The content discusses Large-Scale Multi-Robot Coverage Path Planning via Local Search. It introduces LS-MCPP, a novel local search framework that integrates the Extended-STC paradigm and boundary editing operators to optimize Multi-Robot Coverage Path Planning solutions directly on decomposed graphs. The study compares LS-MCPP with baseline algorithms, demonstrating superior performance in terms of makespan reduction and runtime efficiency across various instances.
The LS-MCPP framework leverages the Extended-STC paradigm to systematically explore good coverage paths directly on decomposed graphs for Multi-Robot Coverage Path Planning tasks. By integrating boundary editing operators like grow, deduplicate, and exchange operators, LS-MCPP aims to achieve cost-balancing coverage paths efficiently. The study includes an empirical evaluation comparing LS-MCPP with baseline algorithms across different instances, highlighting its effectiveness in optimizing large-scale real-world coverage tasks.
LS-MCPP outperforms VOR, MFC, MSTC∗ for all instances within 20 minutes of runtime limit. It showcases a notable makespan reduction of up to 67.0%, 35.7%, and 30.3% compared to the baselines. Additionally, an ablation study validates the importance of different components of LS-MCPP such as ESTC vs Full-STC comparison, initial solution selection impact, operator sampling methods comparison, and forced deduplication function validation.
Overall, the study demonstrates the efficacy of LS-MCPP in improving Multi-Robot Coverage Path Planning solutions through innovative approaches like Extended-STC paradigm integration and boundary editing operators utilization.
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arxiv.org
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