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Поиск путей для группы автономных транспортных средств при исследовании неизвестной территорий

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dc.contributor.author Кузаков, Дмитрий Евгеньевич ru_RU
dc.contributor.author Дьяков, Михаил Станиславович ru_RU
dc.contributor.author Лаврентьев, Михаил Михайлович ru_RU
dc.contributor.author Kuzakov, Dmitriy Evgenevich en
dc.contributor.author Diakov, Mihail Stanislavovich en
dc.contributor.author Lavrentyev, Mikhail Mikhailovich en
dc.creator Новосибирский государственный университет ru_RU
dc.creator ООО «СофтЛаб-НСК» ru_RU
dc.creator Институт автоматики и электрометрии СО РАН ru_RU
dc.creator Novosibirsk State University en
dc.creator SoftLab-NSK Co. Ltd en
dc.creator Institute of Automation and Electrometry SB RAS en
dc.date.accessioned 2017-01-23T11:34:45Z
dc.date.available 2017-01-23T11:34:45Z
dc.date.issued 2016-06
dc.identifier.citation Кузаков Д. Е., Дьяков М. С., Лаврентьев М. М. Поиск путей для группы автономных транспортных средств при исследовании неизвестной территорий // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2016. Т. 14, № 2. С. 59–71. ru_RU
dc.identifier.citation Kuzakov D. E., Diakov M. S., Lavrentyev M. M. Path Planning for Multi-Robot Exploration Using Frontier Space Clusterization // Vestnik NSU Series: Information Technologies. - 2016. - Volume 14, Issue No 2. - P. 59-71. - ISSN 1818-7900. (in Russian). en
dc.identifier.issn 1818-7900
dc.identifier.uri https://lib.nsu.ru/xmlui/handle/nsu/11538
dc.description.abstract Представлен алгоритм исследования заранее неизвестной территории с помощью группы автономных транспортных средств (АТС). В нем используется новый метод выбора точек назначения для каждого АТС из группы. Новизна данного метода заключается в использовании кластеризации граничной области – области карты препятствий, находящейся на границе ее исследованной части. Каждому АТС сопоставляется некоторый кластер с помощью поиска паросочетания минимального веса. Точка назначения выбирается из кластера с помощью функции приоритета – функции, определяющей выгодность выбора клетки в зависимости от затрат на ее достижение, количества полученной информации и расстояния до целей других АТС. ru_RU
dc.description.abstract In this paper, a path planning algorithm for multi-robot exploration is presented. It is developed for exploration in initially unknown areas. The algorithm is based on a novel method of choosing exploration targets. This method uses clusterization of frontier space – part of explored map space which is situated on its border with an unexplored part. Every robot is being associated with a cluster. Then the exploration target for the robot is chosen from associated cluster with a priority function. This function defines utility for choosing a map cell considering traverse cost, information gain and distance to other robots' targets. en
dc.language.iso ru ru_RU
dc.publisher Новосибирский государственный университет ru_RU
dc.subject исследование территории ru_RU
dc.subject кластеризация граничной области ru_RU
dc.subject гранично-ориентированный алгоритм ru_RU
dc.subject группа автономных транспортных средств ru_RU
dc.subject multi-robot exploration en
dc.subject frontier space clusterization en
dc.subject frontier-based algorithm en
dc.title Поиск путей для группы автономных транспортных средств при исследовании неизвестной территорий ru_RU
dc.title.alternative Path Planning for Multi-Robot Exploration Using Frontier Space Clusterization en
dc.type Article ru_RU
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dc.description.reference 1. Gabrielly Y., Rimon E. Spanning-tree based coverage of continuous areas by a mobile robot // Annals of Mathematics and Artificial Intelligence. 2001. No. 31. P. 77–98. 2. Hazon N., Kaminka G. On redundancy, efficiency and robustness in coverage for multi-robot // Robot Autonomous System. 2008. No. 56. P. 1102–1114. 3. Murphy L., Newman P. Using incomplete online metric maps for topological exploration with the gap navigation tree // IEEE International Conference on Robotics and Automation, 2008. P. 2717–2722. 4. Santosh D., Achar S., Jawahar C. V. Autonomous image-based exploration for mobile robot navigation // IEEE International Conference on Robotics and Automation, 2008. P. 47–60. 5. Andries M., Charpillet F. Multi-robot exploration of unknown environments with identication of exploration completion and post-exploration rendezvous using ant algorithms // IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2013. P. 5571–5578. 6. Liu T. M., Lyons D. M. Leveraging Area Bounds Information for Autonomous Multi-Robot Exploration. 7. Senthilkumar K., Bharadwaj K. Multi-robot terrain coverage by constructing multiple spanning trees simultaneously // International Journal of Robotics and Automation. 2010. Vol. 3. No. 25. P. 195–203. 8. Lau H. Behavioural approach for multi-robot exploration // Proc. of the 2003 Australasian Conference on Robotics and Automation, 2003. 9. Yamauchi B. A frontier-based approach for autonomous exploration // IEEE International Symposium on Computational Intelligence in Robotics and Automation, 1997. P. 146–151. 10. Gonzalez-Banos H. H., Latombe J. C. Navigation strategies for exploring indoor environments // The International Journal of Robotics Research. 2002. Vol. 21, 10–11. P. 829–848. 11. Marjovi A., Marques L. Multi-robot topological exploration using olfactory cues // Distributed Autonomous Robotic Systems. 2013. P. 47–60. 12. Burgard W., Moors M., Fox D., Simmons R., Thrun S. Collaborative multi-robot exploration // IEEE International Conference on Robotics and Automation. 2000. Vol. 1. P. 476–481. 13. Al Khawaldah M., Al-Khedher M., Al-Adwan I., Al Rawashdeh A. An Autonomous Exploration Strategy for Cooperative Mobile Robots // Journal of Software Engineering and Applications. 2014. Vol. 7. No. 3. P. 142–149. 14. Solanas A., Garcia M. A. Coordinated multi-robot exploration through unsupervised clustering of unknown space // IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004. Vol. 1. P. 717–721. 15. Steinhaus H. Sur la division des corp materiels en parties // Bull. Acad. Polon. Sci. 1956. Vol. 1. P. 801–804. 16. Kuhn H. W. The Hungarian method for the assignment problem // Naval Research Logistics. 1955. Vol. 2, 1–2. P. 83–97. 17. Dolgov D., Thrun S., Montemerlo M., Diebel J. Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments // The International Journal of Robotics Research. 2010. No. 29. P. 485–501. 18. Bradski G. Clustering and Search in Multi-Dimensional Spaces // Dr. Dobb’s Journal of Software Tools. 2000. en
dc.relation.ispartofvolume 14
dc.relation.ispartofnumber 2
dc.relation.ispartofpages 59-71


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