Context-aware Mission Control for Astronaut-Robot Collaboration

Space robot assistants are envisaged as semi-autonomous co-workers deployed to lighten the workload of astronauts in cumbersome and dangerous situations. In view of this, this work considers the prospects on the technology requirements for future space robot operations, by presenting a novel mission control concept for close astronaut-robot collaboration. A decentralized approach is proposed, in which an astronaut is put in charge of commanding the robot, and a mission control center on Earth maintains a list of authorized robot actions by applying symbolic, geometric, and context-specific filters. The concept is applied to actual space robot operations within the METERON SUPVIS Justin experiment. In particular, it is shown how the concept is utilized to guide an astronaut aboard the ISS in its mission to survey and maintain a solar panel farm in a simulated Mars environment

Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays. As one of the best alternatives to human-level intelligence, Reinforcement Learning (RL) may offer a solution to cope with these issues. This paper proposes to integrate RL with the Model Mediated Teleoperation (MMT) concept. The teleoperator interacts with a simulated virtual environment, which provides instant feedback. Whereas feedback from the real environment is delayed, feedback from the model is instantaneous, leading to high transparency. The MMT is realized in combination with an intelligent system with two layers. The first layer utilizes Dynamic Movement Primitives (DMP) which accounts for certain changes in the avatar environment. And, the second layer addresses the problems caused by uncertainty in the model using RL methods. Augmented reality was also provided to fuse the avatar device and virtual environment models for the teleoperator. Implemented on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods are able to find different solutions when changes are applied to the object position after the demonstration. The results also show DMPs to be effective at adapting to new conditions where there is no uncertainty involved

Building on the Moon Using Additive Manufacturing: Discussion of Robotic Approaches

We present two concepts for building structures on the moon. The first involves an all-purpose robot with exchangeable tools, able to excavate regolith, produce slurry and print a structure. The second involves a team of robots: one excavator, one sintering using sunlight, and another gathering the sintered bricks and building a structure. ESA's Space Resources Strategy sets the challenge of achieving human presence at the Moon, sustained by local resources, by 2040, which includes in-situ manufacturing and construction. We take a realistic look at the feasibility of these concepts and their component technologies, and their challenges with respect to the state of the art and expected technological readiness in the next 2-5 years

Prediction of Mechanical Properties of Polymers With Various Force Fields

The effect of force field type on the predicted elastic properties of a polyimide is examined using a multiscale modeling technique. Molecular Dynamics simulations are used to predict the atomic structure and elastic properties of the polymer by subjecting a representative volume element of the material to bulk and shear finite deformations. The elastic properties of the polyimide are determined using three force fields: AMBER, OPLS-AA, and MM3. The predicted values of Young s modulus and shear modulus of the polyimide are compared with experimental values. The results indicate that the mechanical properties of the polyimide predicted with the OPLS-AA force field most closely matched those from experiment. The results also indicate that while the complexity of the force field does not have a significant effect on the accuracy of predicted properties, small differences in the force constants and the functional form of individual terms in the force fields determine the accuracy of the force field in predicting the elastic properties of the polyimide

Nucleation and growth kinetics for intercalated islands during deposition on layered materials with isolated point-like surface defects

Theory and stochastic lattice-gas modeling is developed for the formation of intercalated metal islands in the gallery between the top layer and the underlying layer at the surface of layered materials. Our model for this process involves deposition of atoms, some fraction of which then enter the gallery through well-separated point-like defects in the top layer. Subsequently, these atoms diffuse within the subsurface gallery leading to nucleation and growth of intercalated islands nearby the defect point source. For the case of a single point defect, continuum diffusion equation analysis provides insight into the nucleation kinetics. However, complementary tailored lattice-gas modeling produces a more comprehensive and quantitative characterization. We analyze the large spread in nucleation times and positions relative to the defect for the first nucleated island. We also consider the formation of subsequent islands and the evolution of island growth shapes. The shapes reflect in part our natural adoption of a hexagonal close-packed island structure. Motivation and support for the model is provided by scanning tunneling microscopy observations of the formation of intercalated metal islands in highly-ordered pyrolytic graphite at higher temperatures

