Relevant bibliographies by topics / Plume-tracing

Academic literature on the topic 'Plume-tracing'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Plume-tracing"

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Eu, Kok Seng, and Kian Meng Yap. "Chemical plume tracing." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141875587. http://dx.doi.org/10.1177/1729881418755877.

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Most of the reported three-dimensional chemical plume tracing methods use stereo sensing method to determine the next tracing step direction. For example, multiple sensors are used for detection in the left, right, up and down directions. Left and right detections are feasible for stereo sniffing; unfortunately, the same approach is infeasible for the up/down sensing of the quadrotor platforms because the propellers of the quadrotor continuously draw the air from the top and bring it down, which affects the sensing of the upper and lower sensors, and fails to determine the subsequent tracing step of up/down direction. Therefore, up/down sensing in the surging stage of chemical plume tracing is ineffective for quadrotor platforms (chemical plume tracing has two stages: surging and casting). To solve the problem, we propose an alternative that is not in the surging but in the casting stage of chemical plume tracing, by designing a new three-dimensional chemical plume tracing technique with variations of altitude ( z-axis) control during the casting stage, which has never been considered in the previous works. Besides, we use a computational fluid dynamics software to study the airflow pattern of quadrotor platform. Subsequently, a fuzzy-based stereo-sniffing algorithm is developed by considering the quadrotor propeller’s air intake stream angle associated with the environmental wind direction angle, so as to improve the accuracy of stereo sensing. The results of the proposed solutions are verified and validated via both experimental and simulation approaches.
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Rowan, L. "GEOCHEMISTRY: Tracing a Plume." Science 291, no. 5501 (January 5, 2001): 15d—15. http://dx.doi.org/10.1126/science.291.5501.15d.

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Ishida, Hiroshi, T. Nakamoto, and Toyosaka Moriizumi. "Gas/Odor Plume Tracing Robot." Sensors Update 6, no. 1 (November 1999): 397–418. http://dx.doi.org/10.1002/1616-8984(199911)6:1<397::aid-seup397>3.0.co;2-3.

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Gaurav, Kumar, Ramanpreet Singh, and Ajay Kumar. "Modified Simple Chemical Plume Tracing Algorithm." Journal of Physics: Conference Series 1455 (February 2020): 012006. http://dx.doi.org/10.1088/1742-6596/1455/1/012006.

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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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Cao, Meng-Li, Qing-Hao Meng, Jia-Ying Wang, Bing Luo, Ya-Qi Jing, and Shu-Gen Ma. "Learning to Rapidly Re-Contact the Lost Plume in Chemical Plume Tracing." Sensors 15, no. 4 (March 27, 2015): 7512–36. http://dx.doi.org/10.3390/s150407512.

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Zhang, Yu Li, and Xiao Ping Ma. "Comparing Plume-Tracing Algorithms in Obstructed Multi-Source Environments." Advanced Materials Research 756-759 (September 2013): 228–32. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.228.

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In this paper, we compare the common plume-tracing algorithms: chemotaxis and anemotaxis in obstructed multi-source environment using multi-robot system. A multi-robot cooperation strategy with virtual physics force, which includes structure formation force, goal force, obstacle avoidance force, repulsion force and rotary force, is proposed. First, plume model with two sources in three obstacles environment is constructed by computation fluid dynamics simulations. Second, parallel searches by two groups robots with chemotaxis and anemotaxis are used to locate two sources in obstructed environment. Simulation comparison experiment with two plume-tracing algorithms 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 is illustrated.
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Farrell, J. A., S. Pang, and W. Li. "Chemical Plume Tracing via an Autonomous Underwater Vehicle." IEEE Journal of Oceanic Engineering 30, no. 2 (April 2005): 428–42. http://dx.doi.org/10.1109/joe.2004.838066.

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Hu, Hangkai, Shiji Song, and C. L. Phillip Chen. "Plume Tracing via Model-Free Reinforcement Learning Method." IEEE Transactions on Neural Networks and Learning Systems 30, no. 8 (August 2019): 2515–27. http://dx.doi.org/10.1109/tnnls.2018.2885374.

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Hai-Feng, Jiu, Chen Yu, Deng Wei, and Pang Shuo. "Underwater chemical plume tracing based on partially observable Markov decision process." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983187. http://dx.doi.org/10.1177/1729881419831874.

