Few-Shot Learning-Enhanced Tiered Path Planning for Mars Rover Navigation
Ziyi Wang, Di Yu, Mina Khalilzadeh Fathi, Chaoying Pei · 2025
Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables quick updates and redeployment of the policy when necessary. Then, a modified A* path planning algorithm is proposed to generate paths on the tiered terrain maps. This algorithm takes into account the classified terrain tiers, allowing the rover to dynamically adjust its path based on real-time assessments. By integrating few-shot learning with the modified A* algorithm, the rover is equipped to make real-time intelligent decisions, enhancing its ability to navigate complex terrains effectively. Simulation results demonstrate the rover’s enhanced capability to navigate complex terrains, illustrating the effectiveness and flexibility of this integrated approach.