Conference Paper

Bi-modal search using complementary sensing (olfaction/vision) for odour source localisation

Intelligent Robotics Res. Centre, Monash Univ., Clayton, Vic.
DOI: 10.1109/ROBOT.2006.1642005 Conference: Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT

Odour localisation in an enclosed area is difficult due to the formation of sectors of circulating airflow. Well-defined plumes do not exist, and reactive plume following may not be possible. Odour localisation has been partially achieved in this environment by using knowledge of airflow, and a search that relies on chemical sensing and reasoning. However the results are not specific, with the odour source only restricted to a broad area. This paper presents a solution to the problem by introducing a second search stage using visual sensing. It therefore comprises a bi-modal, two-stage search, with each stage exploiting complementary sensing modalities. This paper presents details of the method and experimental results

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Available from: Gideon Kowadlo, Dec 22, 2013
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    • "In recent years, a few researchers attempted to integrate vision and olfaction to localize the odor source. Kowadlo et al.[10]took crackles as the vision feature assisting olfaction to search for the odor source. Ishida et al.[11]proposed a color-based algorithm to deal with the vision information in the searching process. "
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    ABSTRACT: In order to take full advantage of the multisensor information, a MIMO fuzzy control system based on semitensor product (STP) is set up for mobile robot odor source localization (OSL). Multisensor information, such as vision, olfaction, laser, wind speed, and direction, is the input of the fuzzy control system and the relative searching strategies, such as random searching (RS), nearest distance-based vision searching (NDVS), and odor source declaration (OSD), are the outputs. Fuzzy control rules with algebraic equations are given according to the multisensor information via STP. Any output can be updated in the proposed fuzzy control system and has no influence on the other searching strategies. The proposed MIMO fuzzy control scheme based on STP can reach the theoretical system of the mobile robot OSL. Experimental results show the efficiency of the proposed method.
    Full-text · Article · Oct 2015 · Mathematical Problems in Engineering
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    • "Air duct flow direction: In the case of unknown boundary conditions. 2. Flows past obstacles: The scenario illustrated in Fig. 1, shows the flow patterns that arise with differing vertical positions of two types of obstacles (simulated with Flo++ and verified through practical experimentation [6]). Significantly different, stable, macroscopic flow patterns occur with even small changes in the vertical position of the obstruction. "
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    ABSTRACT: Previous work on robotic odour localisation in enclosed environments, relying on an airflow model, has faced significant limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. We present a method for dealing with these uncertainties through the generation of multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. Experimental results show that this method is capable of improving the robustness of odour localisation in the presence of uncertainties, where previously it was incapable. The results further demonstrate the usefulness of naïve physics for practical robotics applications.
    Full-text · Article · Jun 2009 · Robotics and Autonomous Systems
  • Source
    • "Air duct flow direction: In the case of unknown boundary conditions. 2. Flows past obstacles: The scenario illustrated in Fig. 1, shows the flow patterns that arise with differing vertical positions of two types of obstacles (simulated with Flo++ and verified through practical experimentation [6]). Significantly different, stable, macroscopic flow patterns occur with even small changes in the vertical position of the obstruction. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Previous work on odour localisation in enclosed environments, relying on an airflow model, has faced significant limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. We have presented a method for dealing with these uncertainties, by generating multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. We have shown experimentally that this method is capable of improving the robustness of our method for odour localisation in the presence of uncertainties, where previously it was incapable. The results further demonstrate the usefulness of naive physics for practical robotics applications.
    Full-text · Conference Paper · Jan 2006
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