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Relevant bibliographies by topics / Odour source localisation

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Academic literature on the topic 'Odour source localisation'

Author: Grafiati

Published: 19 February 2023

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Journal articles on the topic "Odour source localisation"

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Eu, Kok Seng, Kian Meng Yap, and Tiam Hee Tee. "Supporting Odour Source Localisation of Multi-Sniffer Robots with up-Wind Formation Repeater Network Topology." Journal of Advances in Computer Networks 3, no. 1 (2015): 24–27. http://dx.doi.org/10.7763/jacn.2015.v3.136.

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Liu, Zhenzhang, and Tien Fu Lu. "Odour source localisation in a wind-varying indoor environment." International Journal of Mechatronics and Manufacturing Systems 2, no. 1/2 (2009): 168. http://dx.doi.org/10.1504/ijmms.2009.024354.

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Naeem, W., R. Sutton, and J. Chudley. "Chemical Plume Tracing and Odour Source Localisation by Autonomous Vehicles." Journal of Navigation 60, no. 2 (April 20, 2007): 173–90. http://dx.doi.org/10.1017/s0373463307004183.

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Autonomous vehicles with an ability to trace chemical plumes can be instrumental in tasks such as detection of unexploded ordnance, search for undersea wreckage and environmental monitoring. As a consequence, use of autonomous vehicles to perform chemical plume tracing has received an increasing interest from the research community in recent years. Owing to the diversity of applications and ambient fluid environment of the plumes, there are numerous plume tracing strategies and approaches. This paper reviews two main approaches and a number of strategies that have been successfully implemented to track air or water borne plumes in order to locate odour sources using autonomous vehicles. The first strategy considered is the biomimetic approach that offers excellent models for the development of robotic systems. Strategies inspired by lobsters and bacterium are the main focus in this study. The second scheme considers parallelization of the search procedure by employing a multi-robot approach. This approach has the advantage of utilising a group of smaller and simpler communicating robots which are capable of performing a collaborative search of the plume.

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Lytridis, C., E. E. Kadar, and G. S. Virk. "A systematic approach to the problem of odour source localisation." Autonomous Robots 20, no. 3 (June 2006): 261–76. http://dx.doi.org/10.1007/s10514-006-7414-3.

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Lu, Tien-Fu. "Indoor odour source localisation using robot: Initial location and surge distance matter?" Robotics and Autonomous Systems 61, no. 6 (June 2013): 637–47. http://dx.doi.org/10.1016/j.robot.2013.02.002.

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Macedo, João, Lino Marques, and Ernesto Costa. "A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective." Sensors 19, no. 10 (May 14, 2019): 2231. http://dx.doi.org/10.3390/s19102231.

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Locating odour sources with robots is an interesting problem with many important real-world applications. In the past years, the robotics community has adapted several bio-inspired strategies to search for odour sources in a variety of environments. This work studies and compares some of the most common strategies from a behavioural perspective with the aim of knowing: (1) how different are the behaviours exhibited by the strategies for the same perceptual state; and (2) which are the most consensual actions for each perceptual state in each environment. The first step of this analysis consists of clustering the perceptual states, and building histograms of the actions taken for each cluster. In case of (1), a histogram is made for each strategy separately, whereas for (2), a single histogram containing the actions of all strategies is produced for each cluster of states. Finally, statistical hypotheses tests are used to find the statistically significant differences between the behaviours of the strategies in each state. The data used for performing this study was gathered from a purpose-built simulator which accurately simulates the real-world phenomena of odour dispersion and air flow, whilst being sufficiently fast to be employed in learning and evolutionary robotics experiments. This paper also proposes an xml-inspired structure for the generated datasets that are used to store the perceptual information of the robots over the course of the simulations. These datasets may be used in learning experiments to estimate the quality of a candidate solution or for measuring its novelty.

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Dissertations / Theses on the topic "Odour source localisation"

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Lytridis, Christodoulos. "Cooperative robotic search strategies for odour source localisation." Thesis, University of Portsmouth, 2005. https://researchportal.port.ac.uk/portal/en/theses/cooperative-robotic-search-strategies-for-odour-source-localisation(9cafb45a-5881-42cc-956c-514ead3298d8).html.

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Sehdev, Aarti [Verfasser]. "Odour-background segregation and source localisation using fast olfactory processing / Aarti Sehdev." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1202012965/34.

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(9814871), Qiang Lu. "Decision and finite-time cooperative control of multi-robot systems for odour source localisation." Thesis, 2013. https://figshare.com/articles/thesis/Decision_and_finite-time_cooperative_control_of_multi-robot_systems_for_odour_source_localisation/13438202.

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In the last two decades, the problem of odour source localisation has been widely studied by using a single robot due to its practical significance for human security, such as searching for the source of toxic gas leakage and locating the origin of a fire at its initial stage. Recently, using a multi-robot system to locate the odour source has received increasing interest from researchers because of several major benefits over a single robot such as a wider detection range and multiple detection information. In this dissertation, two aspects on the odour source localisation problem are studied; one is to learn from data detected and collected by the multi-robot system to make a decision on the position of the odour source while the other is to coordinate and control the multi-robot system to locate the source of odour in terms of the decision results. In particular, a distributed coordination control architecture including two levels: a decision level and a control level, is designed. In the decision level, a new distributed decision algorithm, which can make a decision on the position of the odour source, is formulated. In the control level, a particle swarm optimization based finite-time motion control algorithm, a consensus-based finite-time motion control algorithm, and a potential-based finite-time motion control algorithm, are then developed to control the robot group to locate the odour source, respectively. Finally, the effectiveness of the proposed solutions consisting of the architecture, the decision algorithm, and the control algorithms for odour source localisation is illustrated through simulations.

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Books on the topic "Odour source localisation"

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Lytridis, Christodoulos. Cooperative robotic search strategies for odour source localisation. Portsmouth: University of Portsmouth, 2004.

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Book chapters on the topic "Odour source localisation"

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Macedo, João, Lino Marques, and Ernesto Costa. "Evolving Swarm Formations for Odour Source Localisation." In ROBOT2022: Fifth Iberian Robotics Conference, 142–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21062-4_12.

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Magalhães, Hugo, Rui Baptista, and Lino Marques. "Evaluating Cognitive Odour Source Localisation Strategies in Natural Water Streams." In ROBOT2022: Fifth Iberian Robotics Conference, 154–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21062-4_13.

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Lytridis, Chris, Gurvinder S. Virk, and Endre E. Kadar. "Search Performance of a Multi-robot Team in Odour Source Localisation." In Climbing and Walking Robots, 809–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-26415-9_97.

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Conference papers on the topic "Odour source localisation"

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Macedo, João, Lino Marques, and Ernesto Costa. "Designing fitness functions for odour source localisation." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459524.

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KADAR, ENDRE E., PAUL C. FISHER, and GURVINDER S. VIRK. "OVERCOMING TIME CONSTRAINTS ON ODOUR SOURCE LOCALISATION." In Proceedings of the 13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814329927_0035.

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KADAR, E. E., G. S. VIRK, and C. LYTRIDIS. "IN SEARCH OF PRINCIPLES OF ODOUR SOURCE LOCALISATION." In Proceedings of 10th International Conference (CLAWAR 2007). WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812770189_0072.

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Macedo, Joao, Lino Marques, and Ernesto Costa. "A performance comparison of bio-inspired behaviours for odour source localisation." In 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2019. http://dx.doi.org/10.1109/icarsc.2019.8733627.

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Lu, Tien-Fu. "Indoor odour source localisation using robot: Are there advantageous initial locations?" In 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181316.

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Kok Seng Eu and Kian Meng Yap. "Enhanced position based routing protocols for multi-sniffer robots to perform odour source localisation." In International Conference on Frontiers of Communications, Networks and Applications (ICFCNA 2014 - Malaysia). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1420.

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