A Reinforcement Learning Approach for the Autonomous Assembly of In-Space Habitats and Infrastructures in Uncertain Environments
Explore the source record for details and available documents.
Joshua Moser↗
Engineering topics
Joshua Moser
Publications and source records attributed to Joshua Moser.
Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems
The construction of in-space facilities is a significant capability for the establishment of a long-term human presence in space. Autonomous robotic technologies are a critical tool to enabling the construction and maintenance of such permanent facilities. This paper evaluates the outfitting portion of the construction process, focusing on modeling required for robotic manipulation of cable-like objects, referred to as Deformable Linear Objects (DLOs). DLOs contain a high degree of variability, which makes accurate real-time autonomous operations a difficult task. Different modeling methods for DLOs such as discrete mass-spring systems and Cosserat elastic rod models range in problem complexity and accuracy, a trade-off that must be understood to fully realize autonomous cable routing technologies. This paper validates different DLO models through simulation and a hardware experiment, highlighting the size of the state space and accuracy of different approaches. The development of DLO manipulation models for outfitting enables an autonomous architecture for the construction of lunar surface systems.
Deformable Modeling↗
Performance Evaluation of a Cartesian Move Algorithm for the LSMS Family of Cable-Driven Cranes
The Lightweight Surface Manipulation System, or LSMS, is a family of cable-actuated cranes, designed for surface operations on the Moon and Mars. Its structural design focuses on three fundamental traits for space environments: a high payload-to-mass ratio to reduce launch costs, compactness for space travel in reduced cargo bays, and self-erecting capabilities for operations before astronauts arrive. The resulting design confers nonlinear and switched dynamics to the LSMS, making these cranes an interesting challenge for autonomous and teleoperated applications. One of the protocols required for these applications is a Cartesian move algorithm. This algorithm is responsible for placing the end effector of the crane at a desired location, specified in Cartesian coordinates. This paper evaluates the performance of a Cartesian move algorithm developed for the LSMS at the NASA Langley Research Center. To this end, several payload manipulation tasks were performed with LSMS-L35, the smallest version within the LSMS family. The evaluated algorithm can place the end effector with accuracy, as shown in the hardware and simulation tests.
cable-actuated↗