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Procedure Execution and Projection System
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:15:38.000ZThere is a persistent pressure upon NASA crew members to achieve very high productivity during their missions. Significant challenges exist to maintaining manageable workload while the crew is performing their many and varied tasks allotted for each day while ensuring the crew maintain situation awareness. NASA crew members deal with a large amount of very high technology equipment and perform experiments and procedures that can be extremely long and complex. The solution will require the development of automated management technologies that will operate synergistically with the crew, automating tasks of varying complexity in a dynamic, flexible manner with representations of automation state that the crew is familiar and comfortable with. In this proposal, Cybernet proposes to leverage crew members' capabilities with the design of a distributed Procedure Execution and Projection (PEP) system that focuses on supporting automation of complex procedures while ensuring crew situational awareness and anticipating future problems. Our team will leverage the recent work on the Procedure Representation Language (PRL) and the flexible, distributed and hierarchical capabilities of holonic systems. PRL is an XML encoding of the vehicle/habitat procedures in a form that both crew and automation can use, and the PEP systems' intelligent holonic modules will support crew with a range of capabilities, including automation of procedures, projection of procedures to look for problems and determine courses of action to prevent or mitigate the problems, and make sure that the crew maintain situational awareness of the procedural state. The objectives of the Phase I project are to establish critical requirements for NASA vehicle and habitat crew automation and to design and implement a prototype of the PEP system to demonstrate approach viability.
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Approximate Cartesian Control for Robotic Tool Usage with Graceful Degradation Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:39.000ZMany of NASA's exploration scenarios include important roles for autonomous or partially autonomous robots. It is desirable for them to utilize human tools when possible, rather than needing to build custom tools for each robot. Control of robotic manipulators for tool usage generally requires a very precise Cartesian-space trajectory of the tool tip (e.g., moving a marker along the surface of a whiteboard or rotating a screwdriver about an axis). Well-known techniques exist for manipulator control in Cartesian space, most of which necessitate solving a series of Inverse Kinematics (IK) problems. Closed-form IK solvers work well for 7-degree-of-freedom (DOF) arms with rigid tool attachments, but cannot handle non-rigid tools that slip in the robot's hands. Numerical IK approaches are more generic and can handle non-rigid links to tools, but can be slow to converge. More importantly, if any joints fail or become limited in their range of motion, the robot arm essentially becomes 6-DOF or lower. IK solvers often fail in these lower DOF spaces because the configuration space becomes non-continuous and full of "holes". As a result, a 7-DOF robotic arm in space might be rendered largely useless if a single joint fails or even loses mobility until it can be serviced. TRACLabs proposes to investigate an alternative approach to traditional Cartesian control approaches, which rely on complex IK solvers that go from Cartesian space backwards to joint space. We propose to leverage cheap memory and modern processing speeds to instead perform simple computations that go from joint space forwards to Cartesian space. Such techniques should overcome common changes to a manipulation chain caused by tool slippage or the grasping of a new tool and to overcome uncommon changes to a chain caused by joint failures, reduced joint mobility, changes in joint geometry or range of motion, or added joints.
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Auditory Presentation of H/OZ Critical Flight Data Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:08:03.000ZAutomation of a flight control system to perform functions normally attributed to humans is often not robust and limited to specific operating conditions and types of operation and a small set of fixed behaviors (i.e. modes). eSky has shown that metrics such as the time delay between a required control input from the crew and the actual input is sensitive to crew functional degradation through external distraction. We are currently developing strategies for using such crew state metrics to modulate the level of automation support provided to the flight crew. Dynamic reallocation of function between crew and automation can reduce the cognitive workload on the crew, enhance their ability to supervise the automation and help them intervene in the event of any failure or operation outside the desired operating conditions. eSky is exploring function reallocation in a collaborative flight control system (HFCS) design pioneered at NASA Langley. HFCS combines precise flight control automation with rudimentary intelligence that the flight crew can guide using relatively simple mechanisms. HFCS automation manages short-term control tasks (e.g. path following) while the crew is required to direct every significant trajectory change using flight controls rather than an FMS interface to keep them engaged in conduct of the flight. The automation communicates intentions to the pilot through visual and haptic (tactile) feedback; the crew communicates intentions to the automation through conventional controls. The HFCS user interface is primarily visual and tactile with limited auditory elements, mainly limited to a few alerts and warnings. eSky proposes to establish the auditory channel as a key element in providing flight dynamic information and cueing of required crew in puts in addition to envelope protection warnings. These new interface elements will be integrated into eSky's evolving strategies for functionality reallocation of between automation and crew.
