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The goal of the proposal is to determine the present motion of the main tectonic plates from the Doppler data of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) orbitography system, which includes in its final configuration about 50 tracking stations with a world-wide distribution.
A number of areas of research and laboratory experiments were identified which could lead to development of a cost efficient remote, disable satellite recovery system. Estimates were planned of disabled satellite motion. A concept is defined as a Tumbling Satellite Recovery kit which includes a modular system, composed of a number of subsystem mechanisms that can be readily integrated into varying combinations. This would enable the user to quickly configure a tailored remote, disabled satellite recovery kit to meet a broad spectrum of potential scenarios. The capability was determined of U.S. Earth based satellite tracking facilities to adequately determine the orientation and motion rates of disabled satellites.
INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.
INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.
An advanced infant car seat has been proposed to increase highway safety by reducing the incidence of crying, fussy behavior, and other child-related distractions that divert an adult driver s attention from driving. In addition to a conventional infant car seat with safety restraints, the proposed advanced infant car seat would include a number of components and subsystems that would function together as a comprehensive infant-care system that would keep its occupant safe, comfortable, and entertained, and would enable the driver to monitor the baby without having to either stop the car or turn around to face the infant during driving. The system would include a vibrator with bulb switch to operate; the switch would double as a squeeze toy that would make its own specific sound. A music subsystem would include loudspeakers built into the seat plus digital and analog circuitry that would utilize plug-in memory modules to synthesize music or a variety of other sounds. The music subsystem would include a built-in sound generator that could synthesize white noise or a human heartbeat to calm the baby to sleep. A second bulb switch could be used to control the music subsystem and would double as a squeeze toy that would make a distinct sound. An anti-noise sound-suppression system would isolate the baby from potentially disturbing ambient external noises. This subsystem would include small microphones, placed near the baby s ears, to detect ambient noise. The outputs of the microphone would be amplified and fed to the loudspeakers at appropriate amplitude and in a phase opposite that of the detected ambient noise, such that the net ambient sound arriving at the baby s ears would be almost completely cancelled. A video-camera subsystem would enable the driver to monitor the baby visually while continuing to face forward. One or more portable miniature video cameras could be embedded in the side of the infant car seat (see figure) or in a flip-down handle. The outputs of the video cameras would be transmitted by radio or infrared to a portable, miniature receiver/video monitor unit that would be attached to the dashboard of the car. The video-camera subsystem can also be used within transmission/reception range when the seat was removed from the car. The system would include a biotelemetric and tracking subsystem, which would include a Global Positioning System receiver for measuring its location. This subsystem would transmit the location of the infant car seat (even if the seat were not in a car) along with such biometric data as the baby s heart rate, perspiration rate, urinary status, temperature, and rate of breathing. Upon detecting any anomalies in the biometric data, this subsystem would send a warning to a paging device installed in the car or carried by the driver, so that the driver could pull the car off the road to attend to the baby. A motion detector in this subsystem would send a warning if the infant car seat were to be moved or otherwise disturbed unexpectedly while the infant was seated in it: this warning function, in combination with the position- tracking function, could help in finding a baby who had been kidnapped with the seat. Removable rechargeable batteries would enable uninterrupted functioning of all parts of the system while transporting the baby to and from the car. The batteries could be recharged via the cigarette-lighter outlet in the car or by use of an external AC-powered charger.
This paper describes a machine vision system for relative spacecraft navigation during the terminal phase of approach to docking that: 1) matches high contrast image features of the target vehicle, as seen by a camera that is bore-sighted to the docking adapter on the chase vehicle, to the corresponding features in a 3d model of the docking adapter on the target vehicle and 2) is robust to on-orbit lighting. An implementation is provided for the case of the Space Shuttle Orbiter docking to the International Space Station (ISS) with quantitative test results using a full scale, medium fidelity mock-up of the ISS docking adapter mounted on a 6-DOF motion platform at the NASA Marshall Spaceflight Center Flight Robotics Laboratory and qualitative test results using recorded video from the Orbiter Docking System Camera (ODSC) during multiple orbiter to ISS docking missions. The Natural Feature Image Registration (NFIR) system consists of two modules: 1) Tracking which tracks the target object from image to image and estimates the position and orientation (pose) of the docking camera relative to the target object and 2) Acquisition which recognizes the target object if it is in the docking camera Field-of-View and provides an approximate pose that is used to initialize tracking. Detected image edges are matched to the 3d model edges whose predicted location, based on the pose estimate and its first time derivative from the previous frame, is closest to the detected edge1 . Mismatches are eliminated using a rigid motion constraint. The remaining 2d image to 3d model matches are used to make a least squares estimate of the change in relative pose from the previous image to the current image. The changes in position and in attitude are used as data for two Kalman filters whose outputs are smoothed estimate of position and velocity plus attitude and attitude rate that are then used to predict the location of the 3d model features in the next image.
