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
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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CADENZA compiles each semantic operator instance into an intent-specific TxRA DAG and selects an executable plan under quality–latency–cost preferences via seed synthesis, structural rewrites, semantic alternatives, data-aware routers, and Bayesian optimization.
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.
citing papers explorer
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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.
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CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing
CADENZA compiles each semantic operator instance into an intent-specific TxRA DAG and selects an executable plan under quality–latency–cost preferences via seed synthesis, structural rewrites, semantic alternatives, data-aware routers, and Bayesian optimization.
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Verification-Gated Agentic Mission-State Governance for Intelligent Industrial Multi-Robot Systems
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.
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Large Language Models for Multi-Robot Systems: A Survey
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.