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Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based on partially observable Markov decision process algorithm and artificial potential field algorithm. The partially observable Markov decision process algorithm is used to construct a source likelihood map and update it in real time with environmental information from the sensors on autonomous underwater vehicle in search area. The artificial potential field algorithm uses the source likelihood map for accurately planning tracing path and guiding the autonomous underwater vehicle to track along the path until the source is detected. This article carries out simulation experiments on the proposed algorithm. The experimental results show that the algorithms have good performance, which is suitable for chemical plume tracing via autonomous underwater vehicle. Compared with the bionic method, the simulation results show that the proposed method has higher success rate and better stability than the bionic method.
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Dissertations / Theses on the topic "Plume-tracing"

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Crescenzi, Lanna Eleonora. "The contribution of the Finite Volume Point Dilution Method to the determination of solute mass flux in an alluvial aquifer." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9627/.

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Groundwater represents one of the most important resources of the world and it is essential to prevent its pollution and to consider remediation intervention in case of contamination. According to the scientific community the characterization and the management of the contaminated sites have to be performed in terms of contaminant fluxes and considering their spatial and temporal evolution. One of the most suitable approach to determine the spatial distribution of pollutant and to quantify contaminant fluxes in groundwater is using control panels. The determination of contaminant mass flux, requires measurement of contaminant concentration in the moving phase (water) and velocity/flux of the groundwater. In this Master Thesis a new solute flux mass measurement approach, based on an integrated control panel type methodology combined with the Finite Volume Point Dilution Method (FVPDM), for the monitoring of transient groundwater fluxes, is proposed. Moreover a new adsorption passive sampler, which allow to capture the variation of solute concentration with time, is designed. The present work contributes to the development of this approach on three key points. First, the ability of the FVPDM to monitor transient groundwater fluxes was verified during a step drawdown test at the experimental site of Hermalle Sous Argentau (Belgium). The results showed that this method can be used, with optimal results, to follow transient groundwater fluxes. Moreover, it resulted that performing FVPDM, in several piezometers, during a pumping test allows to determine the different flow rates and flow regimes that can occurs in the various parts of an aquifer. The second field test aiming to determine the representativity of a control panel for measuring mass flus in groundwater underlined that wrong evaluations of Darcy fluxes and discharge surfaces can determine an incorrect estimation of mass fluxes and that this technique has to be used with precaution. Thus, a detailed geological and hydrogeological characterization must be conducted, before applying this technique. Finally, the third outcome of this work concerned laboratory experiments. The test conducted on several type of adsorption material (Oasis HLB cartridge, TDS-ORGANOSORB 10 and TDS-ORGANOSORB 10-AA), in order to determine the optimum medium to dimension the passive sampler, highlighted the necessity to find a material with a reversible adsorption tendency to completely satisfy the request of the new passive sampling technique.
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Taylor, Brian Kyle. "TRACKING FLUID-BORNE ODORS IN DIVERSE AND DYNAMIC ENVIRONMENTS USING MULTIPLE SENSORY MECHANISMS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1341601566.

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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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Book chapters on the topic "Plume-tracing"

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Zarzhitsky, Dimitri, Diana Spears, David Thayer, and William Spears. "Agent-Based Chemical Plume Tracing Using Fluid Dynamics." In Formal Approaches to Agent-Based Systems, 146–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30960-4_10.

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Soares, Jorge M., A. Pedro Aguiar, António M. Pascoal, and Alcherio Martinoli. "A Graph-Based Formation Algorithm for Odor Plume Tracing." In Springer Tracts in Advanced Robotics, 255–69. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55879-8_18.

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Kishi, Kotaro, Daisuke Kurabayashi, Ryo Minegishi, Takeshi Sakurai, Ryohei Kanzaki, Masashi Tabuchi, and Hideki Sezutsu. "Experiment of Stereo Sensors for Chemical Plume Tracing by Optogenetic Silkworm Moth." In Intelligent Autonomous Systems 13, 1481–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_106.

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Wu, Yu-Xiu, Qing-Hao Meng, Yong Zhang, and Ming Zeng. "A Novel Chemical Plume Tracing Method Using a Mobile Sensor Network without Anemometers." In Advances in Intelligent and Soft Computing, 155–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27329-2_22.

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Chew, Jouh Yeong, Kotaro Kishi, Yohei Kinowaki, and Daisuke Kurabayashi. "Estimation of Stimuli Timing to Evaluate Chemical Plume Tracing Behavior of the Silk Moth." In Robot Intelligence Technology and Applications 2, 619–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05582-4_53.

