Готові списки джерел за темами / Odour source localization

Добірка наукової літератури з теми "Odour source localization"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Odour source localization"

1

Ishida, H., K. Hayashi, M. Takakusaki, T. Nakamoto, T. Moriizumi, and R. Kanzaki. "Odour-source localization system mimicking behaviour of silkworm moth." Sensors and Actuators A: Physical 51, no. 2-3 (November 1995): 225–30. http://dx.doi.org/10.1016/0924-4247(95)01220-6.

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2

SEELINGER, GÜNTER, and SIGRID GAGEL. "On the function of sex pheromone components in Periplaneta americana: improved odour source localization with periplanone-A." Physiological Entomology 10, no. 2 (June 1985): 221–34. http://dx.doi.org/10.1111/j.1365-3032.1985.tb00038.x.

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3

Craven, Brent A., Eric G. Paterson, and Gary S. Settles. "The fluid dynamics of canine olfaction: unique nasal airflow patterns as an explanation of macrosmia." Journal of The Royal Society Interface 7, no. 47 (December 9, 2009): 933–43. http://dx.doi.org/10.1098/rsif.2009.0490.

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Анотація:
The canine nasal cavity contains hundreds of millions of sensory neurons, located in the olfactory epithelium that lines convoluted nasal turbinates recessed in the rear of the nose. Traditional explanations for canine olfactory acuity, which include large sensory organ size and receptor gene repertoire, overlook the fluid dynamics of odorant transport during sniffing. But odorant transport to the sensory part of the nose is the first critical step in olfaction. Here we report new experimental data on canine sniffing and demonstrate allometric scaling of sniff frequency, inspiratory airflow rate and tidal volume with body mass. Next, a computational fluid dynamics simulation of airflow in an anatomically accurate three-dimensional model of the canine nasal cavity, reconstructed from high-resolution magnetic resonance imaging scans, reveals that, during sniffing, spatially separate odour samples are acquired by each nostril that may be used for bilateral stimulus intensity comparison and odour source localization. Inside the nose, the computation shows that a unique nasal airflow pattern develops during sniffing, which is optimized for odorant transport to the olfactory part of the nose. These results contrast sharply with nasal airflow in the human. We propose that mammalian olfactory function and acuity may largely depend on odorant transport by nasal airflow patterns resulting from either the presence of a highly developed olfactory recess (in macrosmats such as the canine) or the lack of one (in microsmats including humans).
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4

Hayes, A. T., A. Martinoli, and R. M. Goodman. "Distributed odor source localization." IEEE Sensors Journal 2, no. 3 (June 2002): 260–71. http://dx.doi.org/10.1109/jsen.2002.800682.

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5

Zheng, Jun Bao, Lei Yang, Jing Bo Chen, and Ya Ming Wang. "Study on Odor Source Localization Method Based on Bionic Olfaction." Applied Mechanics and Materials 448-453 (October 2013): 391–95. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.391.

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Анотація:
This paper proposes an odor source localization method based on bionic olfaction. The special nasal cavity and turnable head make mammalian have excellent odor source localization ability. According to this principle, a turnable bionic odor sensing device is proposed by this paper for odor source localization system. This sensing device can rotate freely within the range level 360°, and the detection directions of its sensing channels are different. This also proposes a pattern recognition algorithm based on K-L transform to analyze the data collected by odor sensing device, and the features of odor source are extracted correctly. Experimental results for odor source localization demonstrate the feasibility of the proposed approach.
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6

Lalitta, Suwantaweekul, Geunho Lee, and Nak Young Chong. "2A1-J05 Odor Source Intensity-based Localization Algorithm for Decentralized Swarm Robots(Robotic Systems Based on Autonomous Decentralized Architecture)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2012 (2012): _2A1—J05_1—_2A1—J05_4. http://dx.doi.org/10.1299/jsmermd.2012._2a1-j05_1.

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7

Liu, Zhen Zhang, Yi Jun Wang, and Tien Fu Lu. "Odor Source Localization Using Multiple Robots in Complicated City-Like Environments." Advanced Materials Research 291-294 (July 2011): 3337–44. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.3337.

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Анотація:
The detection of a dangerous emission source location has the potential to be enhanced by using plume-tracing mobile robots, without endangering human life during the detection and source localization process. So far, many researchers focus on odor source localization in simple & laboratory based environments. The present study focuses on more real life odor source localization scenarios. In this study, multiple robots were used and coordinated by a supervisory program to locate an odor source in complicated city-like environments. A series of simulations has been conducted and the results demonstrated the potential of the supervisory program to effectively control a number of robots to locate a dangerous odor source in real life scenarios.
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Cui, Shi Gang, Guang Ming Zeng, Fan Liang, and Jiang Lei Dong. "Simulated Annealing Algorithm Based Single Robot Odor Source Localization Strategy." Applied Mechanics and Materials 494-495 (February 2014): 1286–89. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1286.

