Article(id=1322962942555194040, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322962941087195876, articleNumber=null, orderNo=null, doi=10.11834/jig.260123, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1772899200000, receivedDateStr=2026-03-08, revisedDate=1778083200000, revisedDateStr=2026-05-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348544968, onlineDateStr=2026-10-30, pubDate=1792080000000, pubDateStr=2026-10-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348544968, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348544968, creator=13701087609, updateTime=1793348544968, updator=13701087609, issue=Issue{id=1322962941087195876, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='10', pageStart='3441', pageEnd='3818', issueExtLink='null', onlineDate='null', pubDate='1792080000000', pubDateStr='2026-10-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793348544618, creator='13701087609', updateTime=1793348641113, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963345913021357, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322962941087195876, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963345913021358, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322962941087195876, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3441, endPage=3459, ext={EN=ArticleExt(id=1322962943532466873, articleId=1322962942555194040, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Embodied intelligence methods for deep space exploration： a survey, columnId=1249044010699002654, journalTitle=Journal of Image and Graphics, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

As deep space exploration missions continue to advance toward higher autonomy，longer operational duration，and more complex environments，the traditional task paradigm dominated by ground-based teleoperation can no longer satisfy the requirements for real-time，reliable，and efficient operations in lunar，Martian，and farther deep-space scenarios. Under conditions such as significant communication delay，limited prior environmental knowledge，strict onboard resource constraints，and high trial-and-error cost，future deep space platforms are expected to move beyond single-command execution toward integrated autonomous operation. In this context，embodied intelligence is gradually emerging as an important research direction for deep space exploration. The reason is that it provides a unified framework for coupling environmental perception，autonomous decision-making，and physical execution，which improves the adaptability and autonomy of exploration systems in long-duration extraterrestrial missions. This study systematically reviews recent progress in intelligent methods for deep space exploration from the perspective of embodied intelligence. Focusing on two central challenges—limited perception capability in highly unstructured extraterrestrial environments and insufficient autonomous decision-making capability under long-horizon，high-risk，and resource-constrained conditions—the study reviews recent advances in deep space visual perception and deep space embodied intelligence，and summarizes representative datasets，typical tasks，and evaluation protocols. For visual perception，the survey first reviews traditional image processing-based methods，including stereo terrain reconstruction，visual odometry，photogrammetric modeling，and large-scale localization. These methods established the technical foundation for early planetary exploration because they are grounded in explicit geometric constraints and therefore offer strong interpretability and engineering reliability. However，their effectiveness is increasingly challenged by weak textures，repeated patterns，severe illumination changes，occlusion，and long-range cumulative drift in lunar and Martian environments. The study then examines deep learning-based visual perception methods，which cover terrain semantic classification and segmentation，cross-domain adaptation，and multimodal terrain understanding. Compared with traditional geometry-driven approaches，deep models provide stronger semantic expressiveness and improved environmental understanding in complex planetary scenes. At the same time，their performance still depends heavily on data coverage，annotation quality，training strategy，and sim-to-real consistency，while onboard deployment is constrained by limited computation and energy budgets. From this perspective，the development of deep space perception is gradually shifting from closed-dataset recognition accuracy toward robust representation，long-term adaptability，and task-oriented environmental understanding. From the perspective of embodied intelligence，the study categorizes deep space tasks into three major classes： autonomous visual navigation，autonomous mission planning，and autonomous scientific operation. On this basis，the methodological evolution of deep space embodied intelligence can be viewed in three stages. The first stage is represented by teleoperation-centered control models，in which planning and key decisions are mainly completed on the ground，while the onboard system executes predefined instructions with limited rule-based local autonomy. Although this paradigm has been essential for ensuring mission safety and controllability，its strong dependence on human intervention limits its capability to support future long-duration missions in uncertain environments. The second stage is characterized by deep-learning-enhanced local autonomy. In this stage，learning models are embedded into specific modules of conventional engineering pipelines to improve terrain understanding，candidate path evaluation，risk estimation，target selection，and onboard data screening，while the overall decision and control chain remains largely interpretable and verifiable. The third stage involves multimodal large models and vision-language-action systems，which introduce higher-level cognitive capability into deep space systems. By unifying multimodal observations，state information，and task instructions in a shared semantic space，these methods enable cross-modal reasoning，task decomposition，dynamic replanning，and long-horizon decision organization. The survey argues that such models are promising for future high-autonomy missions. However，under current engineering conditions，they are more suitable as high-level cognition and task-orchestration modules than as direct substitutes for low-level planning and control. The study further reviews datasets and evaluation methods for visual perception and embodied intelligence. Compared with terrestrial benchmarks，publicly available deep space datasets remain limited in scale，annotation richness，and task diversity. For embodied intelligence，the challenge is even more fundamental，considering that datasets must support temporality，interaction，and closed-loop evaluation by including not only observations but also pose，action，state，and feedback information. As a result，high-fidelity simulation environments and digital twin-style benchmarks currently play an important role in supporting reproducible system evaluation. Correspondingly，evaluation protocols are also evolving from static recognition metrics toward task-level criteria involving safety，effectiveness，efficiency，robustness，and long-horizon reliability. Based on the abovementioned survey，the study discusses several future directions for deep space embodied intelligence，including perceptual generalization in strongly unstructured environments，trustworthy autonomous decision-making，system-level collaboration across perception and control modules，and lightweight deployment on resource-constrained platforms. In summary，deep space embodied intelligence is emerging as a system-level intelligence problem shaped by extreme extraterrestrial environments，long-duration mission chains，strict resource limitations，and high reliability requirements. Its future advancement depends not only on improving individual algorithms but also on establishing an integrated framework that unifies perception，decision-making，execution，validation，and deployment for real deep-space missions. This survey aims to provide a structured reference and clearer analytical perspective for the further development and application of autonomous intelligence technologies in deep space exploration.

