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...

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Main Authors: LUO, Tianze, CHEN, Zichen, SUBAGDJA, Budhitama, TAN, Ah-hwee
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/7561
https://ink.library.smu.edu.sg/context/sis_research/article/8564/viewcontent/Real_Time_Hierarchical_Map_Segmentation_for_Coordinating_Multirobot_Exploration.pdf
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spelling sg-smu-ink.sis_research-85642024-03-06T06:22:42Z Real-time hierarchical map segmentation for coordinating multi-robot exploration LUO, Tianze CHEN, Zichen SUBAGDJA, Budhitama TAN, Ah-hwee 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. 2023-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7561 info:doi/10.1109/ACCESS.2022.3171925 https://ink.library.smu.edu.sg/context/sis_research/article/8564/viewcontent/Real_Time_Hierarchical_Map_Segmentation_for_Coordinating_Multirobot_Exploration.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Autonomous agents Intelligent agents Multi-agent systems agent-based modeling image segmentation Artificial Intelligence and Robotics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Autonomous agents
Intelligent agents
Multi-agent systems
agent-based modeling
image segmentation
Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle Autonomous agents
Intelligent agents
Multi-agent systems
agent-based modeling
image segmentation
Artificial Intelligence and Robotics
Databases and Information Systems
LUO, Tianze
CHEN, Zichen
SUBAGDJA, Budhitama
TAN, Ah-hwee
Real-time hierarchical map segmentation for coordinating multi-robot exploration
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.
format text
author LUO, Tianze
CHEN, Zichen
SUBAGDJA, Budhitama
TAN, Ah-hwee
author_facet LUO, Tianze
CHEN, Zichen
SUBAGDJA, Budhitama
TAN, Ah-hwee
author_sort LUO, Tianze
title Real-time hierarchical map segmentation for coordinating multi-robot exploration
title_short 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_sort real-time hierarchical map segmentation for coordinating multi-robot exploration
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/7561
https://ink.library.smu.edu.sg/context/sis_research/article/8564/viewcontent/Real_Time_Hierarchical_Map_Segmentation_for_Coordinating_Multirobot_Exploration.pdf
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