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Efficient graph-based area partitioning for balanced multi-robot coverage path planning
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AI
DOI：10.1016/j.robot.2026.105445.png)
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Abstract
En 中文
This paper introduces a novel, direct, graph-theoretic framework for area partitioning in multi-robot coverage path planning (MCPP), which works directly with the original area represented as a graph. MCPP is an essential component of mobile robot technology, particularly for exploration and survey missions that require full area coverage. Traditional methods often rely on grid map representations with iterative schemes, leading to loss of geometric fidelity (e.g., discretization errors) and higher computational costs, which make balancing workloads among robots challenging. The proposed framework addresses these issues by efficiently dividing both convex and non-convex polygons, ensuring fair workload distribution while avoiding overlaps and operational inefficiencies arising from kinematically constraining grid structures. Extensive simulations across diverse environments demonstrate the effectiveness of the algorithm in ensuring equitable and efficient area coverage.
Keywords:
Multi-robot system
Area partitioning
Coverage path planning
Journal
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5.2
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621
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