面向高效安全行星导航的VLM赋能多模态系统
VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
AI总结：
针对行星探测复杂环境下的导航需求，提出VLM赋能的多模态行星漫游车导航系统，可根据地形复杂度切换导航模式，在保障安全的同时将长距离穿越效率提升79.5%。
摘要：
日益复杂多样的行星探测环境要求火星车具备更具适应性与灵活性的导航策略。本研究提出一种VLM（视觉语言模型）赋能的多模态系统，以实现行星漫游车高效且安全的自主导航。系统采用Vision-Language Model（VLM，视觉语言模型）通过图像输入解析场景信息，实现对地形复杂度的人类级理解。基于复杂度分类结果，系统会切换至最合适的导航模式，该模式由针对不同地形类型设计的感知、建图与规划模块组成，用于在抵达下一个航点前穿越前方地形。通过将局部导航系统与地图服务器、全局航点生成模块相集成，漫游车可应对复杂场景下的长距离导航任务。研究在多种仿真环境中对该导航系统进行了评估。与单模态保守导航方法相比，本文的多模态系统在存在各类障碍物的长距离穿越任务中，可大幅提升时间与能源效率，效率提升达79.5%，同时保留了地形危险规避能力以保障漫游车安全。更多系统信息详见https://chengsn1234.github.io/multi-mode-planetary-navigation/。
英文摘要：
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable navigation mode, composing of perception, mapping and planning modules designed for different terrain types, to traverse the terrain ahead before reaching the next waypoint. By integrating the local navigation system with a map server and a global waypoint generation module, the rover is equipped to handle long-distance navigation tasks in complex scenarios. The navigation system is evaluated in various simulation environments. Compared to the single-mode conservative navigation method, our multi-mode system is able to bootstrap the time and energy efficiency in a long-distance traversal with varied type of obstacles, enhancing efficiency by 79.5%, while maintaining its avoidance capabilities against terrain hazards to guarantee rover safety. More system information is shown at https://chengsn1234.github.io/multi-mode-planetary-navigation/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
