mlsanityData to 14 Oct 2026

Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems

arXiv 2506.12710 ↗First posted 15 Jun 2025cs.ROcited by 7

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, safety-critical missions that demand rapid situational understanding and autonomous adaptation. This paper explores potential solutions for integrating MLLMs with UAV swarms to enhance the intelligence and adaptability across diverse tasks. Specifically, we first outline the fundamental architectures and functions of UAVs and MLLMs. Then, we analyze how MLLMs can enhance the UAV system performance in terms of target detection, autonomous navigation, and multi-agent coordination, while exploring solutions for integrating MLLMs into UAV systems. Next, we propose a practical case study focused on the forest fire fighting. To fully reveal the capabilities of the proposed framework, human-machine interaction, swarm task planning, fire assessment, and task execution are investigated. Finally, we discuss the challenges and future research directions for the MLLMs-enabled UAV swarm. An experiment illustration video could be found online at https://youtu.be/zwnB9ZSa5A4.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 10 of its 27 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

10 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing20 Jun

Other references · 9

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  3. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  4. 2023A Survey on Multimodal Large Language Modelscited by 909
  5. 2023RoCo: Dialectic Multi-Robot Collaboration with Large Language Modelscited by 172
  6. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  7. 2023Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?cited by 106
  8. 2024SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Modelscited by 21
  9. 2022UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubTcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorialcited by 4
  2. 2025SA-GCS: Semantic-Aware Gaussian Curriculum Scheduling for UAV Vision-Language Navigationcited by 3
  3. 2025Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognitioncited by 1
  4. 2026Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memorycited by 1
  5. 2025From Drone Imagery to Livability Mapping: AI-powered Environment Perception in Rural Chinacited by 0
  6. 2026Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Modelscited by 0
  7. 2026Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Responsecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
  2. 2025MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?
  3. 2026Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
  4. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
  5. 2026Agentic AI Meets Edge Computing in Autonomous UAV Swarms
  6. 2025General-Purpose Aerial Intelligent Agents Empowered by Large Language Models
  7. 2026Vision-Language Navigation for Aerial Robots: Towards the Era of Large Language Models
  8. 2026AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution