VLN-Pilot: Large Vision-Language Model as an Autonomous Indoor Drone Operator

2026-02-05

AI Summary

This work addresses the challenge of enabling autonomous drones to accurately interpret and execute free-form natural language instructions in GPS-denied indoor environments. It presents the first end-to-end indoor drone control system that directly leverages a large vision-language model (VLLM), integrating linguistic semantics with visual perception to achieve semantic-driven, context-aware high-level flight behaviors without task-specific engineering. The proposed framework unifies multimodal reasoning, visual navigation, obstacle avoidance, and dynamic response mechanisms within a self-built high-fidelity simulation environment. Experimental results demonstrate successful execution of complex, long-horizon, multi-objective navigation tasks, significantly improving both task success rates and system adaptability compared to conventional approaches.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

This paper introduces VLN-Pilot, a novel framework in which a large Vision-and-Language Model (VLLM) assumes the role of a human pilot for indoor drone navigation. By leveraging the multimodal reasoning abilities of VLLMs, VLN-Pilot interprets free-form natural language instructions and grounds them in visual observations to plan and execute drone trajectories in GPS-denied indoor environments. Unlike traditional rule-based or geometric path-planning approaches, our framework integrates language-driven semantic understanding with visual perception, enabling context-aware, high-level flight behaviors with minimal task-specific engineering. VLN-Pilot supports fully autonomous instruction-following for drones by reasoning about spatial relationships, obstacle avoidance, and dynamic reactivity to unforeseen events. We validate our framework on a custom photorealistic indoor simulation benchmark and demonstrate the ability of the VLLM-driven agent to achieve high success rates on complex instruction-following tasks, including long-horizon navigation with multiple semantic targets. Experimental results highlight the promise of replacing remote drone pilots with a language-guided autonomous agent, opening avenues for scalable, human-friendly control of indoor UAVs in tasks such as inspection, search-and-rescue, and facility monitoring. Our results suggest that VLLM-based pilots may dramatically reduce operator workload while improving safety and mission flexibility in constrained indoor environments.

Problem

• Vision-Language Navigation
• Autonomous Drone
• Indoor Navigation
• Natural Language Instruction
• GPS-denied Environment

Innovation

• Vision-Language Model
• Autonomous Drone Navigation
• Indoor UAV
• Natural Language Instruction
• Multimodal Reasoning