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Анотація:
This paper presents a search strategy for single mobile robots to realize the active olfaction (also called odor/gas source localization or plume tracing). The odor source localization is regarded as a kind of dynamic function optimization problem in this article, using the simulated annealing algorithm to calculate the optimal solution of density distribution function, namely the odor source location. The simulation experiments results in indoor ventilated environment show that the robot can track in plume and locate the odor source under the area of the 10m*10m, and it can effectively jump out of local maximum values in the process of search.
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Yang, Lei, Jun Bao Zheng, Jing Bo Chen, and Ya Ming Wang. "Review of Odor Source Localization Robot Based on Bionic Olfaction." Applied Mechanics and Materials 462-463 (November 2013): 750–54. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.750.

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Анотація:
This paper summarizes some current typical odor source localization strategies and algorithms. And their advantages and disadvantages are pointed out. Some typical olfactory robots and achievements are listed. It is pointed out that the current questions of odor source localization robot based on bionic olfaction are how to build an accurate gas diffusion model and combining multi-information technology.
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de Croon, G. C. H. E., L. M. O'Connor, C. Nicol, and D. Izzo. "Evolutionary robotics approach to odor source localization." Neurocomputing 121 (December 2013): 481–97. http://dx.doi.org/10.1016/j.neucom.2013.05.028.

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Дисертації з теми "Odour source localization"

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Liu, Zhenzhang. "Odour source localization using multiple plume-tracing mobile robots." Thesis, 2010. http://hdl.handle.net/2440/65209.

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The recent increasing threat of chemical weapons technologies has highlighted the need for superior detection of hazardous emission sources. One promising area of technological development is odour source detection using plume-tracing mobile robots, which have the potential to detect emissions released by sources such as fire, toxic gas and explosives, without endangering human life during the detection process. Aims of this proposed research project are to investigate the use of multiple mobile robots to locate a dangerous odour source in realistic environments. The realistic environments presented in this study are two city-like environments and a complicated indoor building environment. These two kinds of environments are considered to have a very close relationship with human life. The research methods for this project include two aspects which are simulation and experimental validation. A simulation framework for this study was constructed using combined CFD and MATLAB techniques. The control strategies for plume-tracing mobile robots were developed and tested in the simulation framework. A prototype plume-tracing mobile robot was physically developed for the purpose of real world experimental validation. A series of experiments proved that the control strategies developed in the simulation framework was applicable to a real plume-tracing robot; on the other hand, the experiments also proved that the developed simulation framework was capable of reflecting real world plume-tracing scenarios. The contributions to the plume-tracing research field achieved in this research project are: (a) a novel simulation framework using combined CFD and MATLAB techniques for plume tracing research was developed and it is believed that the framework is helpful for the researchers in plume-tracing research field; (b) a small size plume-tracing mobile robot was fabricated. This small robot has the capability of tracing odour plumes in complicated wind-varying environments. The capability of this kind of robot is considered to be a new input into this research field. (c) a supervisory approach was proposed in this study and the developed supervisory programs coordinated multiple robots to locate an odour source in realistic environments effectively.
Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2010
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Thomas, Joseph. "Odor Source Localization Using Swarm Robotics." Thesis, 2008. http://hdl.handle.net/2005/698.

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Locating an odor source in a turbulent environment, an instinctive behavior of insects such as moths, is a nontrivial task in robotics. Robots equipped with odor sensors find it difficult to locate the odor source due to the sporadic nature of odor patches in a turbulent environment. In this thesis, we develop a swarm algorithm which acquires information from odor patches and utilizes it to locate the odor source. The algorithm utilizes an intelligent integration of the chemotaxis, anemotaxis and spiralling approaches, where the chemotactic behavior is implemented by the recently proposed Glowworm Swarm Optimization (GSO) algorithm. Agents switch between chemotactic, anemotactic, and spiralling modes in accordance with the information available from the environment for optimal performance. The proposed algorithm takes full advantage of communication and collaboration between the robots. It is shown to be robust, efficient and well suited for implementation in olfactory robots. An important feature of the algorithm is the use of maximum concentration encountered in the recent past for navigation, which is seen to improve algorithmic performance significantly. The algorithm initially assumes agents to be point masses, later this is modified for robots and includes a gyroscopic avoidance strategy. A variant of the algorithm which does not demand wind information, is shown to be capable of locating odor sources even in no wind environment. A deterministic GSO algorithm has been proposed which is shown capable of faster convergence. Another proposed variant, the push pull GSO algorithm is shown to be more efficient in the presence of obstacle avoidance. The proposed algorithm is also seen capable of locating odor source under varying wind conditions. We have also shown the simultaneous capture of multiple odor sources by the proposed algorithm. A mobile odor source is shown to be captured and tracked by the proposed approach. The proposed approaches are later tested on data obtained from a realistic dye mixing experiment. A gas source localization experiment is also carried out in the lab to demonstrate the validity of the proposed approaches under real world conditions.
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Caetano, José Miguel Marques. "Development of an Odor Compass: 2D Thermal Anemometer with Olfactory Information." Master's thesis, 2017. http://hdl.handle.net/10316/83123.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
Muitos animais usam o olfato como um sentido de orientação combinando a informação sobre a concentração de um odor específico, presente no ar, e a velocidade do vento. Este tipo de deteção está ligado aos conceitos de deteção baseados em quimiotaxia e anemotaxia, uma vez que se consideram as propriedades químicas e eólicas aquando da localização da origem de um odor. No entanto, o olfato é muitas vezes desprezado, pelos seres humanos, como um sentido de orientação, não sendo, portanto, objeto de profunda investigação e/ou desenvolvimento. Assim, esta tese pretende dar uma contribuição nesta área através do desenvolvimento de um compasso de odores baseado em métodos térmicos, para medir as propriedades de um fluido, e um sensor de gás eletroquímico, de forma a medir a concentração do odor. Os compassos de odor são adequados para o uso em robôs móveis, possibilitando o rastreamento ativo de um odor, e em redes de sensores ambientais, permitindo um rastreamento estático de odor. Estes podem, portanto, ser usados em aplicações como o controlo de ambientes de vida e de trabalho num edifício, inspeção de ambientes perigosos utilizando robôs móveis, deteção de drogas e explosivos, e deteção precoce de incêndios florestais. Para desenvolver tal dispositivo, foram colocados vários termístores, também designados de elementos aquecidos, em torno de uma estrutura triangular, permitindo que a relação entre as suas respostas possa ser utilizada para determinar a direção da origem de um odor enquanto se usa um sensor de gás de óxidos metálicos para medir a concentração do mesmo.Testes em laboratório realizados com etanol, de forma a produzir a quantidade desejada de odor, e um processo de calibração completamente automatizado, mostraram que o sensor desenvolvido consegue indicar a direção da origem de um odor com precisão, medindo a velocidade do fluxo de ar com um erro absoluto médio de 0,0245 m/s e a direção do fluxo de ar com um erro absoluto médio de 3%.
Many animals use olfaction as a sense of orientation by combining the information regarding the concentration of a specific odor present in the air and the velocity of the wind. This type of detection is linked to the concepts of chemotaxis and anemotaxis based detection, since both chemical and wind properties are considered when locating the source of an odor. However, olfaction is often disregarded by humans as a sense of orientation, thus not being subject of deep research and/or development. Therefore, this thesis intends to give a contribution in this area through the development of an odor compass based on thermal methods for measuring the flow properties of a fluid and an electrochemical gas sensor for measuring its odor concentration. Odor compasses are suitable for usage in both mobile robots, in order to perform an active odor tracing, and environmental sensor networks, allowing for a static odor tracing. They can, therefore, be used in applications like control of living and working environments in a building, inspection of dangerous environments with mobile robots, drugs and explosives detection and earlier detection of active wildfires. To develop such a device, multiple thermistors, also referred to as heated elements, were placed around a triangular structure, so that the relation between their responses can be used to determine the direction of the odor source, while a metal oxide gas sensor was used to measure the odor concentration.Indoor experiments realized with ethanol, in order to produce the desired amount of odor, and a fully automated calibration process showed that the developed sensor could indicate the direction of the odor source with precision, measuring the airflow velocity with a mean absolute error of 0.0245 m/s and the airflow direction with a 3% mean absolute error.
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Частини книг з теми "Odour source localization"

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Gaurav, Kumar, Ramanpreet Singh, Ajay Kumar, and Ram Dayal. "Application of a Nature-Inspired Algorithm in Odor Source Localization." In Applied Mechatronics and Mechanics, 51–66. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9781003019060-4.

