A robust method for large-scale route optimization on lunar surface utilizing a multi-level map model  

作　　者：Yutong JIA Shengnan ZHANG Bin LIU Kaichang DI Bin XIE Jing NAN Chenxu ZHAO Gang WAN 

机构地区：[1]Department of Surveying and Mapping and Space Environment,Space Engineering University,Beijing 101416,China [2]State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China [3]State IJR Center of Aerospace Design and Additive Manufacturing,Northwestern Polytechnical University,Xi’an,Shaanxi 710072,China

出　　处：《Chinese Journal of Aeronautics》2025年第3期134-150,共17页中国航空学报(英文版)

基　　金：co-supported by the National Key Research and Development Program of China(No.2022YFF0503100);the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).

摘　　要：As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.

关 键 词：Crewed lunar exploration Long-range path planningi Multi-level map Deep learning Volcanic activities 

分 类 号：V476.3[航空宇航科学与技术—飞行器设计]