Article

Robot-Task Suitability in Heterogeneous Multi-Robot Task Allocation

This study compares 10 deterministic methods and 2 LLM-based variants for heterogeneous multi-robot task allocation, covering rule-based, similarity-based, requirement-oriented, fuzzy, and semantic definitions of suitability. The results show that the methods often disagree on scoring and ranking candidate robots in the same scenario, that generic similarity measures can violate the suitability property of one-sided requirement satisfaction, and that LLM scoring introduces stochasticity and linguistic sensitivity. The paper was accepted to the FM-MRS Workshop at IROS 2026.

Part of the event: Comparative Study of Multi-Robot Task Suitability Methods Accepted to IROS 2026

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