Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation
This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914722001891 |
| _version_ | 1797334779425718272 |
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| author | Chen Wang Zhaohui Shi Minqiang Gu Weicheng Luo Xiaomin Zhu Zhun Fan |
| author_facet | Chen Wang Zhaohui Shi Minqiang Gu Weicheng Luo Xiaomin Zhu Zhun Fan |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to the environmental input, including the positions of the targets and obstacles. Next, an adaptive decision mechanism is proposed, allowing each robot to choose the most well-adapted capture point on the pattern, based on its environment. The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly, without colliding with other robots or obstacles. The proposed decision mechanism, combined with the improved Vicsek-model, can form a uniform entrapment shape and create a revolving effect around targets while entrapping them. This study also enables swarm robots, with an adaptive pattern formation, to entrap multiple targets in complex environments. Swarm robots can be deployed in the military field of unmanned aerial vehicles’ (UAVs) entrapping multiple targets. Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method. |
| first_indexed | 2024-03-08T08:26:09Z |
| format | Article |
| id | doaj.art-16d5b6a2f85a46d88cbceb86bd867293 |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-03-08T08:26:09Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-16d5b6a2f85a46d88cbceb86bd8672932024-02-02T04:39:05ZengKeAi Communications Co., Ltd.Defence Technology2214-91472024-01-0131496509Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formationChen Wang0Zhaohui Shi1Minqiang Gu2Weicheng Luo3Xiaomin Zhu4Zhun Fan5Shantou University, Shantou, Guangdong, ChinaShantou University, Shantou, Guangdong, ChinaShantou University, Shantou, Guangdong, ChinaShantou University, Shantou, Guangdong, ChinaNational University of Defense Technology, Changsha, ChinaShantou University, Shantou, Guangdong, China; Key Lab of Digital Signal and Image Processing of Guangdong Province, China; Corresponding author. Department of Electronic and Information Engineering, Shantou University, 243 Daxue Road, Shantou, Guangdong, China.This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to the environmental input, including the positions of the targets and obstacles. Next, an adaptive decision mechanism is proposed, allowing each robot to choose the most well-adapted capture point on the pattern, based on its environment. The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly, without colliding with other robots or obstacles. The proposed decision mechanism, combined with the improved Vicsek-model, can form a uniform entrapment shape and create a revolving effect around targets while entrapping them. This study also enables swarm robots, with an adaptive pattern formation, to entrap multiple targets in complex environments. Swarm robots can be deployed in the military field of unmanned aerial vehicles’ (UAVs) entrapping multiple targets. Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.http://www.sciencedirect.com/science/article/pii/S2214914722001891Swarm intelligenceRevolutionary entrapmentFlockingRobotsGene regulatory networkVicsek-model |
| spellingShingle | Chen Wang Zhaohui Shi Minqiang Gu Weicheng Luo Xiaomin Zhu Zhun Fan Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation Defence Technology Swarm intelligence Revolutionary entrapment Flocking Robots Gene regulatory network Vicsek-model |
| title | Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| title_full | Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| title_fullStr | Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| title_full_unstemmed | Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| title_short | Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| title_sort | revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation |
| topic | Swarm intelligence Revolutionary entrapment Flocking Robots Gene regulatory network Vicsek-model |
| url | http://www.sciencedirect.com/science/article/pii/S2214914722001891 |
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