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Journal articles on the topic 'Odour source localization'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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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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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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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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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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8

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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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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9

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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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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10

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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11

Li, Chun Shu, Zhi Hua Yang, Gen Qun Cui, and Bo Jin. "Odor Source Localization Research of Mobile Robots in Indoor Environments." Applied Mechanics and Materials 441 (December 2013): 796–800. http://dx.doi.org/10.4028/www.scientific.net/amm.441.796.

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Aiming at the odor source localization in an obstacle-filled wind-varying indoor environment, a new method based odor source localization algorithm for a single mobile robot is proposed. With the information of the wind and the concentration gradient, Wasps can find odor source in a short time. However, it is very difficult for mobile robots to mimic the behaviors of wasps exactly. So, besides the bionics, BP neural network is adopted for the mobile robot to find the odor source. The control strategies for the plume-tracing mobile robot are proposed which include the intelligent plume-tracing algorithm and the collision avoidance algorithm based on improved potential grid method. The algorithms were integrated to control the robot trace plumes in obstructed indoor environments. Experimental results have demonstrated the capability of this kind of plume-tracing mobile robot.

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12

Gao, Xiang, and Levent Acar. "Using a Mobile Robot with Interpolation and Extrapolation Method for Chemical Source Localization in Dynamic Advection-diffusion Environment." IAES International Journal of Robotics and Automation (IJRA) 5, no. 2 (June 1, 2016): 87. http://dx.doi.org/10.11591/ijra.v5i2.pp87-97.

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<span lang="EN-US">this paper address the problem of mapping likely particle path derived from a chemical source using interpolation and extrapolation method. Order localization is the problem of finding the source of an odor or other volatile chemical. Most localization method require the robot to follow the odor plume along its entire length, which is time consuming and may be especially difficult in a cluttered environment. In this paper, a map of sensors’ environment was used, together with the path line of airflow, to predict the pattern of air movement. The robot then used the airflow pattern to reason about the probable location of the odor source. This demonstrates that interpolation and extrapolation method can be used to assist odor localization search and indicates that similar techniques have great operating in an unstructured environment to reason about its surroundings. This paper present details of getting the model of particle path using interpolation and extrapolation method, model of particle path surrounding the obstacles and openings, result of practical odor source location simulation.</span>

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13

Jain, Upma, W. Wilfred Godfrey, and Ritu Tiwari. "A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization." International Journal of Robotics Applications and Technologies 5, no. 1 (January 2017): 20–33. http://dx.doi.org/10.4018/ijrat.2017010102.

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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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14

Voges, Nicole, Antoine Chaffiol, Philippe Lucas, and Dominique Martinez. "Reactive Searching and Infotaxis in Odor Source Localization." PLoS Computational Biology 10, no. 10 (October 16, 2014): e1003861. http://dx.doi.org/10.1371/journal.pcbi.1003861.

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15

Sinha, Abhinav, Ritesh Kumar, Rishemjit Kaur, and Amol P. Bhondekar. "Consensus-Based Odor Source Localization by Multiagent Systems." IEEE Transactions on Cybernetics 49, no. 12 (December 2019): 4450–59. http://dx.doi.org/10.1109/tcyb.2018.2869224.

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16

Zhang, Yu Li, and Xiao Ping Ma. "Localizing Multiple Odor Sources Using Virtual Physics Based Robots." Advanced Materials Research 756-759 (September 2013): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.223.

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This paper is concerned with the problem of multiple chemical sources localization using multi-robot system. A multi-robot cooperation strategy with virtual physics force, which includes structure formation force, goal force, repulsion force and rotary force, is proposed. First, in order to test the effectiveness of the proposed strategy, two sources plume model are constructed by computation fluid dynamics simulations. Second, parallel search by two groups robots is used to locate two sources in simulation environment. With the purpose of preventing two groups from locating the same source, we proposed a rotary force which made each subgroup can locate different chemical source. Simulation experiment discussed the influence of the varied wind direction/ speed frequency and methane release frequency and different initial positions of two groups to the search performance. Finally, the comparative result about them is illustrated.

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17

Jing, Tao, Qing‐Hao Meng, and Hiroshi Ishida. "Recent Progress and Trend of Robot Odor Source Localization." IEEJ Transactions on Electrical and Electronic Engineering 16, no. 7 (May 14, 2021): 938–53. http://dx.doi.org/10.1002/tee.23364.

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18

Esquivelzeta Rabell, José, Kadir Mutlu, João Noutel, Pamela Martin del Olmo, and Sebastian Haesler. "Spontaneous Rapid Odor Source Localization Behavior Requires Interhemispheric Communication." Current Biology 27, no. 10 (May 2017): 1542–48. http://dx.doi.org/10.1016/j.cub.2017.04.027.