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ED Total Private Covered Employment
internal.open.piercecountywa.gov | Last Updated 2024-04-17T18:09:50.000ZTotal private-sector employment for jobs covered by Unemployment Insurance as reported by employers through the Quarterly Census of Employment and Wages (QCEW).
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PHOENIX MARS ROBOTIC ARM CAMERA 5 XYZ OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:09:16.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains xyz data from the Robotic Arm Camera (RAC).
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Administrative Law Judge Opinions issued by the Office of the Secretary of Transportation
datahub.transportation.gov | Last Updated 2018-12-19T00:13:37.000ZAdministrative Law Judge Opinions from the Office of the Secretary of Transportation may include, but are not limited to, Aviation Safety Civil Penalty Decisions, Hazardous Materials Safety Civil Penalty Decisions, Motor Carrier Safety Civil Penalty Decisions, Airport-Airline Fees/Rates and Charges Decisions, Aviation Economic Violation Enforcement Proceedings, Aviation Economic Orders and Decisions, Airline Prices/Routes/Services Preemption Decisions, Aviation Enforcement Consent Orders, and Aviation Economic Decisions
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Administrative Law Judge Opinions issued by the Office of the Secretary of Transportation -
datahub.transportation.gov | Last Updated 2018-12-19T00:13:35.000ZAdministrative Law Judge Opinions from the Office of the Secretary of Transportation may include, but are not limited to, Aviation Safety Civil Penalty Decisions, Hazardous Materials Safety Civil Penalty Decisions, Motor Carrier Safety Civil Penalty Decisions, Airport-Airline Fees/Rates and Charges Decisions, Aviation Economic Violation Enforcement Proceedings, Aviation Economic Orders and Decisions, Airline Prices/Routes/Services Preemption Decisions, Aviation Enforcement Consent Orders, and Aviation Economic Decisions
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PHOENIX MARS ROBOTIC ARM CAMERA 5 NORMAL OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:52:37.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains normal data from the Robotic Arm Camera (RAC).
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Report Card Enrollment 2016-17 School Year
data.wa.gov | Last Updated 2023-12-22T20:52:25.000ZThis file includes Report Card enrollment data from 2016-17 school year. Data is disaggregated by school, district, and the state level and includes counts of students by the following groups: grade level, gender, race/ethnicity, and student programs and special characteristics. Please review the notes below for more information.
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ICI - Accommodation and Food Services Garbage Composition - Proper Disposal Location
data.calgary.ca | Last Updated 2023-02-01T15:39:37.000ZThis chart shows weight per cent composition grouped by proper disposal location. This dataset is for garbage bin waste from the Industrial, Commercial, and Institutional (ICI) sector. The North American Industry Classification System (NAICS) is used to categorize ICI businesses and organizations into sub-sectors for ease of data collection and reporting. The NAICS sub-sectors included in this study are: Accommodation and Food Services (NAICS code 72), Retail Trade (44-45), Manufacturing (31-33), Health Care and Social Assistance (62), and Public Administration (91). All businesses and organizations included in the study were customers of The City’s Commercial Collections service, except for one privately-serviced customer in the Accommodation and Food Services sub-sector that was added to provide better sector representation. A total of 115 samples are included in the dataset: 30 in Accommodation and Food Services, 35 in Retail Trade, 17 in Manufacturing, 25 in Health Care and Social Assistance, and 8 in Public Administration. The weight per cent for each sub-sector is the pooled average of samples collected in the four seasons of 2019. Waste Composition studies are periodically conducted by Waste and Recycling Services to help assess the performance of diversion and education programs and inform improvements and new program design.