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A summary of findings of the Flight Elements Subpanel is given in viewgraph form. Topics covered include advanced avionics architecture; processors; integrated Global Positioning System and guidance, navigation and control; displays and controls; telemetry; sensors and instrumentation; fault detection and management; electrical power distribution and control; spacecraft power supplies; and in-flight crew training.
The development of the polarization lidar technique is reviewed, and the current capabilities and limitations of the technique for the cloud research are discussed. At present, polarization lidar is a key component of climate-research programs designed to characterize the properties of cirrus clouds and is an integral part of multiple remote-sensor studies of mixed-phase cloud systems such as winter mountain storms, making it possible to discriminate between cloud phases and to identify some particle types and orientations. Recent theoretical approaches involving ice crystal ray-tracing and cloud microphysical-model simulations are expected to increase the utility of the polarization lidar technique.
A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.
The Automated Rendezvous and Capture (AR&C) system was designed and tested at NASA's Marshall Space Flight Center (MSFC) to demonstrate technologies and mission strategies for automated rendezvous and docking of spacecraft in Earth orbit, The system incorporates some of the latest innovations in Global Positioning, System space navigation, laser sensor technologies and automated mission sequencing algorithms. The system's initial design and integration was completed in 1998 and has undergone testing at MSFC. This paper describes the major components of the AR&C system and presents results from the official system tests performed in MSFC's Flight Robotics Laboratory with digital simulations and hardware in the loop tests. The results show that the AR&C system can safely and reliably perform automated rendezvous and docking missions in the absence of system failures with 100 percent success. When system failures are included, the system uses its automated collision avoidance maneuver logic to recover in a safe manner. The primary objective of the AR&C project is to prove that by designing a safe and robust automated system, mission operations cost can be reduced by decreasing the personnel required for mission design, preflight planning and training required for crewed rendezvous and docking missions.
Health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVs) in order to reduce life cycle costs, to increase safety margins, and to improve mission reliability. NASA Ames is leading the effort to develop inspection and health management technologies for thermal protection systems. This paper summarizes a joint project between NASA Ames and industry partners to develop "wireless" devices that can be embedded in the thermal protection system to monitor temperature or other quantities of interest. These devices are sensors integrated with radio-frequency identification (RFID) microchips to enable non-contact communication of sensor data to an external reader that may be a hand-held scanner or a large portal. Both passive and active prototype devices have been developed. The passive device uses a thermal fuse to indicate the occurrence of excessive temperature. This device has a diameter under 0.13 cm. (suitable for placement in gaps between ceramic TPS tiles on an RLV) and can withstand 370 C for 15 minutes. The active device contains a small battery to provide power to a thermocouple for recording a temperature history during flight. The bulk of the device must be placed beneath the TPS for protection from high temperature, but the thermocouple can be placed in a hot location such as near the external surface.
A main design criterion for a robust Integrated Systems Health Management (ISHM) system is summed up best by the statement "No data is better than bad data". Traditional data acquisition systems are calibrated in a controlled environment and guaranteed to perform bounded by their tested conditions. To successfully design and implement a real world ISHM system, the data acquisition and signal conditioning needs to function in an uncontrolled environment. Development and testing focuses on a design with the ability to self check in order to extend calibration times, report internal faults and drifts and notify the overall system when the data acquisition is not performing as it should. All of this will be designed in a system that is flexible, requiring little redesign to be deployed on a wide variety of systems. Development progress and testing results will be reported.
A document discusses a silicon-on-insulator (SOI) complementary metal oxide/semiconductor (CMOS) integrated- circuit operational amplifier to be replicated and incorporated into sensor and actuator systems of Mars-explorer robots. This amplifier is designed to function at a supply potential less than or equal to 5.5 V, at any temperature from -180 to +120 C. The design is implemented on a commercial radiation-hard SOI CMOS process rated for a supply potential of less than or equal to 3.6 V and temperatures from -55 to +110 C. The design incorporates several innovations to achieve this, the main ones being the following: NMOS transistor channel lengths below 1 m are generally not used because research showed that this change could reduce the adverse effect of hot carrier injection on the lifetimes of transistors at low temperatures. To enable the amplifier to withstand the 5.5-V supply potential, a circuit topology including cascade devices, clamping devices, and dynamic voltage biasing was adopted so that no individual transistor would be exposed to more than 3.6 V. To minimize undesired variations in performance over the temperature range, the transistors in the amplifier are biased by circuitry that maintains a constant inversion coefficient over the temperature range.
Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools). Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.
Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools).Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.
Systems and methods for detecting biometrics using microwave radar sensor modules are disclosed. Integrated microwave sensor modules can include a transmitter unit configured to generate at least one continuous wave transmit signal based upon at least one frequency control signal, a receiver unit configured to utilize a cancellation path to cancel contributions to a return signal based upon at least one cancellation path control signal, and a microcontroller unit that includes a processor, a memory containing a microcontroller application, where the microcontroller application configures the processor to generate at least one frequency control signal to generate least one CW transmit signal having a plurality of frequencies, generate at least one cancellation path control signal to automatically adjust the cancellation path in real time, receive at least one demodulated signal, digitize the at least one demodulated signal, and update the at least one frequency control and cancellation path control signals.
