Mining beyond Earth with Space Robots: Exploration, Sampling, and Extraction
Authors: Dong Li, Dujun Nie, Xiaotong Zhang, Ruilin Wang, Yuchen Li, Chang Ge, Chao Xiong, Kaichang Di, Andreas Nüchter, Levente Kovács, Qingquan Li, Shirong Ge, Fei-Yue Wang, Long Chen
Organizations: Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China · OpenSpace Lab, China · School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany · University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary · School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China · School of Earth and Space Sciences, Wuhan University, Wuhan, 430072, China · State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China · Faculty of Mathematics and Computer Science, University of Würzburg, Würzburg, 97074, Germany · School of Architecture & Urban Planning, Shenzhen University, Shenzhen, 518060, China · School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China · Institude of Research & Development, WAYTOUS, Beijing, 100190, China
Space resource acquisition and utilization, commonly referred to as Space Mining, represent critical pathways for enabling sustained human exploration and unlocking commercial opportunities in space. These resources mainly include helium-3, water, mineral resources on the Moon and Mars, and abundant mineral deposits on asteroids. Due to the harsh conditions of space, communication delays, and high launch costs, the development of autonomous robotic systems is critical to achieving efficient, cost-effective space mining. This paper provides a comprehensive overview of space mining robotics and associated technologies. First, we review the background of space mining, including international policies, commercial entities, and recent advancements. We define a systematic six-stage architecture for space mining: Exploration is initiated by (1) remote sensing for target identification and (2) precise in situ robotic detection; Sampling progresses from (3) single-robot small-scale sampling to (4) multi-robot large-scale excavation; and Extraction integrates (5) autonomous resource extraction and (6) final integration into in situ construction or terrestrial transport. Additionally, we review and curate existing resources for space mining research, including real-world mission data, terrestrial analog datasets, and high-fidelity simulation environments. Finally, we identify critical open challenges in autonomous space mining and delineate a strategic research roadmap to bridge current technological gaps, fostering the transition toward a sustainable off-world economy. To track ongoing developments in space mining, we maintain an updated project page: https://github.com/OpenSpace-Lab/Space-Mining-with-Robotics-List.
Figures & tables
Figure 1: Conceptual Overview of Space Mining. Schematic of the multi-stage process for extraterrestrial resource extraction and utilization, from prospecting and excavation, through transport to processing facilities or depots, to in-situ or orbital refining of volatiles, metals, and regolith, culminating in applications for propellant production, life support, construction, or return to Earth. (Image credit: Original work by the authors.)
Parameter
Moon
Mars
Near-Earth Asteroid
Temperature Range
− 190°C – 127°C
− 143°C – 35°C
∼− 270°C
Surface Gravity
1.625 m/s² (1/6 g )
3.71 m/s² (0.38 g )
Microgravity ( 10−6 to 10−3 m/s²)
Day-Night Cycle
∼ 29 Earth days
24 h 39 min
N/A (rotational period varies)
Atmosphere
None
Thin
None
Communication Delay
∼ 1.3 s (one-way)
4–24 min (one-way)
10 s to ∼ 20 min (varies by orbit)
Key Resources
3 He, water ice, rare earths
Water (ice/polar), metals (in ores)
Metals (Ni-Fe), volatiles
Table 1: Comparative environmental and operational conditions for lunar, Martian, and near-Earth asteroid (NEA) missions.
Figure 2: Timeline of Space Exploration and Mining Advancements. Chronological overview highlighting major milestones in space exploration, key aerospace developments, and evolving international policies on space resource development.
Table 3: Inventory of Deployed Extraterrestrial Robotic Explorers. This survey catalogs mobile systems successfully operated on the Moon, Mars, and small bodies.
Figure 3: Trailblazers of Planetary Surface Exploration. Montage of rovers and specialized robotic systems that have successfully operated on extraterrestrial surfaces. Lunar rovers: Lunokhod 1, Lunokhod 2, Yutu, Yutu-2, Pragyan. Mars rovers: Sojourner, Spirit, Opportunity, Curiosity, Perseverance, Zhurong. Additional pioneering platforms: Jinchan (lunar sample-return manipulator), Ingenuity (Mars helicopter), and MASCOT (asteroid hopper on Ryugu). These systems represent key milestones in autonomous robotic exploration of the Moon, Mars, and small bodies.
Robot
Affiliation
Destination
Objective
VIPER Rover
USA
Lunar
Map the location of water ice and other potential resources.
ISRU Pilot Excavator (IPEx)
USA
Lunar
Excavation and transport of lunar regolith for ISRU.
ATHLETE Hexapod
USA
Lunar
Heavy-duty cargo transport across lunar terrain.
Quadruped Hopping Robot
China
Lunar
Designed for low-gravity hopping or jet-assisted leaps.
Crewed Lunar Rover
China
Lunar
Pressurized crewed rover; currently in preliminary prototype development.
Sample Fetch Rover (SFR)
European
Mars
Retrieval of sample tubes cached by NASA’s Perseverance rover.
Table 4: Selected Robotic Systems for Lunar and Martian Exploration and Resource Utilization (2026–2030).
Table 5: Space Mining versus Terrestrial Open-Pit Mining
Figure 4: The Hierarchical Six-stage Framework for Autonomous Robotic Space Mining. Schematic of the end-to-end operational lifecycle for extraterrestrial resource acquisition: (a) Prospecting for regional site selection; (b) Exploration via in situ robotic sensing; (c) Sampling for material validation; (d) Excavation for large-scale retrieval; (e) Extraction of refined volatiles or minerals; and (f) Utilization through in situ manufacturing or terrestrial transport.
Dataset
Year
Env.