, authors=Daixun Li, Weiying Xie^*, Yunsong Li, authorsList=Daixun Li, Weiying Xie, Yunsong Li, authorCompany=null, correspAuthors=Weiying Xie, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322962953162588862, articleId=1322962942555194040, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=面向深空探测的具身智能方法研究进展, columnId=1249044010992603937, journalTitle=中国图象图形学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

随着深空探测任务不断向高自主、长时序和复杂环境作业发展，传统依赖地面的遥控遥操任务模式已难以满足月球、火星及更远深空环境下实时、可靠和高效作业的需求。面向这一发展趋势，具身智能逐渐成为深空探测的重要研究方向，其核心在于推动探测系统由单一指令执行向感知、决策与执行一体化自主运行演进，从而提升深空任务中的环境感知能力与自主决策水平。本文系统回顾了深空探测智能化方法的研究进展，围绕深空感知能力受限、自主决策能力不足等关键问题，梳理了深空视觉感知方法与深空具身智能方法的研究现状，并对相关数据集、典型任务及评价方式进行了归纳总结。在此基础上，进一步从感知泛化、可信决策、系统协同与轻量化部署等方面，对深空探测具身智能的发展方向进行了展望，旨在为深空探测自主智能技术的研究与应用提供新的技术路径。

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李岱勋，男，博士研究生，主要研究方向为遥感分布式在轨处理和具身智能。E-mail： ldx@stu.xidian.edu.cn

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李岱勋，男，博士研究生，主要研究方向为遥感分布式在轨处理和具身智能。E-mail： ldx@stu.xidian.edu.cn

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谢卫莹，通信作者，女，教授，主要研究方向为遥感图像在轨实时处理、分布式边缘计算及具身智能。E-mail： wyxie@xidian.edu.cn

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谢卫莹，通信作者，女，教授，主要研究方向为遥感图像在轨实时处理、分布式边缘计算及具身智能。E-mail： wyxie@xidian.edu.cn

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李云松，男，教授，主要研究方向为图像压缩编码和图像处理。E-mail： ysli@mail.xidian.edu.cn

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李云松，男，教授，主要研究方向为图像压缩编码和图像处理。E-mail： ysli@mail.xidian.edu.cn

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Atlanta，USA： PMLR： 2165-2183, articleTitle=RT-2： vision-language-action models transfer web knowledge to robotic control, refAbstract=null)], funds=[Fund(id=1322962957709214438, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, awardId=null, language=EN, fundingSource=National Natural Science Foundation of China （62531018，U24B20136）； Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China （JYB2025XDXM105）, fundOrder=null, country=null), Fund(id=1322962957780517607, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, awardId=null, language=CN, fundingSource=国家自然科学基金项目（62531018，U24B20136）；教育部学科先导突破项目（JYB2025XDXM105）, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322962953531687615, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, xref=null, ext=[AuthorCompanyExt(id=1322962953540076224, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, companyId=1322962953531687615, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Communication Engineering，Xidian University，Xi’an 710071，China), AuthorCompanyExt(id=1322962953552659137, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, companyId=1322962953531687615, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=西安电子科技大学通信工程学院，西安　710071)])], figs=[ArticleFig(id=1322962955322655454, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=EN, label=Fig. 1, caption=Deep space sensor data challenge, figureFileSmall=ZErMtz19TTxmRX1T5BdWow==, figureFileBig=6K5/hJtgvKxuZ2jpFefwZg==, tableContent=null), ArticleFig(id=1322962955410735839, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=CN, label=图1, caption=深空传感器数据挑战

