A hybrid search algorithm for swarm robots searching in an unknown environment.
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching;...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4227730?pdf=render |
| _version_ | 1818129515723882496 |
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| author | Shoutao Li Lina Li Gordon Lee Hao Zhang |
| author_facet | Shoutao Li Lina Li Gordon Lee Hao Zhang |
| author_sort | Shoutao Li |
| collection | DOAJ |
| description | This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. |
| first_indexed | 2024-12-11T07:50:23Z |
| format | Article |
| id | doaj.art-43f695a72eb0485faab83c86abca7a5c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T07:50:23Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-43f695a72eb0485faab83c86abca7a5c2022-12-22T01:15:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01911e11197010.1371/journal.pone.0111970A hybrid search algorithm for swarm robots searching in an unknown environment.Shoutao LiLina LiGordon LeeHao ZhangThis paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.http://europepmc.org/articles/PMC4227730?pdf=render |
| spellingShingle | Shoutao Li Lina Li Gordon Lee Hao Zhang A hybrid search algorithm for swarm robots searching in an unknown environment. PLoS ONE |
| title | A hybrid search algorithm for swarm robots searching in an unknown environment. |
| title_full | A hybrid search algorithm for swarm robots searching in an unknown environment. |
| title_fullStr | A hybrid search algorithm for swarm robots searching in an unknown environment. |
| title_full_unstemmed | A hybrid search algorithm for swarm robots searching in an unknown environment. |
| title_short | A hybrid search algorithm for swarm robots searching in an unknown environment. |
| title_sort | hybrid search algorithm for swarm robots searching in an unknown environment |
| url | http://europepmc.org/articles/PMC4227730?pdf=render |
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