Flow instability of dope solution in hollow fiber spinning process for different flow channel length

The study demonstrated the effect of different flow channel length on the spinneret with respect to the ceramic hollow fiber membrane morphology. A smartly designed spinneret is utilized for the spinning process where the nozzle used can be change to different length via the use of adapters. Thus, allowing the effect of having different flow channel length to be investigated. Three spinneret adapters with different nozzle length were fabricated at 29 mm, 34 mm, and 39 mm. Ceramic hollow fiber membrane is produced using these configurations in the spinning process. Then, the micrographic cross sections of hollow fiber membranes is investigated using Scanning Electron Microscope (SEM) where it shows finger like, dense layer and damage structure morphology. Out of the samples investigated, hollow fiber membrane spun using 39 mm nozzle length produce the best concentricity. CFD simulation is initiated to study the flow behavior inside the flow channel to correlate with that of the experimental result attained

Biopolymer-based structuring of liquid oil into soft solids and oleogels using water-continuous emulsions as templates

Physical trapping of a hydrophobic liquid oil in a matrix of water-soluble biopolymers was achieved using a facile two-step process by first formulating a surfactant-free oil-in-water emulsion stabilized by biopolymers (a protein and a polysaccharide) followed by complete removal of the water phase (by either high- or low-temperature drying of the emulsion) resulting in structured solid systems containing a high concentration of liquid oil (above 97 wt %). The microstructure of these systems was revealed by confocal and cryo-scanning electron microscopy, and the effect of biopolymer concentrations on the consistency of emulsions as well as the dried product was evaluated using a combination of small-amplitude oscillatory shear rheometry and large deformation fracture studies. The oleogel prepared by shearing the dried product showed a high gel strength as well as a certain degree of thixotropic recovery even at high temperatures. Moreover, the reversibility of the process was demonstrated by shearing the dried product in the presence of water to obtain reconstituted emulsions with rheological properties comparable to those of the fresh emulsion

Exodex Adam—A Reconfigurable Dexterous Haptic User Interface for the Whole Hand

Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly “feel” the environment they work in, whether virtual or a remote site through an avatar. We introduce the DLR Exodex Adam, a reconfigurable, dexterous, whole-hand haptic input device. The device comprises multiple modular, three degrees of freedom (3-DOF) robotic fingers, whose placement on the device can be adjusted to optimize manipulability for different user hand sizes. Additionally, the device is mounted on a 7-DOF robot arm to increase the user’s workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the user’s fingertips, the thumb, and two points on the palm. Including the palm, as opposed to only the fingertips as is common in existing devices, enables accurate tracking of the whole hand without additional sensors such as a data glove or motion capture. By providing “whole-hand” interaction with omnidirectional force-feedback at the attachment points, we enable the user to experience the environment with the complete hand instead of only the fingertips, thus realizing deeper immersion. Interaction using Exodex Adam can range from palpation of objects and surfaces to manipulation using both power and precision grasps, all while receiving haptic feedback. This article details the concept and design of the Exodex Adam, as well as use cases where it is deployed with different command modalities. These include mixed-media interaction in a virtual environment, gesture-based telemanipulation, and robotic hand–arm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments

Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

elerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays. As one of the best alternatives to human-level intelligence, Reinforcement Learning (RL) may offer a solution to cope with these issues. This paper proposes to integrate RL with the Model Mediated Teleoperation (MMT) concept. The teleoperator interacts with a simulated virtual environment, which provides instant feedback. Whereas feedback from the real environment is delayed, feedback from the model is instantaneous, leading to high transparency. The MMT is realized in combination with an intelligent system with two layers. The first layer utilizes Dynamic Movement Primitives (DMP) which accounts for certain changes in the avatar environment. And, the second layer addresses the problems caused by uncertainty in the model using RL methods. Augmented reality was also provided to fuse the avatar device and virtual environment models for the teleoperator. Implemented on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods are able to find different solutions when changes are applied to the object position after the demonstration. The results also show DMPs to be effective at adapting to new conditions where there is no uncertainty involved