（（a） high sensor noise； （b） irregular spatial distribution；

（c） complex terrain structure）

, figureFileSmall=ZErMtz19TTxmRX1T5BdWow==, figureFileBig=6K5/hJtgvKxuZ2jpFefwZg==, tableContent=null), ArticleFig(id=1322962955603673824, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=EN, label=Fig.2, caption=Overview of embodied intelligence model, figureFileSmall=GLDMn0JdUQExfaUoSGeTBA==, figureFileBig=Aal4kUigA9fdXPXjUqs3iQ==, tableContent=null), ArticleFig(id=1322962955674976993, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=CN, label=图2, caption=具身智能模型概述

（（a） multimodal inputs for deep-space embodied intelligence model；（b） hierarchical multimodal language model；

（c） end-to-end vision-language-action model；

（d） end-to-end world-action model）

, figureFileSmall=GLDMn0JdUQExfaUoSGeTBA==, figureFileBig=Aal4kUigA9fdXPXjUqs3iQ==, tableContent=null), ArticleFig(id=1322962955742085858, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=EN, label=Table 1, caption=Overview of visual perception methods for deep space exploration, figureFileSmall=null, figureFileBig=null, tableContent=

任务类别	文献	方法类别	数据来源	主要任务
导航定位	Olson等人（2007）	立体视觉	火星探测器/轨道飞行器影像	火星三维地形建模
	Maimone等人（2007）	视觉里程计	火星探测器导航相机	火星车视觉里程计
	Li等人（2013）	立体视觉	月球探测车	月球车里程计
	马友青等人（2014）	立体导航	月球探测车	月球车导航定位
	耿迅和徐青（2022）	摄影测量	火星车高分辨率立体相机/月球勘测轨道飞行器窄角相机	行星摄影测量
	Driver等人（2023）	深度学习	小行星	自主导航
	Chiuchiarelli等人（2024）	深度学习	火星探测车	自主导航
目标检测	Bickel等人（2021）	深度学习	火星勘测轨道飞行器/月球勘测轨道飞行器	落石检测
	Lyu等人（2023）	深度学习	“祝融号”火星车	岩石检测
	曹学欢等人（2025）	深度学习	“祝融号”火星车	岩石检测
分类分割	Rothrock等人（2016）	深度学习	火星 2020 火星车	地形分类
	Swan等人（2021）	深度学习	“好奇号”、“机遇号”和“勇气号”火星探测车	地形分类
	Barrett等人（2022）	深度学习	火星探测车	地貌分类
	Vincent等人（2022）	深度学习	“好奇号”、“毅力号”火星探测车	地形分类
	Goh等人（2022）	深度学习	火星探测车	地形分类
	Tan等人（2024）	深度学习	火星探测车	地形分类
	Mohammad等人（2024）	深度学习	火星探测车	地形分类
	Castilla-Arquillo等人（2025）	深度学习	火星探测车	地形分类
	Panambur和Parente（2025）	深度学习	火星探测车	地形分类
	Tan等人（2025）	深度学习	火星探测车	零样本地形分类
	Jiao等人（2025）	深度学习	月球探测车	月球障碍检测

), ArticleFig(id=1322962955813389027, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=CN, label=表1, caption=

