From Programmed Execution to Autonomous Reasoning: The LLM-Driven Paradigm Shift in Air-Ground Collaborative Systems
TL;DR
This review critically examines how large language models (LLMs) and multimodal large language models (MLLMs) are reshaping the intelligence paradigm of air-ground heterogeneous collaborative systems between 2023 and 2026 and argues that the core of this transformation is a change in how AGCS understand tasks, coordinate heterogeneous agents, and reason in unknown environments.
Abstract
Air-ground collaborative systems (AGCS), in which unmanned aerial vehicles survey from above while ground vehicles navigate the terrain below, can achieve capabilities far beyond those of any single platform when they share perception and coordinate action. AGCS are undergoing a three-stage paradigm shift: from rule- and reinforcement learning-driven programmed execution, through vision–language navigation that introduces a language interface, to MLLM-driven autonomous perception, reasoning, and heterogeneous collaboration. Taking this paradigm shift as its organizing thread, this review critically examines how large language models (LLMs) and multimodal large language models (MLLMs) are reshaping the intelligence paradigm of air-ground heterogeneous collaborative systems between 2023 and 2026. We argue that the core of this transformation is not the substitution of one technical tool for another, but a change in how AGCS understand tasks, coordinate heterogeneous agents, and reason in unknown environments. They are moving from reliance on pre-specified structures toward semantic understanding and autonomous decision-making. Organized around a three-stage framework, the review covers collaborative perception, vision–language navigation (VLN), vision–language–action (VLA) models, multi-agent collaboration, and air-ground heterogeneous MLLM coordination. We analyze the capability boundaries of MLLMs in air-ground settings and outline the key challenges and roadmap from proof-of-concept to generalizable frameworks.