. Technol. 9(4), 1395–1400 (2020)
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D 404, 132306 (2020)
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Yan, Z., Jing, T., Chen, SW., Jabeen, M., Meng, QH. (2023). A Novel Odor Source Localization Method via a Deep Neural Network-Based Odor Compass. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_16
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