VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation

TL;DR

This paper introduces a VLM-empowered multi-mode system for planetary rover navigation that adapts to terrain complexity, significantly improving efficiency while ensuring safety in complex exploration environments.

Contribution

The study presents a novel multi-mode navigation system leveraging Vision-Language Models to classify terrain and switch modes, enhancing efficiency and safety in planetary exploration.

Findings

Efficiency improved by 79.5% compared to single-mode methods

System maintains terrain hazard avoidance capabilities

Effective in diverse simulation environments

Abstract

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/.