Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration
Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9819930/ |
| _version_ | 1797900240724951040 |
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| author | Tianze Luo Zichen Chen Budhitama Subagdja Ah-Hwee Tan |
| author_facet | Tianze Luo Zichen Chen Budhitama Subagdja Ah-Hwee Tan |
| author_sort | Tianze Luo |
| collection | DOAJ |
| description | Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible for the agents to visit. In this way, map segmentation and target allocation can be performed continually in real-time to efficiently explore the environment. To evaluate our proposed model, we conduct extensive experiments on map segmentation and multi-agent exploration. The results show that the proposed method can produce more accurate and meaningful segments leading to a higher level of efficiency in exploring the environment. Furthermore, the robustness tests by adding noises to the environments were conducted to simulate the performance of our model in the real-world environment. The results demonstrate the robustness of our model in map segmentation and multi-agent environment exploration. |
| first_indexed | 2024-04-10T08:42:51Z |
| format | Article |
| id | doaj.art-b1fb40ae45404ed39f757a3ebddd8824 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T08:42:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1fb40ae45404ed39f757a3ebddd88242023-02-23T00:00:39ZengIEEEIEEE Access2169-35362023-01-0111156801569210.1109/ACCESS.2022.31719259819930Real-Time Hierarchical Map Segmentation for Coordinating Multirobot ExplorationTianze Luo0https://orcid.org/0000-0001-9648-0982Zichen Chen1https://orcid.org/0000-0001-9024-8218Budhitama Subagdja2https://orcid.org/0000-0001-9774-0264Ah-Hwee Tan3https://orcid.org/0000-0003-0378-4069School of Computer Science and Engineering, Nanyang Technological University, Jurong West, SingaporeSchool of Computer Science and Engineering, Nanyang Technological University, Jurong West, SingaporeSchool of Computing and Information Systems, Singapore Management University, Bras Basah, SingaporeSchool of Computing and Information Systems, Singapore Management University, Bras Basah, SingaporeCoordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible for the agents to visit. In this way, map segmentation and target allocation can be performed continually in real-time to efficiently explore the environment. To evaluate our proposed model, we conduct extensive experiments on map segmentation and multi-agent exploration. The results show that the proposed method can produce more accurate and meaningful segments leading to a higher level of efficiency in exploring the environment. Furthermore, the robustness tests by adding noises to the environments were conducted to simulate the performance of our model in the real-world environment. The results demonstrate the robustness of our model in map segmentation and multi-agent environment exploration.https://ieeexplore.ieee.org/document/9819930/Autonomous agentsintelligent agentsmulti-agent systemsagent-based modelingimage segmentation |
| spellingShingle | Tianze Luo Zichen Chen Budhitama Subagdja Ah-Hwee Tan Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration IEEE Access Autonomous agents intelligent agents multi-agent systems agent-based modeling image segmentation |
| title | Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration |
| title_full | Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration |
| title_fullStr | Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration |
| title_full_unstemmed | Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration |
| title_short | Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration |
| title_sort | real time hierarchical map segmentation for coordinating multirobot exploration |
| topic | Autonomous agents intelligent agents multi-agent systems agent-based modeling image segmentation |
| url | https://ieeexplore.ieee.org/document/9819930/ |
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