Unmanned Aircraft Systems (UAS) need to be able to comply with the Code of Federal Regulations Part 91 ‘see and avoid’ requirement. Detect and avoid (DAA) is an essential enabler for integrating UAS into the National Airspace System. A DAA system is a suite of sensors, trackers, and alerting and guidance algorithms that assists a remote pilot in maintaining separation from airborne traffic. Previous works have focused on operations transiting to and from Class A or special use airspace. More recent efforts have defined DAA system requirements for operations in and around the airport terminal environment. In continuation of this effort, this paper defines the DAA Terminal Area (DTA) size and switching technique for the DAA Well Clear (DWC) volume while transitioning between en route and the terminal area.
Ongoing efforts at NASA’s Langley Research Center have produced a single passive sensor system for detecting ground objects and pinpointing their real-world location to a desired level of precision. The Langley center serves as a test range for unmanned aerial systems (UAS) and real-time knowledge about the location of people on campus is needed to inform least-risk UAS flight operations. The proposed system provides this knowledge through a camera combined with a convolutional neural network and an algorithm that projects an imaginary grid of square cells from the ground plane onto the perspective view of the camera. The position of detected objects on the camera’s projected grid determines their location in the real-world. The imaginary grid is easily mapped to a universal coordinate system, such as longitude and latitude, to provide both relative and absolute positional information of the detected objects. This simple system is shown to be accurate and effective, with decisive advantages over alternative multi-sensor and active sensor approaches. Extensions to the system are described to allow adaptation to a variety of other use cases.
This paper presents a medium-voltage intelligent measurement platform that provides post-processing of raw measurements received from commercially-available voltage/current sensors to enhance accuracy and bandwidth of the monitoring system. Furthermore, this platform integrates into a Data Distribution Service (DDS) messaging bus for distributed control and interoperability. The authors present the design, specifications, and major components included in the platform, and argue for applications and use cases that most benefit from this new approach. A laboratory testbed has been created to demonstrate the functionality, for which test results are presented for selected test cases.
Texas Instruments (TI) is currently contracted by the Air Force Wright Laboratory and the Defense Advanced Research Projects Agency (DARPA) to develop the next generation flexible semiconductor wafer fabrication system called Microelectronics Manufacturing Science & Technology (MMST). Several revolutionary concepts are being pioneered on MMST, including the following: new single-wafer rapid thermal processes, in-situ sensors, cluster equipment, and advanced Computer Integrated Manufacturing (CIM) software. The objective of the project is to develop a manufacturing system capable of achieving an order of magnitude improvement in almost all aspects of wafer fabrication. TI was awarded the contract in Oct., 1988, and will complete development with a fabrication facility demonstration in April, 1993. An important part of MMST is development of the CIM environment responsible for coordinating all parts of the system. The CIM architecture being developed is based on a distributed object oriented framework made of several cooperating subsystems. The software subsystems include the following: process control for dynamic control of factory processes; modular processing system for controlling the processing equipment; generic equipment model which provides an interface between processing equipment and the rest of the factory; specification system which maintains factory documents and product specifications; simulator for modelling the factory for analysis purposes; scheduler for scheduling work on the factory floor; and the planner for planning and monitoring of orders within the factory. This paper first outlines the division of responsibility between the planner, scheduler, and simulator subsystems. It then describes the approach to incremental planning and the way in which uncertainty is modelled within the plan representation. Finally, current status and initial results are described.
This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.
NASA s Synthetic Vision Systems (SVS) Project conducted research aimed at eliminating visibility-induced errors and low visibility conditions as causal factors in civil aircraft accidents while enabling the operational benefits of clear day flight operations regardless of actual outside visibility. SVS takes advantage of many enabling technologies to achieve this capability including, for example, the Global Positioning System (GPS), data links, radar, imaging sensors, geospatial databases, advanced display media and three dimensional video graphics processors. Integration of these technologies to achieve the SVS concept provides pilots with high-integrity information that improves situational awareness with respect to terrain, obstacles, traffic, and flight path. This paper attempts to emphasize the system aspects of SVS - true systems, rather than just terrain on a flight display - and to document from an historical viewpoint many of the best practices that evolved during the SVS Project from the perspective of some of the NASA researchers most heavily involved in its execution. The Integrated SVS Concepts are envisagements of what production-grade Synthetic Vision systems might, or perhaps should, be in order to provide the desired functional capabilities that eliminate low visibility as a causal factor to accidents and enable clear-day operational benefits regardless of visibility conditions.
Remote sensing for earth resources applications has as its objective the collection and interpretation of information on all environmentally significant features of the atmosphere, oceans, and land surfaces. This information is most effectively gathered by airborne and satellite borne imaging systems. These systems operate at a variety of wavelengths extending from the visual spectrum, through the infrared and into the centimeter wavelengths of the microwave band. A comparison is made of the signal characteristics at these spectrum regions. In particular, the ability of microwave radiometric imaging to augment visual and infrared imaging is discussed. Recent developments in electronically scanned microwave radiometric sensors are described, together with methods of optimizing microwave data display. Results achieved with integrated display systems for both microwave and optical data are presented, together with projections for future applications.-
In this paper we report on the development and demonstration of density feedback control for MAST-U. Sinusoidal perturbations are used to measure the frequency response from a deuterium gas valve (actuator) to line-integrated core electron density measured by the interferometer (sensor). In the frequency range relevant for control design, only two system-identification experiments were needed to regress a first-order dynamic model. This control-oriented model informs the offline design of a proportional integral controller with the established loop-shaping controller design method. After offline verification of the controller implementation, control is demonstrated by experimentally tracking a staircase reference for the line-integrated electron density. This paper demonstrates the efficiency of controller design using system-identification and loop-shaping, providing reliable density control for MAST-U.