Type
Platforms
Sensors
GT
Tasks
Key Features
DIND [ 37 ]
2012
Mars
Real
Wheeled
RGB, Sun sensor, Inclinometer
DGPS
Localize.
1.5 km traverse
Canadian 3D Mapping [ 119 ]
2013
Mars
Real
Wheeled
Stereo, Laser, IMU
DGPS
SLAM
Heterogeneous platforms
Katwijk Beach [ 52 ]
2018
Mars
Real
Wheeled
Stereo, ToF, LiDAR
Aerial
SLAM
Rock-strewn beach
LRU [ 122 ]
2018
Mars
Real
Wheeled
Stereo, IMU, Odom
DGPS
Localize.
Rugged volcanic terrain
Labelmar [ 104 ]
2019
Mars
Real
Wheeled
RGB
-
Segment.
Authentic Martian data
Lunar Landscape [ 91 ]
2019
Lunar
Synth.
-
RGB
-
Segment.
Synth. from real data
Table 6: A Comprehensive List of Datasets for Extraterrestrial Scenarios.
Simulator
Years
Scenes
Renderer
Robots
Tasks
RP Simulator [ 2 ] *
2019
Lunar
ROS Gazebo
Wheeled
Navigation, Localization
MarsSim [ 43 ]
2020
Mars
ROS Gazebo
Wheeled
Perception, Localization
Space Robotics Challenge*
2021
Lunar
ROS Gazebo
Wheeled
Lunar Mining, Navigation, etc.
MarsSim [ 140 ] *
2022
Mars
ROS Gazebo
Wheeled
Perception, Navigation
LunarSim [ 92 ]
2023
Lunar
ROS Gazebo
Wheeled
Segmentation, Localization, Navigation
Lunar Autonomy Challenge*
2024
Lunar
CARLA
Wheeled
Perception, Mapping, Localization
Table 7: Overview of Robotic Simulators for Extraterrestrial Environments. Asterisks (*) denote proprietary or closed-source systems.
Figure 5: Closed-loop Data Cycle for Space Robotics. From simulation testing to terrestrial emulation validation, on to real-world deployment, with bidirectional enhancement of both simulation environments and terrestrial emulators.
Lunar helium-3 is a highly valuable strategic resource, pivotal to the advancement of both deep-space exploration and space mining. Existing lunar helium-3 exploration methodologies rely primarily on indirect measurements via remote sensing, which are often characterized by limited precision, low reliability, and insufficient spatial resolution. In this paper, we introduce He3-Seeker, an active robotic exploration method for helium-3 distribution mapping. First, we provide a formal definition of the active helium-3 exploration problem. Subsequently, we developed the He3-Seeker framework, which is conceptually based on multi-point drilling, sampling, and in situ analysis. In particular, we use robotic information planning (RIP) to guide autonomous robot navigation and active sensing. Additionally, to thoroughly evaluate the proposed algorithm, we introduce a reliable method for generating reference data of lunar helium-3 distribution based on low-resolution orbital remote sensing measurements. Simulation experiments verify that He3-Seeker achieves both rapid and high-fidelity mapping of helium-3 distribution, providing a reliable solution for resource exploration tasks. Our code and simulation environment will be publicly accessible at https://github.com/OpenSpace-Lab/He3-Seeker.
Dong Li, Yujie Zheng, Chengdeng Cao +4
Institute of Automation, Chinese Academy of Sciences, China, and also with the Faculty of Innovation Engineering, Macau University of Science and Technology, Macau · OpenSpace Lab, China · College of Artificial Intelligence, China University of Petroleum (Beijing) +5
In recent years, the Moon has emerged as an unparalleled extraterrestrial testbed for advancing cuttingedge technological and scientific research critical to enabling sustained human presence on its surface and supporting future interplanetary exploration. This study identifies and investigates two pivotal research domains with substantial transformative potential for accelerating humanity interplanetary aspirations. First is Lunar Science Exploration with Artificial Intelligence and Space Robotics which focusses on AI and Space Robotics redefining the frontiers of space exploration. Second being Space Robotics aid in manned spaceflight to the Moon serving as critical assets for pre-deployment infrastructure development, In-Situ Resource Utilization, surface operations support, and astronaut safety assurance. By integrating autonomy, machine learning, and realtime sensor fusion, space robotics not only augment human capabilities but also serve as force multipliers in achieving sustainable lunar exploration, paving the way for future crewed missions to Mars and beyond.
Arsalan Muhammad, Yue Wang, Hai Huang +1
School of Astronautics, Beihang University Xueyuan Road, 102206, Beijing, China · Regional Centre for Space Science and Technology Education in Asia & the Pacific (Affiliated to the United Nations) (RCSSTEAP), Hangzhou International Innovation Institute of Beihang University, China
Space robots are increasingly expected to perform long-duration, contact-rich, and multi-stage operations with limited human intervention. Recent advances in artificial intelligence (AI), robot learning, and embodied foundation models provide new opportunities to improve the autonomy and adaptability of such systems, but their transfer to space is constrained by scarce mission data, space-specific dynamics and sensing conditions, limited onboard resources, and stringent safety requirements. This article reviews artificial intelligence-enabled space robot operations (AI-SRO) from a capability-building perspective. We first summarize representative operational scenarios, autonomy trends, and space-specific constraints. We then establish a three-layer technical framework comprising capability foundations, capability formation, and capability deployment/evolution. Within this framework, we review simulation environments, datasets and benchmarks; task and environment understanding, state perception, decision-making and planning, and action execution; and onboard deployment, ground-to-space adaptation, continual learning, and capability transfer. Finally, we propose key research directions toward trustworthy simulation and data, open-world multimodal cognition, long-horizon safe decision-making, physically constrained policy learning, and space computing infrastructures.
Zeyuan Huang, Gang Chen, Zixuan Hao +7
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, China