An inertial navigation system is described and analyzed based on two two-degree-of-freedom Schuler-gyropendulums and one two-degree-of-freedom azimuth gyro. The three sensors, each base motion isolated about its two input axes, are mounted on a common base, strapped down to the vehicle. The up and down pointing spin vectors of the two properly tuned gyropendulums track the vertical and indicate physically their velocity with respect to inertial space. The spin vector of the azimuth gyro is pointing northerly parallel to the earth axis. The system can be made self-aligning on a stationary base. If external measurements for the north direction and the vertical are available, initial disturbance torques can be measured and easily biased out. The error analysis shows that the system is practicable with today's technology.
An electrooptical head-position monitoring system was designed and built and is used in single-axis and three-axis 'hands-off' control tasks. The monitor consists of a transparent plexiglass body-fixed helmet provided with a set of eight silicon photodetectors sensing pitch, roll, and yaw motions of the head. Two light-emitting diodes, attached to the pilot's helmet liner, provide the ac modulated near infrared radiation. Head control is compared with conventional manual control for single-axis and three-axis tracking tasks. Both performance curves and describing functions are presented.
Results are presented for a study program designed to develop a better means of obtaining wind data needed by hurricane forecasters and to determine the optimum space and time resolution of satellite data used to obtain such winds. The discussion covers cloud tracking with an image display and manipulation system and analysis of IR imagery of Hurricane Eloise. It is shown that greater resolution in time and space of satellite imagery made it possible to derive up to six times as many low-level winds in a hurricane case, to eliminate 'bad winds' caused by mistaking cloud growth for cloud motion, and to remove ambiguities that would have caused difficulty in tracking clouds using only the imagery taken at 30-min intervals. It is suggested to use short-interval imagery (less than 10 min) for wind determination for future research in areas of tropical cyclones.
The dynamics of tracked air-cushion vehicles considered for high-speed ground transportation systems are examined using a vehicle model represented by a one-degree-of-freedom system. The respective equations of motion are derived, and the control system is discussed. The equations obtained for the cushion are solved using an analog computer. The computer showed the effect of the control technique on the relative motion between vehicle and guideway.
A piloted simulation study was conducted to examine the effect of motion cues using a high fidelity simulation of commercial aircraft during the performance of complex approach and landing tasks in the Microwave Landing System (MLS) signal environment. The data from these tests indicate that in a high complexity MLS approach task with moderate turbulence and wind, the pilot uses motion cues to improve path tracking performance. No significant differences in tracking accuracy were noted for the low and medium complexity tasks, regardless of the presence of motion cues. Higher control input rates were measured for all tasks when motion was used. Pilot eye scan, as measured by instrument dwell time, was faster when motion cues were used regardless of the complexity of the approach tasks. Pilot comments indicated a preference for motion. With motion cues, pilots appeared to work harder in all levels of task complexity and to improve tracking performance in the most complex approach task.
The programming and algorithms needed for implementing a full-field, 3-D velocimeter for laminar flow systems and the appropriate hardware to fully implement this ultimate system are discussed. It appears that imaging using a synched pair of video cameras and digitizer boards with synched rails for camera motion will provide a viable solution to the laminar tracking problem. The algorithms given here are simple, which should speed processing. On a heavily loaded VAXstation 3100 the particle identification can take 15 to 30 seconds, with the tracking taking less than one second. It seeems reasonable to assume that four image pairs can thus be acquired and analyzed in under one minute.
There has long been qualitative evidence that humans can pursue an object defined only by the motion of its parts. We explored this quantitatively using an occluded diamond stimulus. Four subjects (one naive) tracked a line-figure diamond moving along an elliptical path (0.9 Hz) either clockwise (CW) or counterclockwise (CCW) behind either an X-shaped aperture (CROSS) or two vertical rectangular apertures (BARS), which obscured the corners. Although the stimulus consisted of only four line segments (108 cd/square m) moving within a visible aperture (0.2 cd/square m) behind a foreground (38 cd/square m), it is largely perceived as a coherently moving diamond. The inter-saccadic portions of eye-position traces were fit with sinusoids. All subjects tracked object motion with considerable temporal accuracy. The mean phase lag was 5 deg/6 deg (CROSS/BARS) and the mean relative phase between the horizontal and vertical components was +95 deg/+92 deg (CW) and -85 deg/-75 deg (CCW), which is close to perfect. Furthermore, a chi-square analysis showed that 56% of BARS trials were consistent with tracking the correct elliptical shape (p is less than 0.05), although segment motion was purely vertical. These data disprove the main tenet of most models of pursuit: that it is a system that seeks to minimize retinal image motion through negative feedback. Rather, the main drive must be a visual signal which has already integrated spatiotemporal retinal information into an object-motion signal.
