OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.
A novel multi- robot task assignment scheme based on a multi-angle k-means clustering algorithm and a two-stage load-balancing strategy
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Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming
OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.