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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A cable-driven additive manufacturing system includes an end effector configured for linear translation within a three-dimensional workspace, an aerial hoist suspending the end effector by at least one suspension cable, a plurality of base stations disposed below the aerial hoist, and control cables running from each of the base stations to the end effector.
SCALE 6.2 consolidated the multiple special-purpose graphical user interfaces (GUI) of SCALE 6.1 into a completely new and modern interface called FULCRUM. FULCRUM is a cross-platform GUI designed to create, edit, validate, and visualize SCALE input, output, and data. It is intended to be usable by both novice and experienced SCALE users with an unobtrusive design. This paper presents a summary of FULCRUM capabilities. Specifically, interactive input validation, context-aware auto-completion, navigation, interactive geometry visualization with mesh results overlay, job execution, data plotting, and highly configurable workspace layouts will be discussed. (authors)
Los Alamos National Laboratory (LANL) is the primary plutonium science and manufacturing facility in the United States supporting multiple national security programs. It is responsible for completing a high variety of small-batch manufacturing tasks requiring the handling and precision machining of Special Nuclear Materials (SNM). Given the myriad of tasks that must be completed in a limited workspace, LANL intends to utilize flexible, multi-use robotic work-cells capable of completing a variety of mission critical tasks in a single glovebox with no re-tooling needed between batch jobs. This approach would allow LANL more flexibility in its production schedule and to meet its future production and research objectives. In this paper, we present a system that integrates multiple sensors and other manufacturing peripherals into existing glovebox infrastructure. Currently, a technician can place an object in the work area where vision sensors and supervisory software identify the object and its pose and automatically determine which manufacturing task to complete. Pose uncertainty is addressed in real-time by the task planner, eliminating the need for fixtures. Grasps are validated using pressure sensors, payload sensors, and grasp quality metrics to ensure that objects are not dropped or damaged during manipulation. The robot, sensors, and peripherals are controlled using open-source software built utilizing ROS (Robot Operating System), which minimizes redevelopment and allows for re-use and extensibility. Established capabilities support vision, collision-free path planning, and more, minimizing development time. For this effort, particular attention was given to the integration of general peripheral devices (drill, chuck, etc.), which is critical to efficiently utilizing ROS in manufacturing environments. This integration is achieved with an in-development framework that allows for quick integration of peripherals, including I/O devices and motors. Contact tasks utilize the Generalized Contact Control Framework (GCCF) previously developed at UT Austin. GCCF allows application developers to easily define multiple contact control tasks for a single robot. This paper summarizes the key software components necessary to develop the multi-use work-cell including existing ROS components. UT-developed software for grasp validation, peripheral integration, and contact control is presented in more depth. Finally the glovebox system is demonstrated completing a set of tasks with no fixturing necessary. Primary tasks include surface preparation/ablation, drilling, press-fitting, and sorting, as well as supporting material transfer and inspection tasks. The glovebox work-cell reduces cost, operator dose, ergonomic issues, and time loss due to material transfer between gloveboxes that only support one task. If multiple glovebox work-cells are in use, they have the potential to eliminate bottlenecks and scale production capabilities beyond current limits. (authors)
The handling of high-consequence materials is a difficult task requiring safe object manipulation while avoiding the risk of contamination or spillage. Specific operations including opening containers, segregating the wastes, and repackaging are required to be safely executed inside contained spaces such as gloveboxes. However, the workers' ability and dexterity to manipulate objects through thick protective gloves are compromised in many aspects. The fixed position of the glovebox arm ports restricts the movements of the workers' arms, which also makes it hard to lift heavy objects. Further, the operational workspace inside the glovebox is restricted by the gloves' reachability. Safety of workers is the paramount concern in glovebox operations and the very reason for their existence. Sharp edges and tools increases the risk of glove punctures, which may expose the operator to chemicals and radiation and risks contamination in the vicinity outside the glovebox. The operators are also affected by ergonomic stressors due to prolonged and repetitive