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Kurabayashi, Daisuke, Yosuke Takahashi, Ryo Minegishi, Elisa Tosello, Enrico Pagello, and Ryohei Kanzaki. "Property Investigation of Chemical Plume Tracing Algorithm in an Insect Using Bio-machine Hybrid System." In Biomimetic and Biohybrid Systems, 131–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39802-5_12.

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Li, Wei, and Yu Tian. "Chemical Plume Tracing and Mapping via Swarm Robots." In Unmanned Aerial Vehicles, 306–45. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8365-3.ch014.

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This chapter addresses the key issues of chemical plume mapping and tracing via swarm robots. First, the authors present the models of turbulent odor plumes with both non-buoyant and buoyant features, which can efficiently evaluate strategies for tracing plumes, identifying their sources in two or three-dimensions. Second, the authors use the Monte Carlo technique to optimize moth-inspired plume tracing via swarm robots under formation control, which includes a leader to perform plume tracing maneuvers and non-leaders to follow the leader during plume tracing missions. Third, the authors introduce a variety of robot-based plume tracers, including ground-based robots, autonomous underwater vehicles, or unmanned aerial vehicles. Finally, the authors prospect the further research in this area, e.g., applying swarm robots to detect oil or gas leak, or to investigate subsea chemical pollution and greenhouse gases.
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Conference papers on the topic "Plume-tracing"

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Zarzhitsky, D., D. F. Spears, and W. M. Spears. "Distributed robotics approach to chemical plume tracing." In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005. http://dx.doi.org/10.1109/iros.2005.1545428.

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Okajima, Kei, Shunsuke Shigaki, Kazushi Sanada, and Daisuke Kurabayashi. "Chemical plume tracing model learning from insect behavior." In 2021 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf49454.2021.9382638.

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Sampathkumar, Aswanth, Dmitrii Dugaev, Aijun Song, Fei Hu, Zheng Peng, and Fumin Zhang. "Plume tracing simulations using multiple autonomous underwater vehicles." In WUWNet'22: The 16th International Conference on Underwater Networks & Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3567600.3568149.

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Farrell, J. A., S. Pang, W. Li, and R. Arrieta. "Chemical plume tracing experimental results with a REMUS AUV." In Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492). IEEE, 2003. http://dx.doi.org/10.1109/oceans.2003.178458.

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Liu, Zhenzhang, and Tien-Fu Lu. "Multiple robots plume-tracing in open space obstructed environments." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420742.

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Robinett, Rush D., and David G. Wilson. "Collective Plume Tracing: A Minimal Information Approach to Collective Control." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282567.

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Pang, Shuo. "Development of a Guidance System for AUV Chemical Plume Tracing." In OCEANS 2006. IEEE, 2006. http://dx.doi.org/10.1109/oceans.2006.306871.

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Wang, Lingxiao, Shuo Pang, and Guangyu Xu. "3-Dimensional Hydrothermal Vent Localization Based on Chemical Plume Tracing." In Global Oceans 2020: Singapore - U.S. Gulf Coast. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf38699.2020.9389264.

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Niu, Lvyin, Shiji Song, and Keyou You. "A plume-tracing strategy via continuous state-action reinforcement learning." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8242868.

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Chew, Jouh Yeong, Shigaki Shunsuke, and Daisuke Kurabayashi. "Time-variant chemical plume tracing inspired by the silk moth." In 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2014. http://dx.doi.org/10.1109/ssrr.2014.7017684.

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Reports on the topic "Plume-tracing"

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Atema, Jelle. Odor Plume Tracing: Lobster Inspired Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada415619.

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Carde, Ring T., and Jay A. Farrell. Chemical Plume Tracing: Insects as Model Navigators. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada399790.

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Byrne, Raymond Harry, Elizabeth L. Savage, John Edward Hurtado, and Steven E. Eskridge. Algorithms and analysis for underwater vehicle plume tracing. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/918240.

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Hurtado, John E. Distributed Sensing & Cooperative Control for Plume Tracing. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada410645.

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Koehl, M. A. Animal Plume-Tracing Behavior in Wave Influenced Flow Domains. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada405371.

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Koseff, Jeffrey R., and Stephen G. Monismith. Characterization and Modeling of Plumes and Animal Plume-Tracing in Wave-Influenced Coastal Environments. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada399263.

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