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19

Qiang Lu, Qing-Long Han, Xiaogao Xie, and Shirong Liu. "A Finite-Time Motion Control Strategy for Odor Source Localization." IEEE Transactions on Industrial Electronics 61, no. 10 (October 2014): 5419–30. http://dx.doi.org/10.1109/tie.2014.2301751.

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20

Lu, Qiang, and Ping Luo. "A learning particle swarm optimization algorithm for odor source localization." International Journal of Automation and Computing 8, no. 3 (August 2011): 371–80. http://dx.doi.org/10.1007/s11633-011-0594-0.

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21

Lu, Guangda, Qiuyue Zhang, Tongtong Qie, and Qihui Feng. "A Robot Odor Source Localization Strategy Based on Bionic Behavior." IOP Conference Series: Materials Science and Engineering 470 (January 27, 2019): 012033. http://dx.doi.org/10.1088/1757-899x/470/1/012033.

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22

Saxena, Nitesh, Dinesh Natesan, and Sanjay P. Sane. "Odor source localization in complex visual environments by fruit flies." Journal of Experimental Biology 221, no. 2 (November 16, 2017): jeb172023. http://dx.doi.org/10.1242/jeb.172023.

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23

SHIGAKI, Shunsuke, and Daisuke KURABAYASHI. "Insect-Machine interface for odor source localization by micro UAV." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2P1—E07. http://dx.doi.org/10.1299/jsmermd.2018.2p1-e07.

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24

Katsumata, Souichirou, Noriyasu Ando, and Ryohei Kanzaki. "An Insect-sized Atmospheric Ion Source Localization Robot for the Evaluation of Odor Source Localization Algorithms of Insects." Journal of the Robotics Society of Japan 27, no. 7 (2011): 711–17. http://dx.doi.org/10.7210/jrsj.27.711.

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25

Yu, Yan S. W., Matthew M. Graff, Chris S. Bresee, Yan B. Man, and Mitra J. Z. Hartmann. "Whiskers aid anemotaxis in rats." Science Advances 2, no. 8 (August 2016): e1600716. http://dx.doi.org/10.1126/sciadv.1600716.

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Observation of terrestrial mammals suggests that they can follow the wind (anemotaxis), but the sensory cues underlying this ability have not been studied. We identify a significant contribution to anemotaxis mediated by whiskers (vibrissae), a modality previously studied only in the context of direct tactile contact. Five rats trained on a five-alternative forced-choice airflow localization task exhibited significant performance decrements after vibrissal removal. In contrast, vibrissal removal did not disrupt the performance of control animals trained to localize a light source. The performance decrement of individual rats was related to their airspeed threshold for successful localization: animals that found the task more challenging relied more on the vibrissae for localization cues. Following vibrissal removal, the rats deviated more from the straight-line path to the air source, choosing sources farther from the correct location. Our results indicate that rats can perform anemotaxis and that whiskers greatly facilitate this ability. Because air currents carry information about both odor content and location, these findings are discussed in terms of the adaptive significance of the interaction between sniffing and whisking in rodents.

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26

Gaurav, Kumar, Ajay Kumar, and Ram Dayal. "Veco-Taxis as a Novel Engineered Algorithm for Odor Source Localization." International Journal of Ambient Computing and Intelligence 11, no. 2 (April 2020): 1–29. http://dx.doi.org/10.4018/ijaci.2020040101.

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Algorithms with limited intelligence are unable to localize an odor source in an indoor environment with weak or no airflow. Stage wise solutions to odor source localization has been provided with a novel engineered algorithm called veco-taxis for plume traversal. It finds turn angles by calculating concentration gradients using vector algebra-based search algorithms. Levy walk is used in the plume finding phase. The concept of last chemical detection points (LCDPs) has been adopted for source declaration. The success rate of implemented algorithms is quantified using minimum and maximum move lengths—a key parameter—during source localization. A unified success and performance index (SPI) of the search algorithm is presented for the first time. SPI uncovers implicit parameters accountable for success in locating source and considers a qualitative performance. Higher SPIs are observed when the move length in plume finding is minimum and kept smaller than the plume traversal move length by some factor. It has been also demonstrated through simulations that veco-taxis is superior to the E. coli algorithm.

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27

He, Ning, and Jian Hua Yang. "The Localization Method of Active Olfaction Robot and Experimental Study." Advanced Materials Research 516-517 (May 2012): 1827–30. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1827.

