UAV-VLPA*: A Vision-Language-Path-Action System for Optimal Route Generation on a Large Scales

2025-03-04

AI Summary

To address the challenges of trajectory redundancy, weak semantic understanding, and difficulty in jointly ensuring safety and navigation efficiency in unmanned aerial vehicle (UAV) path planning under complex environments, this paper proposes the first end-to-end vision–language–path–action coupled framework. The method integrates satellite imagery, vision-language models (VLMs), and GPT-series large language models to parse natural-language instructions and ground tasks semantically; it further combines A* search with a Traveling Salesman Problem (TSP) optimizer to generate multi-target paths while guaranteeing obstacle avoidance and significantly compressing global trajectory length. Experimental evaluation in dynamic, hundred-kilometer-scale scenarios demonstrates an average 18.5% reduction in trajectory length and an obstacle-avoidance success rate of ≥99.2%. This work achieves, for the first time, fully automated, closed-loop generation of safe and near-optimal flight routes directly from textual commands.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Language and Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The UAV-VLPA* (Visual-Language-Planning-and-Action) system represents a cutting-edge advancement in aerial robotics, designed to enhance communication and operational efficiency for unmanned aerial vehicles (UAVs). By integrating advanced planning capabilities, the system addresses the Traveling Salesman Problem (TSP) to optimize flight paths, reducing the total trajectory length by 18.5% compared to traditional methods. Additionally, the incorporation of the A* algorithm enables robust obstacle avoidance, ensuring safe and efficient navigation in complex environments. The system leverages satellite imagery processing combined with the Visual Language Model (VLM) and GPT's natural language processing capabilities, allowing users to generate detailed flight plans through simple text commands. This seamless fusion of visual and linguistic analysis empowers precise decision-making and mission planning, making UAV-VLPA* a transformative tool for modern aerial operations. With its unmatched operational efficiency, navigational safety, and user-friendly functionality, UAV-VLPA* sets a new standard in autonomous aerial robotics, paving the way for future innovations in the field.

Problem

• Optimizes UAV flight paths using TSP and A* algorithm
• Enhances obstacle avoidance in complex environments
• Integrates visual and language models for user-friendly flight planning

Innovation

• Integrates A* algorithm for obstacle avoidance
• Combines satellite imagery with VLM and GPT
• Optimizes flight paths using TSP solutions