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Distributed Maze Exploration Using Multiple Agents and Optimal Goal Assignment
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AI
DOI：10.1109/ACCESS.2024.3431909.png)
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Abstract
En 中文
Robotic exploration has long captivated researchers aiming to map complex environments efficiently. Techniques such as potential fields and frontier exploration have traditionally been employed in this pursuit, primarily focusing on solitary agents. Recent advancements have shifted towards optimizing exploration efficiency through multiagent systems. However, many existing approaches overlook critical real-world factors, such as broadcast range limitations, communication costs, and coverage overlap. This paper addresses these gaps by proposing a distributed maze exploration strategy (CU-LVP) that assumes constrained broadcast ranges and utilizes Voronoi diagrams for better area partitioning. By adapting traditional multiagent methods to distributed environments with limited broadcast ranges, this study evaluates their performance across diverse maze topologies, demonstrating the efficacy and practical applicability of the proposed method. The code and experimental results supporting this study are available in the following repository: https://github.com/manouslinard/multiagent-exploration/.
Keywords:
Task analysis
Merging
Robot kinematics
Sensors
Costs
Simultaneous localization and mapping
Robustness
Multi-agent systems
Cost-utility
distributed maze exploration
multiagent
potential fields
Voronoi partitioning