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robotics automation and control
Circumferential Scissor Spring Enhances Precision in Hand Controllers
The traditional scissor spring design for hand controllers has been improved upon with a circumferential spring controller mechanism that facilitates easy customization, enhanced durability, and optimum controller feedback. These advantages are partially facilitated by locating the spring to the outside of the mechanism which allows for easier spring replacement to adjust the deflection force or for maintenance. The new mechanism is comprised of two rounded blades, or cams, that pivot forward and back under operation and meet to form a circle. An expansion spring is looped around the blade perimeter and resides in a channel, providing the restoring force that returns the control stick to a neutral position. Due to the use of a longer circumferential spring, the proportion of spring expansion is smaller for a given distance of deflection, so the forces associated with the deflection remain on a more linear portion of the force deflection curve. The Circumferential Scissor Spring for Controllers is at technology readiness level (TRL) 8 (actual system completed and flight qualified through test and demonstration) and is available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.
information technology and software
https://images.nasa.gov/details-iss062e000422
Computer Vision Lends Precision to Robotic Grappling
The goal of this computer vision software is to take the guesswork out of grapple operations aboard the ISS by providing a robotic arm operator with real-time pose estimation of the grapple fixtures relative to the robotic arms end effectors. To solve this Perspective-n-Point challenge, the software uses computer vision algorithms to determine alignment solutions between the position of the camera eyepoint with the position of the end effector as the borescope camera sensors are typically located several centimeters from their respective end effector grasping mechanisms. The software includes a machine learning component that uses a trained regional Convolutional Neural Network (r-CNN) to provide the capability to analyze a live camera feed to determine ISS fixture targets a robotic arm operator can interact with on orbit. This feature is intended to increase the grappling operational range of ISSs main robotic arm from a previous maximum of 0.5 meters for certain target types, to greater than 1.5 meters, while significantly reducing computation times for grasping operations. Industrial automation and robotics applications that rely on computer vision solutions may find value in this softwares capabilities. A wide range of emerging terrestrial robotic applications, outside of controlled environments, may also find value in the dynamic object recognition and state determination capabilities of this technology as successfully demonstrated by NASA on-orbit. This computer vision software is at a technology readiness level (TRL) 6, (system/sub-system model or prototype demonstration in an operational environment.), and the software is now available to license. Please note that NASA does not manufacture products itself for commercial sale.
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