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An active olfaction implementation scheme based on zigzag search algorithm using a wheeled mobile robot and the experimental verification are put forward. Firstly, the hardware structure of the active olfaction robot is setup. Secondly, the Zigzag-based robot active olfaction dynamic system implementation procedure in the simulated environment is presented, including plume finding, plume tracking and odor source declaration, and simulation results validate the proposed search scheme. The entire system was used under steady wind condition to locate the ethanol odor source. Choice 15m as the start distance, and after 10 times locating, the error is less than 5% and the experimental results prove that the system recognition is accuracy.

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28

Jiang, Ping, Yuzhen Wang, and Aidong Ge. "Multivariable Fuzzy Control Based Mobile Robot Odor Source Localization via Semitensor Product." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/736720.

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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.

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29

Lu, Qiang, Qing-Long Han, and Shirong Liu. "A finite-time particle swarm optimization algorithm for odor source localization." Information Sciences 277 (September 2014): 111–40. http://dx.doi.org/10.1016/j.ins.2014.02.010.

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30

Sinha, Abhinav, Ritesh Kumar, Rishemjit Kaur, and Rajiv Kumar Mishra. "Consensus-Based Odor Source Localization by Multiagent Systems Under Resource Constraints." IEEE Transactions on Cybernetics 50, no. 7 (July 2020): 3254–63. http://dx.doi.org/10.1109/tcyb.2019.2924328.

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31

Luong, Duc-Nhat, and Daisuke Kurabayashi. "Odor Source Localization in Obstacle Regions Using Switching Planning Algorithms with a Switching Framework." Sensors 23, no. 3 (January 19, 2023): 1140. http://dx.doi.org/10.3390/s23031140.

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Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot’s balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%.

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32

Rahbar, Faezeh, Ali Marjovi, and Alcherio Martinoli. "Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization." Sensors 19, no. 3 (February 5, 2019): 656. http://dx.doi.org/10.3390/s19030656.

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Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.

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33

Men, Mao Chen, and Li Wei Chen. "An Approach for Active Odor Source Localization Based on Particle Swarm Optimization." Applied Mechanics and Materials 738-739 (March 2015): 493–98. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.493.

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This paper discusses the multi-robot cooperation positioning based on particle swarm optimization (PSO) search strategies. A group of active olfaction robots can find the position of odor source depend on the new “active olfaction” arithmetic. The active olfaction robot is regarded as a particle which can exchange message with each other.The simulation experiment wind field is built based on turbulent fluid model. A series of simulation experiments were performed to test the new localization arithmetic, and the experimental results were analysed.

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34

Fang, Jiandong. "The Fuzzy Logic Algorithm of Space Odor Source Localization to Mobile Robot." Journal of Information and Computational Science 12, no. 8 (May 20, 2015): 3173–83. http://dx.doi.org/10.12733/jics20105335.

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35

Chen, Xin-xing, and Jian Huang. "Odor source localization algorithms on mobile robots: A review and future outlook." Robotics and Autonomous Systems 112 (February 2019): 123–36. http://dx.doi.org/10.1016/j.robot.2018.11.014.

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36

Wang, Lingxiao, and Shuo Pang. "Robotic odor source localization via adaptive bio-inspired navigation using fuzzy inference methods." Robotics and Autonomous Systems 147 (January 2022): 103914. http://dx.doi.org/10.1016/j.robot.2021.103914.

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37

Lu, Qiang, and Qing-Long Han. "A Probability Particle Swarm Optimizer with Information-Sharing Mechanism for Odor Source Localization." IFAC Proceedings Volumes 44, no. 1 (January 2011): 9440–45. http://dx.doi.org/10.3182/20110828-6-it-1002.00507.

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38

Kurotsuchi, Kenzo, Mitsuharu Tai, and Hiromasa Takahashi. "Autonomous Micro-Air-Vehicle Control Based on Visual Sensing for Odor Source Localization." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (July 2017): 1152–58. http://dx.doi.org/10.25046/aj0203145.

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39

Zhu, Hongbiao, Yibo Wang, Chengjin Du, Quan Zhang, and Weidong Wang. "A novel odor source localization system based on particle filtering and information entropy." Robotics and Autonomous Systems 132 (October 2020): 103619. http://dx.doi.org/10.1016/j.robot.2020.103619.

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40

Ishida, Hiroshi, Yukihiko Kagawa, Takamichi Nakamoto, and Toyosaka Moriizumi. "Odor-source localization in the clean room by an autonomous mobile sensing system." Sensors and Actuators B: Chemical 33, no. 1-3 (July 1996): 115–21. http://dx.doi.org/10.1016/0925-4005(96)01907-7.

