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A brief description of the wind furnace concept is presented along with some preliminary performance data. Particular emphasis is placed on the design, construction, and manufacturing procedure for the 32.5 foot diameter GRP blades.
Integration of real-time visual procedures for use on the Mobile Perception Lab (MPL) was presented. The MPL is an autonomous vehicle designed for testing visually guided behavior. Two critical areas of focus in the system design were data storage/exchange and process control. The Intermediate Symbolic Representation (ISR3) supported data storage and exchange, and the MPL script monitor provided process control. Resource allocation, inter-process communication, and real-time control are difficult problems which must be solved in order to construct strong autonomous systems.
The Mount Washington Icing Sensors Project (MWISP) was a multi-investigator experiment with participants from Quadrant Engineering, NOAA Environmental Technology Laboratory (NOAA/ETL), the Microwave Remote Sensing Laboratory (MIRSL) of the University of Massachusetts (UMass), and others. Radar systems from UMass and NOAA/ETL were used to measure X-, Ka-, and W-band backscatter data from the base of Mt. Washington, while simultaneous in-situ particle measurements were made from aircraft and from the observatory at the summit. This report presents range and time profiles of liquid water content and particle size parameters derived from range profiles of radar reflectivity as measured at X-, Ka-, and W-band (9.3, 33.1, and 94.9 GHz) using an artificial neural network inversion algorithm. In this report, we provide a brief description of the experiment configuration, radar systems, and a review of the artificial neural network used to extract cloud parameters from the radar data. Time histories of liquid water content (LWC), mean volume diameter (MVD) and mean Z diameter (MZD) are plotted at 300 m range intervals for slant ranges between 1.1 and 4 km. Appendix A provides details on the extraction of radar reflectivity from measured radar power, and Appendix B provides summary logs of the weather conditions for each day in which we processed data.
A new algorithm for estimating the proportions of the multiyear and first-year sea ice types under variable atmospheric and sea surface conditions is presented, which uses all six channels of the SMMR. The algorithm is specifically tuned to derive sea ice parameters while accepting error in the auxiliary parameters of surface temperature, ocean surface wind speed, atmospheric water vapor, and cloud liquid water content. Not only does the algorithm naturally correct for changes in these weather conditions, but it retrieves sea ice parameters to the extent that gross errors in atmospheric conditions propagate only small errors into the sea ice retrievals. A preliminary evaluation indicates that the weather-correcting algorithm provides a better data product than the 'UMass-AES' algorithm, whose quality has been cross checked with independent surface observations. The algorithm performs best when the sea ice concentration is less than 20 percent.
Monitoring the ocean surface winds and mean ocean surface level is essential for improving our knowledge of the climate. Two instruments that may provide us with this information are satellite-based scatterometers and altimeters. However, these instruments measure the backscatter characteristics of the ocean surface from which other physical parameters, such as the wind speed or ocean surface height, are derived. To improve the algorithms or models that relate the electromagnetic backscatter to the desired physical parameters, the University of Massachusetts (UMass) Microwave Remote Sensing Laboratory (MIRSL) designed and fabricated three airborne scatterometers: a C-band scatterometer (CSCAT), Ku-band scatterometer (KUSCAT) and C/Ku-band scatterometer (EMBR). One or more of these instruments participated in the Electromagnetic Bias experiment (EM Bias), Shelf Edge Exchange Processes experiment (SEEP), Surface Wave Dynamics Experiment (SWADE), Southern Ocean Wave Experiment (SOWEX), Hurricane Tina research flights, Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE), and Ladir In-space Technology Experiment (LITE). This document describes the three scatterometers, summarizes our measurement campaigns and major contributions to the scientific and engineering communities, lists the publications that resulted, and presents the degrees earned under the support of this NASA grant.
The goal of the UMass work on this grant was to build an LO source at 1036 GHz for a receiver which was to be built at JPL. The 1 THz source will consist of a high power Gunn oscillator at 86 GHz followed by a cascaded pair of planar diode doublers and finally a whisker contacted tripler. All multipliers will use single mode waveguide mounts. This use of single mode waveguide even for the final mount is a departure from the original plan, and reflects the progress that has been made in fabricating small structures. The advantages to the use of waveguide over a quasi-optical approach are that the complete system is much more compact, and much easier to use.
The primary objective of this grant was to validate the modified CMOD4 wind speed algorithm used to invert RADARSAT data. We proposed to do the following: TASK 1: Participate in at least two underflights of RADARSAT (collecting backscatter measurements with the UMASS C-band and Ku-band scatterometers, CSCAT and KUSCAT. TASK 2: Process data gathered with CSCAT and KUSCAT into one kilometer along-track pixels with each pixel consisting of seventy-two five degree azimuth bins - each bin containing normalized radar cross section (NRCS) values for that particular azimuth angle and for each of the four incidence angles viewed by CSCAT and KUSCAT. Derive wind speed and direction estimates from each NRCS along-track pixel using CMOD4 and NSCAT models. TASK 3: Compare collocated CSCAT vertically polarized (VV) and RADARSAT horizontally polarized (HH) NRCS data and wind estimates, and based on the comparison, evaluate the performance of the modified CMOD4 wind speed algorithm to invert RADARSAT NRCS measurements. The first two tasks were completed. The third task is still on-going since the RADARSAT data for the underflights has not been released to the University of Massachusetts. We expect that the RADARSAT data will become available in the next few months, and we will complete the third task in a promptly manner once the data has been received. Below the missions flown and the data collected are summarized. Two missions, based out of the NASA Wallops Flight Facility, were flown. The first flight was on 21 September 1997 and the second on 24 September 1997. The time for each mission was approximately 2 hours. Figure 1 plots the flight tracks and marks the location of the NOAA buoys that were in the vicinity.
During the NOAA/NESDIS 2005 Hurricane Season (HS2005) and the 2006 Winter Experiment, the University of Massachusetts (UMass) installed two instruments on the NOAA N42RF WP-3D research aircraft: the Imaging Wind and Rain Airborne Profiler (IWRAP) and the Simultaneous Frequency Microwave Radiometer (SFMR). IWRAP is a dual-band (C- and Ku), dual-polarized pencil-beam airborne radar that profiles the volume backscatter and Doppler velocity from rain and that also measures the ocean backscatter response. It simultaneously profiles along four separate incidence angles while conically scanning at 60 RPM. SFMR is a C-band nadir viewing radiometer that measures the emission from the ocean surface and intervening atmosphere simultaneously at six frequencies. It is designed to obtain the surface wind speed and the column average rain rate. Both instruments have previously been flown during the 2002, 2003 and 2004 hurricane seasons. For the HS2005, the IWRAP system was modified to implement a raw data acquisition system. The importance of the raw data system arises when trying to profile the atmosphere all the way down to the surface with a non-nadir looking radar system. With this particular geometry, problems arise mainly from the fact that both rain and ocean provide a return echo coincident in time through the antenna s main lobe. This paper shows how this limitation has been removed and presents initial results demonstrating its new capabilities to derive the atmospheric boundary layer (ABL) wind field within the inner core of hurricanes to much lower altitudes than the ones the original system was capable of, and to analyze the spectral response of the ocean backscatter and the rain under different wind and rain conditions.
Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success.
During lunar excursions in the EVA suit, real-time measurement of metabolic rate is required to manage consumables and guide activities to ensure safe return to the base. Metabolic rate, or oxygen consumption (VO2), is normally measured from pulmonary parameters but cannot be determined with standard techniques in the oxygen-rich environment of a spacesuit. Our group has developed novel near infrared spectroscopic (NIRS) methods to calculate muscle oxygen saturation (SmO 2), hematocrit, and pH, and we recently demonstrated that we can use our NIRS sensor to measure VO 2 on the leg during cycling. Our NSBRI project has 4 objectives: (1) increase the accuracy of the metabolic rate calculation through improved prediction of stroke volume; (2) investigate the relative contributions of calf and thigh oxygen consumption to metabolic rate calculation for walking and running; (3) demonstrate that the NIRS-based noninvasive metabolic rate methodology is sensitive enough to detect decrement in VO 2 in a space analog; and (4) improve instrumentation to allow testing within a spacesuit. Over the past year we have made progress on all four objectives, but the most significant progress was made in improving the instrumentation. The NIRS system currently in use at JSC is based on fiber optics technology. Optical fiber bundles are used to deliver light from a light source in the monitor to the patient, and light reflected back from the patient s muscle to the monitor for spectroscopic analysis. The fiber optic cables are large and fragile, and there is no way to get them in and out of the test spacesuit used for ground-based studies. With complimentary funding from the US Army, we undertook a complete redesign of the sensor and control electronics to build a novel system small enough to be used within the spacesuit and portable enough to be used by a combat medic. In the new system the filament lamp used in the fiber optic system was replaced with a novel broadband near infrared LED light source. The compact grating spectrometer was replaced with a chip-scale spectrometer. With this new design, the sensor is 4 in 2 in 0.5 in, weighs 60 g, and no fiber optic cables are needed. The sensor, which contains the light source and the spectrometer, is adhered directly to the skin with medical grade adhesive. The sensor can be powered via the USB port of the laptop computer that controls the sensor operation. Alternatively, for studies in the spacesuit, the sensor can be powered by a small battery pack and operated by an ultra-portable hand-held computer. Both the handheld computer and battery pack will easily fit within the PLSS of the test spacesuit. System automation was significantly improved, to add features suggested by our colleagues in the Cardiovascular Laboratory and the NASA JSC Exercise Physiology and Countermeasures Project. The functionality and portability of this system were demonstrated in our UMass laboratory.
Humanoid class robots must have sufficient dexterity to assist people and work in an environment designed for human comfort and productivity. This dexterity, in particular the ability to use tools, requires a cognitive understanding of self and the world that exceeds contemporary robotics. Our hypothesis is that the sense-think-act paradigm that has proven so successful for autonomous robots is missing one or more key elements that will be needed for humanoids to meet their full potential as autonomous human assistants. This key ingredient is knowledge. The presented work includes experiments conducted on the Robonaut system, a NASA and the Defense Advanced research Projects Agency (DARPA) joint project, and includes collaborative efforts with a DARPA Mobile Autonomous Robot Software technical program team of researchers at NASA, MIT, USC, NRL, UMass and Vanderbilt. The paper reports on results in the areas of human-robot interaction (human tracking, gesture recognition, natural language, supervised control), perception (stereo vision, object identification, object pose estimation), autonomous grasping (tactile sensing, grasp reflex, grasp stability) and learning (human instruction, task level sequences, and sensorimotor association).