Research/ PLANETARY ROBOTICS
AI-Enabled Capabilities to Facilitate Next-Generation Rover Surface Operations
Cristina Luna, Robert Field, Steven Kay
Perception & State EstimationOpenAlex topic: Robotics and Sensor-Based Localization · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics · HRI & TeleoperationOpenAlex keyword: human-robot collaboration
arXivcs.ROOpenAlexRobotics and Sensor-Based Localization · Reinforcement Learning in Robotics · Planetary Science and Exploration
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.
Comments: Paper for 18th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA), presented on October 7th at Leiden, Netherlands