MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.
DART-LLM: Dependency- Aware Multi-Robot Task Decomposition and Execution using Large Language Models
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CADENZA introduces TxRA and dual planners to compile semantic operator intents into optimized task DAGs, claiming large gains in quality, latency, and cost on SemBench.
Presents a verification-gated agentic mission-state governance framework using synchronized task forests and blackboards with deterministic verification before any state commits in multi-robot systems.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
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MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning
MultiUAV-Plat supplies a new RESTful simulation platform and 1500-task benchmark where Agent4Drone reaches 57.9% task pass rate versus 30.6% for ReAct baseline across 75 multi-UAV missions.