深空探测视觉感知方法归纳

, figureFileSmall=null, figureFileBig=null, tableContent=

任务类别	文献	方法类别	数据来源	主要任务
导航定位	Olson等人（2007）	立体视觉	火星探测器/轨道飞行器影像	火星三维地形建模
	Maimone等人（2007）	视觉里程计	火星探测器导航相机	火星车视觉里程计
	Li等人（2013）	立体视觉	月球探测车	月球车里程计
	马友青等人（2014）	立体导航	月球探测车	月球车导航定位
	耿迅和徐青（2022）	摄影测量	火星车高分辨率立体相机/月球勘测轨道飞行器窄角相机	行星摄影测量
	Driver等人（2023）	深度学习	小行星	自主导航
	Chiuchiarelli等人（2024）	深度学习	火星探测车	自主导航
目标检测	Bickel等人（2021）	深度学习	火星勘测轨道飞行器/月球勘测轨道飞行器	落石检测
	Lyu等人（2023）	深度学习	“祝融号”火星车	岩石检测
	曹学欢等人（2025）	深度学习	“祝融号”火星车	岩石检测
分类分割	Rothrock等人（2016）	深度学习	火星 2020 火星车	地形分类
	Swan等人（2021）	深度学习	“好奇号”、“机遇号”和“勇气号”火星探测车	地形分类
	Barrett等人（2022）	深度学习	火星探测车	地貌分类
	Vincent等人（2022）	深度学习	“好奇号”、“毅力号”火星探测车	地形分类
	Goh等人（2022）	深度学习	火星探测车	地形分类
	Tan等人（2024）	深度学习	火星探测车	地形分类
	Mohammad等人（2024）	深度学习	火星探测车	地形分类
	Castilla-Arquillo等人（2025）	深度学习	火星探测车	地形分类
	Panambur和Parente（2025）	深度学习	火星探测车	地形分类
	Tan等人（2025）	深度学习	火星探测车	零样本地形分类
	Jiao等人（2025）	深度学习	月球探测车	月球障碍检测

), ArticleFig(id=1322962957486916324, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=EN, label=Table 2, caption=Overview of embodied intelligence methods for deep space exploration, figureFileSmall=null, figureFileBig=null, tableContent=

任务类别	文献	方法类别	数据来源	主要任务
自主视觉 导航	Bajracharya等人（2008）	视觉姿态估计	火星探测车	火星车自主导航
	Yu等人（2021）	端到端深度学习	月球车仿真数据	月球车路径规划
	Hu等人（2021）	深度强化学习	月球车仿真环境	月球车导航与路径规划
	Daftry等人（2022）	机器学习	火星探测车真实与仿真数据	火星车安全路径规划
	Verma等人（2023）	导航系统集成	“毅力号”多传感器数据	火星车自动驾驶
	Gao等人（2024）	深度强化学习	月球探测车	月球车路径规划
	Santra等人（2025）	跨域强化学习	月球仿真数据	跨域导航策略迁移
自主任务 规划	徐瑞等人（2021）	局部自主规划	深空探测任务\系统数据	科学作业任务规划
	孙泽翼等人（2024）	多智能体强化学习	深空任务仿真数据	探测器自主任务规划
自主科学 操作	Estlin等人（2012）	规则驱动目标采样	MER火星车	自动科学目标识别与观测
	Francis等人（2017）	规则驱动目标采样	“好奇号”火星车	自动科学观测任务执行
	Stefanuk和Skonieczny（2022）	自编码器异常检测	火星探测车	行星表面未知目标
	苗仕林等人（2025）	深度学习	月球空间站数据	空间站行为识别

), ArticleFig(id=1322962957604356837, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962942555194040, language=CN, label=表2, caption=

深空探测具身智能方法研究归纳

, figureFileSmall=null, figureFileBig=null, tableContent=

任务类别	文献	方法类别	数据来源	主要任务
自主视觉 导航	Bajracharya等人（2008）	视觉姿态估计	火星探测车	火星车自主导航
	Yu等人（2021）	端到端深度学习	月球车仿真数据	月球车路径规划
	Hu等人（2021）	深度强化学习	月球车仿真环境	月球车导航与路径规划
	Daftry等人（2022）	机器学习	火星探测车真实与仿真数据	火星车安全路径规划
	Verma等人（2023）	导航系统集成	“毅力号”多传感器数据	火星车自动驾驶
	Gao等人（2024）	深度强化学习	月球探测车	月球车路径规划
	Santra等人（2025）	跨域强化学习	月球仿真数据	跨域导航策略迁移
自主任务 规划	徐瑞等人（2021）	局部自主规划	深空探测任务\系统数据	科学作业任务规划
	孙泽翼等人（2024）	多智能体强化学习	深空任务仿真数据	探测器自主任务规划
自主科学 操作	Estlin等人（2012）	规则驱动目标采样	MER火星车	自动科学目标识别与观测
	Francis等人（2017）	规则驱动目标采样	“好奇号”火星车	自动科学观测任务执行
	Stefanuk和Skonieczny（2022）	自编码器异常检测	火星探测车	行星表面未知目标
	苗仕林等人（2025）	深度学习	月球空间站数据	空间站行为识别

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