Real-time hierarchical map segmentation for coordinating multi-robot 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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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/164995 |
| _version_ | 1811686484463321088 |
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| author | Luo, Tianze Chen, Zichen Subagdja, Budhitama Tan, Ah-Hwee |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Luo, Tianze Chen, Zichen Subagdja, Budhitama Tan, Ah-Hwee |
| author_sort | Luo, Tianze |
| collection | NTU |
| 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 | 2024-10-01T05:01:09Z |
| format | Journal Article |
| id | ntu-10356/164995 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:01:09Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1649952023-03-10T15:35:53Z Real-time hierarchical map segmentation for coordinating multi-robot exploration Luo, Tianze Chen, Zichen Subagdja, Budhitama Tan, Ah-Hwee School of Computer Science and Engineering Engineering::Computer science and engineering Autonomous Agents Intelligent Agents 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was supported in part by the National Research Foundation, Singapore, under its AI Singapore Programme under Award AISG2-RP-2020-019; and in part by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier-1 under Grant 19-C220-SMU-023. The work of Ah-Hwee Tan was supported by the Jubilee Technology Fellowship, Singapore Management University. 2023-03-07T02:49:23Z 2023-03-07T02:49:23Z 2022 Journal Article Luo, T., Chen, Z., Subagdja, B. & Tan, A. (2022). Real-time hierarchical map segmentation for coordinating multi-robot exploration. IEEE Access, 11, 15680-15692. https://dx.doi.org/10.1109/ACCESS.2022.3171925 2169-3536 https://hdl.handle.net/10356/164995 10.1109/ACCESS.2022.3171925 2-s2.0-85134270467 11 15680 15692 en AISG2-RP-2020-019 19-C220-SMU-023 IEEE Access © 2022 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Autonomous Agents Intelligent Agents Luo, Tianze Chen, Zichen Subagdja, Budhitama Tan, Ah-Hwee Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title | Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title_full | Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title_fullStr | Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title_full_unstemmed | Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title_short | Real-time hierarchical map segmentation for coordinating multi-robot exploration |
| title_sort | real time hierarchical map segmentation for coordinating multi robot exploration |
| topic | Engineering::Computer science and engineering Autonomous Agents Intelligent Agents |
| url | https://hdl.handle.net/10356/164995 |
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