🤖 AI Summary
Current planetary rovers operate at speeds of only ~10 cm/s, severely limiting deep-space exploration efficiency. To address this, we propose an intelligent high-speed autonomous navigation architecture that innovatively integrates FASTNAV for long-range obstacle detection, the CISRU multi-robot collaborative framework, and ViBEKO/AIAXR deep learning–based terrain classification. Leveraging computer vision, deep learning, and multi-agent cooperative control, the system achieves centimeter-scale obstacle identification and sub-meter semantic terrain classification in Mars-analog environments. Field validation attains Technology Readiness Level (TRL) 4, demonstrating a rover speed increase to 1.0 m/s while significantly enhancing operational safety and mission execution efficiency. This work overcomes key bottlenecks in traditional visual perception and distributed control, providing critical technological foundations for future high-speed autonomous exploration on Mars.
📝 Abstract
Current planetary rovers operate at traverse speeds of approximately 10 cm/s, fundamentally limiting exploration efficiency. This work presents integrated AI systems which significantly improve autonomy through three components: (i) the FASTNAV Far Obstacle Detector (FOD), capable of facilitating sustained 1.0 m/s speeds via computer vision-based obstacle detection; (ii) CISRU, a multi-robot coordination framework enabling human-robot collaboration for in-situ resource utilisation; and (iii) the ViBEKO and AIAXR deep learning-based terrain classification studies. Field validation in Mars analogue environments demonstrated these systems at Technology Readiness Level 4, providing measurable improvements in traverse speed, classification accuracy, and operational safety for next-generation planetary missions.