The effect of weightlessness on the human operator's performance in force reflecting position control of remote manipulators was investigated. A gravity compensation system was developed to simulate the effect of weightlessness on the operator's arm. A universal force reflecting hand controller (FRHC) and task simulation software were employed. Two experiments were performed because of anticipated disturbances in neuromotor control specification on the human operator in an orbital control environment to investigate: (1) the effect of controller stiffness on the attainment of a learned terminal position in the three dimensional controller space, and (2) the effect of controller stiffness and damping on force tracking of the contour of a simulated three dimensional cube using the part simulation of weightless conditions. The results support the extension of neuromotor control models, which postulate a stiffness balance encoding of terminal position, to three dimensional motion of a multilink system, confirm the existence of a disturbance in human manual control performance under gravity compensated conditions, and suggest techniques for compensation of weightlessness induced performance decrement through appropriate specification of hand controller response characteristics. These techniques are based on the human control model.
A software system for autonomous operation of a Mars rover is composed of several key algorithms that enable the rover to accurately follow a designated path, compensate for slippage of its wheels on terrain, and reach intended goals. The techniques implemented by the algorithms are visual odometry, full vehicle kinematics, a Kalman filter, and path following with slip compensation. The visual-odometry algorithm tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs, by use of a maximum-likelihood motion-estimation algorithm. The full-vehicle kinematics algorithm estimates motion, with a no-slip assumption, from measured wheel rates, steering angles, and angles of rockers and bogies in the rover suspension system. The Kalman filter merges data from an inertial measurement unit (IMU) and the visual-odometry algorithm. The merged estimate is then compared to the kinematic estimate to determine whether and how much slippage has occurred. The kinematic estimate is used to complement the Kalman-filter estimate if no statistically significant slippage has occurred. If slippage has occurred, then a slip vector is calculated by subtracting the current Kalman filter estimate from the kinematic estimate. This slip vector is then used, in conjunction with the inverse kinematics, to determine the wheel velocities and steering angles needed to compensate for slip and follow the desired path.
The requirement to operate aircraft at low-altitude near the terrain is common in the military community and essential for helicopters. The risk and crew workload in this flight regime is severe, with navigation, guidance, and obstacle avoidance demanding high attention. A guidance system relying on digitized terrain elevation maps has been developed that employs airborne navigation, mission requirements, aircraft performance limits, and radar altimeter returns to generate a valley-seeking, low-altitude trajectory between waypoints for display to the pilot. This system has been flight demonstrated to 150 ft above ground level altitude, and is primarily limited by the ability of the pilot to perform obstacle detection and avoidance. In this study, a wide field of view forward sensor has been modeled and incorporated in the guidance system for the purpose of relieving the pilot of the obstacle avoidance duty. The results of a piloted, motion-based simulation of this enhanced low-altitude guidance system is presented. Simulated flights to 50 ft altitude in the presence of obstacles were demonstrated while maintaining situational awareness and close tracking of the guidance trajectory.
Eye movements are the most frequent (~3 per second), shortest-latency (~150-250 ms), and biomechanically simplest (1 joint, no inertial complexities) voluntary motor behavior in primates, providing a model system to assess sensorimotor disturbances arising from trauma, fatigue, aging, or disease states (e.g., Diefendorf and Dodge, 1908). We developed a 15-minute behavioral tracking protocol consisting of randomized stepramp radial target motion to assess several aspects of the behavioral response to dynamic visual motion, including pursuit initiation, steadystate tracking, direction-tuning, and speed-tuning thresholds. This set of oculomotor metrics provide valid and reliable measures of dynamic visual performance (Stone and Krauzlis, 2003; Krukowski and Stone, 2005; Stone et al, 2009; Liston and Stone, 2014), and may prove to be a useful assessment tool for functional impairments of dynamic visual processing.
The objective is to investigate the application of Doppler radar systems for global wind measurement. A model of the satellite-based radar wind sounder (RAWS) is discussed, and many critical problems in the designing process, such as the antenna scan pattern, tracking the Doppler shift caused by satellite motion, and backscattering of radar signals from different types of clouds, are discussed along with their computer simulations. In addition, algorithms for measuring mean frequency of radar echoes, such as the Fast Fourier Transform (FFT) estimator, the covariance estimator, and the estimators based on autoregressive models, are discussed. Monte Carlo computer simulations were used to compare the performance of these algorithms. Anti-alias methods are discussed for the FFT and the autoregressive methods. Several algorithms for reducing radar ambiguity were studied, such as random phase coding methods and staggered pulse repitition frequncy (PRF) methods. Computer simulations showed that these methods are not applicable to the RAWS because of the broad spectral widths of the radar echoes from clouds. A waveform modulation method using the concept of spread spectrum and correlation detection was developed to solve the radar ambiguity. Radar ambiguity functions were used to analyze the effective signal-to-noise ratios for the waveform modulation method. The results showed that, with suitable bandwidth product and modulation of the waveform, this method can achieve the desired maximum range and maximum frequency of the radar system.