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 |
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. |
ru_RU |
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 |
|