Organizations: Space Robotics Lab. (SRL) in Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan · Department of Electrical Engineering, Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan
Long-term human habitation and in-situ development on the Moon open a new era of space utilization. In this context, robots are a key technology for facilitating the construction of future human outposts. Toward the deployment and establishment of human habitation modules on the lunar surface, we propose a combined system consisting of inflatable modules and a modular, reconfigurable robotic system. This paper presents a report demonstrating various robot-assisted task executions using real hardware, namely the modular and reconfigurable robot MoonBot and the inflatable module HIDAS, to enhance the reliability of their deployment and maintenance. The demonstrated tasks include robotic inspection during inflation, module position alignment, final safety locking, and three-dimensional mapping for post-deployment maintenance. All demonstrations were conducted either in a laboratory environment or at a lunar analogue test site. Finally, lessons learned are discussed to provide essential insights for this robotic application to future lunar habitation.
Figures & tables
Fig. 1: MoonBot, a modular and reconfigurable robot (left), assisting in the deployment of inflatable module HIDAS (right). For the Earth-gravity demonstration, a half-scale inflatable module (diameter: 2 m) was used. The robots monitor the inflation status using a hand-mounted camera, assist in positioning the module by applying external forces, and subsequently insert rolling-proof stoppers to prevent unintended movement.
Fig. 2: Conceptual rendering illustrating the assumed scenario of robot-assisted construction of a human habitation base.
Fig. 3: Concept of the robot teleoperation framework based on shared autonomy.
Fig. 4: Robot teleoperation user interface. Top left: Robot view monitor, top right: joint states visualizer, bottom: telemetry data monitor (active modules, battery life, etc.).
Metric
Take 1
Take 2
Take 3
Mean ± s.d.
Δd [mm]
3.72
4.52
5.29
4.51±0.64
Δθ [deg]
0.55
0.53
0.69
0.59±0.07
TABLE I: Terminal alignment residuals from repeated trials.
Fig. 6: Initial inspection of the inflatable module by the robot: (a) Vision-based inspection during inflation and (b) Contact-based inspection after inflation.
Class
Precision
Recall
mAP@50
mAP@50:95
HIDAS
0.995
0.991
0.995
0.988
Anomaly cell
0.765
0.779
0.793
0.551
All
0.880
0.885
0.894
0.770
TABLE II: Cell-inspection model (YOLOv11n-seg) performance on the held-out validation split. “All” is the unweighted mean over the two classes.
Fig. 7: Well-positioning of the inflated module by two mobile robots, assuming a scenario in which the module is docked to another module. (a) The lateral position was adjusted by applying rolling forces from the left and right sides. Subsequently, (b) the longitudinal position was aligned by pushing the module cooperatively using the two robots.
Fig. 8: Demonstration of inflatable module deployment assistance by MoonBot. Once the inflatable module had been fully deployed and properly positioned, the robot placed stopper objects to further enhance stability (top: pick-up, bottom: insertion).
Fig. 9: Three-dimensional reconstruction of the inflatable modules after deployment. Multiple MoonBots scanned the HIDAS modules using LiDAR sensors mounted on top of the wheel bases (top images) to generate point cloud data (bottom graphics). The complete geometry of HIDAS was reconstructed by fusing multiple scans; the magenta and cyan lines indicate the robot traveling trajectories estimated during two different scanning runs.
Modular reconfigurable robotic systems provide a scalable solution for cooperative surface operations in future lunar missions. However, cooperative cargo transportation remains challenging due to morphology-dependent topology changes, strong payload-induced coupling, long-horizon decision making, and safety constraints. This paper proposes a phase-decomposed reinforcement learning framework for cooperative cargo transport with distributed robotic units. The task is decomposed into lifting, transportation, and placement, each optimized with a dedicated joint-state policy capturing inter-agent coupling. Centralized training promotes stable convergence, while deployment uses onboard proprioception for control and OptiTrack motion capture for ground-truth evaluation and post-processed metrics. A deterministic phase controller expressed in Markov state representation regulates transitions between stages, and a failure-sensitive synchronization mechanism ensures coordinated progression and safety-aware halting during real-world execution. The framework is evaluated in simulation and through controlled field experiments at a JAXA space exploration test facility. Results demonstrate reliable cooperative transport across all stages in both simulation and hardware experiments.
Ashutosh Mishra, Elian Neppel, Shreya Santra +4
Space Robotics Lab. (SRL), Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980–8579, Japan · École Centrale de Lille, France · Institut Teknologi Bandung, Indonesia
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
Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.
David Oberacker, Julia Richter, Philip Arm +10
FZI Forschungszentrum für Informatik, Karlsruhe, Germany · Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany · Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland +1