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URGE: Efficient Decentralized Multirobot Exploration Guided by Unknown Regions Under Limited Communication
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DOI：10.1109/tro.2026.3714659.png)
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Abstract
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Multirobot autonomous exploration often suffers from low efficiency and high communication overhead. To address this, we propose an unknown-region-guided autonomous exploration framework (URGE), which introduces a lightweight, subregion-based environmental representation and information sharing scheme to enable a task allocation strategy that jointly optimizes cost and task load, ultimately achieving spatially dispersed exploration. To capture sufficient spatial information while significantly reducing inter-robot communication volume, the framework incorporates a novel regionalized exploration information map (REIM) that abstracts the environment into subregions with different states. Based on the REIM, a task allocation strategy formulated as a joint optimization problem is proposed. It minimizes the total path cost while balancing the task load across robots, encouraging spatially dispersed exploration and fully leveraging each robot's exploration capability. Furthermore, we extend prior route planning strategies by introducing global geometric cues. This enables more globally informed exploration routes and promotes the exploration of distant, unknown regions. The proposed framework is comprehensively evaluated using five metrics through extensive comparisons with existing methods, ablation studies, and real-world experiments. Compared with the state-of-the-art methods, URGE improves exploration efficiency by 13.3%–45.6% and reduces redundant exploration by 22.5%–51.5%. The evaluation results also demonstrate that URGE achieves superior cooperative performance and strong practical applicability, while maintaining low communication overhead.
Keywords:
Autonomous exploration
limited communication
multirobot system
task allocation
unknown regions
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