Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2023

Abstract

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.

Keywords

Autonomous agents, Intelligent agents, Multi-agent systems, agent-based modeling, image segmentation

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Access

Volume

11

Issue

1

First Page

15680

Last Page

15692

ISSN

2169-3536

Identifier

10.1109/ACCESS.2022.3171925

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

http://doi.org/10.1109/ACCESS.2022.3171925

Download

Share

COinS