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41

Chen, Ziqi, and Jian Wang. "Underground Odor Source Localization Based on a Variation of Lower Organism Search Behavior." IEEE Sensors Journal 17, no. 18 (September 15, 2017): 5963–70. http://dx.doi.org/10.1109/jsen.2017.2729558.

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42

Binyameen, Muhammad, Júlia Jankuvová, Miroslav Blaženec, Rastislav Jakuš, Liwen Song, Fredrik Schlyter, and Martin N. Andersson. "Co-localization of insect olfactory sensory cells improves the discrimination of closely separated odour sources." Functional Ecology 28, no. 5 (March 3, 2014): 1216–23. http://dx.doi.org/10.1111/1365-2435.12252.

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43

Terutsuki, Daigo, Tomoya Uchida, Chihiro Fukui, Yuji Sukekawa, Yuki Okamoto, and Ryohei Kanzaki. "Real-time odor concentration and direction recognition for efficient odor source localization using a small bio-hybrid drone." Sensors and Actuators B: Chemical 339 (July 2021): 129770. http://dx.doi.org/10.1016/j.snb.2021.129770.

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44

WANG, Yang, Qinghao MENG, Teng LI, and Ming ZENG. "Single-Robot Odor Source Localization in a Ventilated Indoor Environment Using Simulated Annealing Algorithm." Robot 35, no. 3 (2013): 283. http://dx.doi.org/10.3724/sp.j.1218.2013.00283.

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45

ANDO, Noriyasu, Yuki KATAOKA, and Ryohei KANZAKI. "2A1-X08 Odor-source localization by insect-machine hybrid system with bio-photocoupler(Biorobotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _2A1—X08_1—_2A1—X08_2. http://dx.doi.org/10.1299/jsmermd.2014._2a1-x08_1.

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46

Matsuo, Hirotaka, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, and Kenshi Hayashi. "Optical Odor Imaging by Fluorescence Probes." Journal of Robotics and Mechatronics 24, no. 1 (February 20, 2012): 47–54. http://dx.doi.org/10.20965/jrm.2012.p0047.

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Odor gas detection is important for the detection of explosives, environmental sensing, biometrics, foodstuffs and a comfortable life. Such odor-source localizations is an active research area for robotics. In this study, we tried to detect odor chemicals with an optical method that can be applied for the spatiotemporal detection of odor. We used four types of fluorescence dyes; tryptophan, quinine sulfate, acridine orange, and 1-anilinonaphthalene-8-sulfonate (ANS). As analyses, we measured the following four odor chemicals, 2-furaldehyde, vanillin, acetophenone, and benzaldehyde. The fluorescence-quenching mechanism of PET (Photoinduced Electron Transfer) or FRET (Fluorescence Resonance Electron Transfer), which occur between fluorescence dyes and odor compounds, could prevent unintended detection of various odorants that is caused by their unspecific adsorption onto the detecting materials. The fluorescence changes were then observed. Thus, we could detect the odor substances through fluorescent quenching by using the fluorescence dyes. Odor information could be obtained by response patterns across all the fluorescence dyes. Moreover, we captured odor images with a cooled CCD camera. Shapes of the targets that emitted odor could be roughly recognized by the odor-shape images. From the spatiotemporal images of odors, twodimensional odor expanse could be obtained.

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Jatmiko, W., F. Jovan, R. Y. S. Dhiemas, M. S. Alvissalim, A. Febrian, D. Widiyanto, D. M. J. Purnomo, H. A. Wisesa, T. Fukuda, and K. Sekiyama. "PSO ALGORITHM FOR SINGLE AND MULTIPLE ODOR SOURCES LOCALIZATION PROBLEMS: PROGRESS AND CHALLENGE." International Journal on Smart Sensing and Intelligent Systems 9, no. 3 (2016): 1431–78. http://dx.doi.org/10.21307/ijssis-2017-925.

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Chen, Xinxing, Chenglong Fu, and Jian Huang. "A Deep Q-Network for robotic odor/gas source localization: Modeling, measurement and comparative study." Measurement 183 (October 2021): 109725. http://dx.doi.org/10.1016/j.measurement.2021.109725.

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Jain, Upma, Ritu Tiwari, and W. Wilfred Godfrey. "Multiple odor source localization using diverse-PSO and group-based strategies in an unknown environment." Journal of Computational Science 34 (May 2019): 33–47. http://dx.doi.org/10.1016/j.jocs.2019.04.008.

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Li, Ji-Gong, Qing-Hao Meng, Yang Wang, and Ming Zeng. "Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm." Autonomous Robots 30, no. 3 (January 21, 2011): 281–92. http://dx.doi.org/10.1007/s10514-011-9219-2.

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