MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning

Multimodal large language models have achieved strong performance across diverse vision-language tasks, yet their capabilities in UAV scenarios remain insufficiently explored. Recent UAV-oriented benchmarks have begun to evaluate MLLMs in aerial scenarios, but they typically focus on scene understanding, event recognition, or navigation completion, rather than jointly assessing the dual-cognition capability required for UAV agents: reasoning about both the UAV's own state and the external environment in multiview spatio-temporal contexts. To address this gap, we present UAV-DualCog, a benchmark for aerial multiview spatio-temporal reasoning built on this dual-cognition perspective. UAV-DualCog includes both image and video tasks to jointly evaluate self-state and environment-state reasoning, while requiring spatial or temporal grounding beyond discrete answer prediction. We also develop an automated pipeline that constructs data from scene-level semantic point clouds, yielding a scalable benchmark with diverse scenes, hundreds of landmarks, and thousands of QA samples. Extensive evaluations show that current MLLMs remain far from reliable in UAV dual cognition. Self-state reasoning, viewpoint transformation, precise spatial grounding, and temporal interval localization are persistent bottlenecks, and additional validation with thinking/frontier models and a human baseline confirms that the benchmark is understandable to humans but challenging for existing models. We further construct UAV-DualCog-Train from disjoint scenes and show through a lightweight optimization probe that it provides useful structured supervision, suggesting its value not only as an evaluation benchmark but also as a data resource for advancing MLLM-based UAV agents. Project website and supplementary materials: https://uav-dualcog.lozumi.com

Large Language Models (LLMs) are increasingly explored as high-level reasoning engines for cyber-physical systems, yet their application to real-time UAV swarm management remains challenging due to heterogeneous interfaces, limited grounding, and the need for long-running closed-loop execution. This paper presents a mission-agnostic, agent-enhanced LLM framework for UAV swarm control, where users express mission objectives in natural language and the system autonomously executes them through grounded, real-time interactions. The proposed architecture combines an LLM-based Agent Core with a Model Context Protocol (MCP) gateway and a Web-of-Drones abstraction based on W3C Web of Things (WoT) standards. By exposing drones, sensors, and services as standardized WoT Things, the framework enables structured tool-based interaction, continuous state observation, and safe actuation without relying on code generation. We evaluate the framework using ArduPilot-based simulation across four swarm missions and six state-of-the-art LLMs. Results show that, despite strong reasoning abilities, current general-purpose LLMs still struggle to achieve reliable execution - even for simple swarm tasks - when operating without explicit grounding and execution support. Task-specific planning tools and runtime guardrails substantially improve robustness, while token consumption alone is not indicative of execution quality or reliability.

In IoT-enabled smart-city settings, Uncrewed Aerial Vehicles (UAVs) are evolving from passive sensing platforms into cyber-physical decision makers that must respect operational rules under degraded observations and ambiguous language. Existing UAV and multimodal benchmarks cover aerial perception, navigation, collaboration, and task reasoning, but rarely test whether physical evidence, protocol constraints, and action risk stay coupled at critical decisions. We introduce MulRobBench, an offline, protocol-conditioned benchmark for Vision-Language-Action (VLA) UAV agents that links real UAV multimodal observations, protocol-level security-policy constraints, and action-level cyber-physical safety within an auditable decision contract. The evaluation set contains 3,024 samples spanning 17 task-taxonomy nodes and 12 metric scoring dimensions, organized around context understanding, multimodal evidence arbitration, degradation-aware reasoning, and risk-aware action planning. MulRobBench reports controlled semantic scores alongside strict structural diagnostics for policy compliance, formatting, unsafe actions, parsing, and dimension-level validity. Across 17 uniformly audited models, the best semantic protocol-decision score reaches 0.5141 and the best strict mean scoring-dimension accuracy reaches 0.1599. A matched 20-anchor modality-removal study changes 4-15 action selections per model, showing both visual and textual inputs influence decisions while the strongest input condition varies across metrics. Per-dimension and conditional analyses identify modality-trust selection, constraint extraction, strong glare, missing data, and high-entropy operator shorthand as principal sources of action instability. The central challenge is thus stable coupling of degraded evidence, security-policy constraints, and risk-bearing action, not isolated scene recognition.