MoonBot: Modular and On-Demand Reconfigurable Robot Toward Moon Base Construction
- URL: http://arxiv.org/abs/2512.21853v1
- Date: Fri, 26 Dec 2025 04:22:28 GMT
- Title: MoonBot: Modular and On-Demand Reconfigurable Robot Toward Moon Base Construction
- Authors: Kentaro Uno, Elian Neppel, Gustavo H. Diaz, Ashutosh Mishra, Shamistan Karimov, A. Sejal Jain, Ayesha Habib, Pascal Pama, Hazal Gozbasi, Shreya Santra, Kazuya Yoshida,
- Abstract summary: We introduce the modular and on-demand reconfigurable robot (MoonBot)<n>MoonBot is a modular and reconfigurable robotic system engineered to maximize functionality while operating within the stringent mass constraints of lunar payloads.<n>This article details the design and development of MoonBot and presents a preliminary field demonstration.
- Score: 5.414960992449607
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The allure of lunar surface exploration and development has recently captured widespread global attention. Robots have proved to be indispensable for exploring uncharted terrains, uncovering and leveraging local resources, and facilitating the construction of future human habitats. In this article, we introduce the modular and on-demand reconfigurable robot (MoonBot), a modular and reconfigurable robotic system engineered to maximize functionality while operating within the stringent mass constraints of lunar payloads and adapting to varying environmental conditions and task requirements. This article details the design and development of MoonBot and presents a preliminary field demonstration that validates the proof of concept through the execution of milestone tasks simulating the establishment of lunar infrastructure. These tasks include essential civil engineering operations, infrastructural component transportation and deployment, and assistive operations with inflatable modules. Furthermore, we systematically summarize the lessons learned during testing, focusing on the connector design and providing valuable insights for the advancement of modular robotic systems in future lunar missions.
Related papers
- MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation [56.30931340537373]
MolmoSpaces is a fully open ecosystem to support benchmarking of robot policies.<n>MolmoSpaces consists of over 230k diverse indoor environments.<n>MolmoSpaces-Bench is a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects.
arXiv Detail & Related papers (2026-02-11T20:16:31Z) - Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
arXiv Detail & Related papers (2025-10-30T18:24:38Z) - Space Robotics Bench: Robot Learning Beyond Earth [16.948852537273655]
Space Robotics Bench is an open-source simulation framework for robot learning in space.<n>It integrates on-demand procedural generation with massively parallel simulation environments.<n>It includes a comprehensive suite of benchmark tasks that span a wide range of mission-relevant scenarios.
arXiv Detail & Related papers (2025-09-27T14:28:31Z) - An integrated process for design and control of lunar robotics using AI and simulation [0.48861336570452174]
We envision an integrated process for developing lunar construction equipment, where physical design and control are explored in parallel.<n>We describe a technical framework that supports this process.<n>It relies on OpenPLX, a readable/writable declarative language that links CAD-models and autonomous systems to real-time 3D simulations of contacting multibody dynamics, machine regolith interaction forces, and non-ideal sensors.
arXiv Detail & Related papers (2025-09-15T19:02:30Z) - Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
arXiv Detail & Related papers (2025-05-14T15:28:43Z) - Holistic Construction Automation with Modular Robots: From High-Level Task Specification to Execution [7.012962572096341]
In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices.<n>This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot.
arXiv Detail & Related papers (2024-12-30T11:11:13Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks.
As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments.
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
arXiv Detail & Related papers (2023-12-14T10:02:55Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Robotic Vision for Space Mining [32.2999577099258]
We show how machine learning-enabled vision could help alleviate the challenges posed by the lunar environment.
A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.
arXiv Detail & Related papers (2021-09-27T02:52:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.