operations. To achieve a high degree of human safety, robotic solutions to handle high-consequence materials inside the glovebox are desirable, as they remove the operators from the hazards listed above. However, robots, in general, lack the degree of adaptability necessary for most high-consequence material handling tasks as these tasks are often unstructured or highly variable. Likewise, most human operators, while highly skilled in the tasks at hand (task expertise), lack the robot programming skills necessary to adapt the robot's motions to the variations (programming expertise). A way to bridge this divide is to make robot programming more intuitive to human operators. Since humans naturally teach one another through demonstration and learning, robotic 'programming by demonstration' paradigms have begun to appear to reduce the burden of robot reprogramming. Gesture-Based Programming attempts to achieve that by enabling a robot to observe the normal actions and affordances of a human performing a task to learn to map those onto the skills of the robot. In the end, this approach permits benefits from both the human and the robot: the human's adaptive decision making and the robot's resilience to high-consequence material. Robotic solutions assuringly provide protection for the operators, however their autonomy brings about other potential risks. Artificial Intelligence is not perfect; it is a black box, which can produce results, but the full extent of those results may not be known precisely. In self-driving cars, a hypothetical AI problem is the 'green man detector'. Since self-driving cars have presumably never seen a green man, how can we know it won't veer toward him the first time it sees a green man? Likewise, even though GbP provides an open method for safe task execution replicating a human, incorrect task inferences can lead to potential collisions or unintended operations. Thus, a hardware safety measure is proposed to provide reliable operation inside the glovebox, and true safe operations are achieved by mechanically limiting the operational range of each arm link. The operational range is computed and validated through exhaustive offline simulations, which can be streamlined by affordable computing power. Super Baxter is one such robot which incorporates these capabilities. It is a human-like, bimanual robot, being developed at the Collaborative Robotics Lab (CRL) in conjunction with the Dept. of Energy/Environmental Management, Rethink Robotics, Barrett Technology, and the NSF Center for Robots and Sensors for the Human Well-Being (RoSe-HUB). Super Baxter is envisioned to represent the next generation of collaborative robots that will be intrinsically human-safe, as well as exceptionally human-intuitive. In summary, Super Baxter will enable safe glovebox operations through low-shot Gesture based Programming incorporated with hardware safety measures. Two aspects of safety will be discussed in the paper. First, the safety of the operator achieved by allowing him to program Super Baxter remotely through Gesture based Programming. Second, the safety of the glovebox, achieved by incorporating hardware safety measures using joint limits. Further, the capability of Super Baxter on Gesture based Programming is demonstrated through typical object manipulation tasks in glovebox operations. (authors)
A DOE-sponsored study conducted by the Los Alamos National Laboratory reported that the incidence rate of repetitive stress injuries (RSI) during manipulation tasks in glovebox and hot cell workspaces increased from 22% after 1-2 hours of work per day to 50% after 3 hours of work per day due to fatigue, repetitive motion, and hyperextension. Robotics can be a viable solution to this issue as robotic manipulators and wearable robotic devices can provide effective assistance to alleviate the workload. In order to use the robotic devices, an initial study to detect the amount of fatigue experienced by the glovebox operators is essential. Hence, in the present research two objectives have been specified and investigated. These include the use of electromyography (EMG) sensors to precisely detect the fatigue in various hands, arm and shoulder muscles and; the use of dexterous robotic arm and hand combinations to effectively perform certain repetitive and monotonous tasks conducted inside an in-house glovebox constructed for the research. A UR5 robotic arm and an i-limb artificial hand have been used. Human tests included eight subjects and two customized glovebox tasks. Pilot scale test results indicate variation in the mean frequency which is an indication of the level of fatigue that occurred. (authors)
We examine the issue of distributed collaboration at the Jet Propulsion Laboratory. With the goals of faster, better, cheaper missions, an efficient, seamless collaboration capabilitity is critical to future JPL space exploration missions.
NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles. Some of these improvements will at the same time support the twin pillars of Open Science: transparency of methods and reproducibility of results. This video is a high level overview of the NLSP for existing and new users.