Search NASA⌕ Search

Engineering topics

Erik E Komendera

Publications and source records attributed to Erik E Komendera.

Persistent Assets in Zero-G and on Planetary Surfaces: Enabled by Modular Technology and Robotic Operations

Space operations are on the cusp of a revolutionary new operational paradigm that leverages modular systems and recurring robotic visits to “Persistent Assets” enabling asset maintenance, repair, and enhancement. A “Persistent Asset” is defined here as any zero-g or planetary surface system that benefits from in-space assembly (ISA) or multiple visits for servicing, repairs, and upgrades. This term is an extension of the term “Persistent Platform” used by Ms. Pam Melroy at the Defense Advanced Research Projects Agency to describe the vision of the Agency for a geosynchronous Earth orbiting platform. In this paper, the term “Persistent Asset” is introduced to encompass not only zero-g systems; such as telecommunication platforms, Earth observing science platform, Department of Defense platforms, and scientific telescope systems, but also planetary surface systems that support missions such as human outposts, science stations, and in-situ resource utilization systems. In contrast to the current state of the art, where space systems are typically launched as a single unit and operated without any further physical intervention after launch; future systems will be maintained, enhanced and reconfigured in-situ as new technology becomes available or mission needs change. Visits to the persistent asset can be regularly scheduled or dictated by funding constraints enabling a pay-as-you-go approach, which is largely independent of time-constraints and able to exploit launches of opportunity. In this paper, historical in-space assembly activities which relied heavily on astronaut extra-vehicular activity will be reviewed as well as early robotic assembly activities. These approaches will be contrasted with emerging modular approaches supporting realization of a new Persistent Asset operational paradigm. The paper will define attributes of the Persistent Asset paradigm and illustrate advantages by applying the paradigm to two relevant applications: 1) a large space telescope backing structure and 2) the backbone structure for a solar electric transport vehicle. Finally, recently developed unique Persistent Asset elements (modules, and interface approaches) will be described.

William R Doggett↗

Robotic Software Architecture for in-Space Outfitting Operations

Space exploration is expanding into longer missions, larger payloads, and more complex operations. To make these larger scale missions a reality, it is necessary to perform assembly, construction, and maintenance tasks via a robotic workforce in addition to crewed operations. While there has been significant research into in-space assembly and manufacturing, it is primarily focused on rigid structural elements, such as ISRU printing or truss construction. Outfitting tasks, such as cable routing, are a critical step to a fully operational in-space facility. This paper seeks to provide a reduced order state model and an optimized combination of state-of-the-art robotics algorithms applied to a cable routing scenario. Simulation results are expected to advance approaches to online autonomous robotic manipulation of non-rigid elements.

Amy M Quartaro↗

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

This paper presents structural models of deformable linear objects (DLOs). DLOs are a subclass of deformable objects that encompasses common outfitting elements such as cables and ropes. Models are validated through hardware experiments, and integration in a robotic autonomy architecture for space environments is discussed. A persistent human presence on the lunar surface is one of the next major milestones in space exploration. This requires the development of robust extraplanetary construction technologies including structures and materials modeling and robotic systems. Previous robotic construction technology development has primarily focused on structural assembly, with significantly less focus on robotically performed outfitting tasks to instantiate subsystems providing power, data, life support, etc. These tasks involve manipulation of highly flexible elements, which are difficult to model, such as cable harnesses, ropes, and hoses. Robotic manipulation of DLOs, especially cable harnesses, is an active area of research as cable harnesses are essential for providing power and data to space assets. DLO models that can be used for robot manipulator trajectory generation are necessary for autonomous operation of lunar infrastructure. There are many proposed methods for modeling DLOs, and they primarily fall into three types: 1) discrete model-based, 2) continuum model-based, and 3) Neural Network-based. These types each have pros and cons, and the tradeoff between model accuracy and computational speed informs which type should be used. An understanding of this trade-off is imperative for real-time control of autonomous systems. High computational requirements reduce the speed of the model, making real-time control difficult, while accuracy is critical to preventing collisions. Discrete models, such as a mass-spring multibody representation, require relatively few calculations, and accuracy is directly tied to the step size of the discretization. Continuum models, such as a B-spline representation or a Cosserat rod model (a mix of continuous and discrete), are more informed of the structural properties of the cable and are much more accurate than a rigid body mass-spring model, but at significant computational cost. A Neural Network approach can provide an online solution with very few computational steps, but properly generating training data can be difficult and validation for an in-space application is not trivial. This paper explores the trade-off between different modeling approaches and compares accuracy and computational speed/complexity of the three types mentioned above. Model accuracy is evaluated using a cable in a static configuration. True cable shape is obtained using a depth camera for RGB images and point-cloud segmentation. The purpose of this experiment is to evaluate the trade-offs of different approaches to the DLO modeling problem. Understanding the tradeoffs between different cable modeling techniques paves the way for developing robotic control and planning architectures necessary for real-time manipulation of DLOs for lunar infrastructure outfitting. Real-time control is required for robotic systems to be able to actively manipulate a cable in a harsh environment where model and sensor errors compound, and environmental conditions can cause significant disturbances. Cable routing must be performed in areas with high density of objects/obstacles: through truss structures, near solar panels or mirror arrays, next to bundles of electrical equipment. Understanding the best way to plan and manipulate a cable without disrupting the environment or damaging the cable is imperative to robotic outfitting operations on the lunar surface.

Amy M Quartaro↗

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.

Amy M Quartaro↗