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Yan, Zheng, Tao Jing, Si-Wen Chen, Meh Jabeen, and Qing-Hao Meng. "A Novel Odor Source Localization Method via a Deep Neural Network-Based Odor Compass." In ROBOT2022: Fifth Iberian Robotics Conference, 189–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21062-4_16.

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Lochmatter, Thomas, and Alcherio Martinoli. "Understanding the Potential Impact of Multiple Robots in Odor Source Localization." In Distributed Autonomous Robotic Systems 8, 239–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00644-9_21.

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Li, Nanqi, Qiang Lu, Yang He, and Jian Wang. "Particle Swarm Optimization Based on Shannon’s Entropy for Odor Source Localization." In Communications in Computer and Information Science, 140–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45261-5_15.

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Jain, Upma, Ritu Tiwari, and W. Wilfred Godfrey. "Odor Source Localization by Concatenating Particle Swarm Optimization and Grey Wolf Optimizer." In Advances in Intelligent Systems and Computing, 145–53. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8237-5_14.

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6

Zou, Yuhua, and Dehan Luo. "A Modified Ant Colony Algorithm Used for Multi-robot Odor Source Localization." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 502–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_60.

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Hoang, Anh-Quy, and Minh-Trien Pham. "Swarm Intelligence-Based Approach for Macroscopic Scale Odor Source Localization Using Multi-robot System." In Advances in Information and Communication Technology, 593–602. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49073-1_63.

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Wang, Yang, Qing-Hao Meng, and Ming Zeng. "Vision-Aided Spiral-Surge Algorithm for Odor Source Localization in Indoor Natural Ventilated Environments." In Advances in Intelligent and Soft Computing, 751–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27329-2_103.

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Li, Fei, Qing-Hao Meng, Shuang Bai, Ji-Gong Li, and Dorin Popescu. "Probability-PSO Algorithm for Multi-robot Based Odor Source Localization in Ventilated Indoor Environments." In Intelligent Robotics and Applications, 1206–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88513-9_128.

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Jain, Upma, W. Wilfred Godfrey, and Ritu Tiwari. "A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization." In Robotic Systems, 1519–33. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch072.

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Анотація:
This paper concerns with the problem of odor source localization by a team of mobile robots. The authors propose two methods for odor source localization which are largely inspired from gravitational search algorithm and particle swarm optimization. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. As robots enter in the vicinity of plume area they form groups using K-nearest neighbor algorithm. The problem of local optima is handled through the use of search counter concept. The proposed approaches are tested and validated through simulation.
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Тези доповідей конференцій з теми "Odour source localization"

1

Marques, L., and A. T. De Almeida. "Electronic nose-based odour source localization." In 6th International Workshop on Advanced Motion Control. Proceedings. IEEE, 2000. http://dx.doi.org/10.1109/amc.2000.862824.

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Castro, Alexander, Nevo Magnezi, Biruk Sintayehu, Alexander Quinto, and Pamela Abshire. "Odor Source Localization on a Nano Quadcopter." In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2018. http://dx.doi.org/10.1109/biocas.2018.8584769.

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3

Zhenzhang Liu and Tien-Fu Lu. "Odor source localization in complicated indoor environments." In 2008 10th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2008. http://dx.doi.org/10.1109/icarcv.2008.4795548.

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Cabrita, Goncalo, Lino Marques, and Veysel Gazi. "Virtual cancelation plume for multiple odor source localization." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6697161.

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Rahbar, Faezeh, Ali Marjovi, and Alcherio Martinoli. "An Algorithm for Odor Source Localization based on Source Term Estimation." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793784.

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Li, Ji-Gong, Qing-Hao Meng, Fei Li, Ming Zeng, and Dorin Popescu. "Mobile robot based odor source localization via particle filter." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400388.

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Lu, Qiang, and Qing-Long Han. "A distributed coordination control scheme for odor source localization." In IECON 2010 - 36th Annual Conference of IEEE Industrial Electronics. IEEE, 2010. http://dx.doi.org/10.1109/iecon.2010.5675475.

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Zou, Yuhua, Dehan Luo, and Weihai Chen. "Swarm robotic odor source localization using ant colony algorithm." In 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410516.

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Miao, Yanzi, Xiaoping Ma, Xin Jin, and Huijuan Lu. "Mobile Robot Odor Source Localization Based on Modified FWA." In 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2018. http://dx.doi.org/10.1109/cyber.2018.8688288.

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Ping, Jiang, Wang Yu-zhen, and Xu Mei-rong. "Mobile robot odor source localization via semi-tensor product." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7260576.

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