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At least 19 records

TOWARD A METHOD FOR SCALING HUMAN BODY MODELS IN AN IMU-BASED WORKFLOW

BACKGROUND Scaled biomechanical models can more accurately inform crew health decisions when tailored to the wide range of astronaut sizes. One component to improve scaling of existing models to better represent each unique astronaut’s size is the individual length scaling of limbs. Traditionally, these lengths are determined by motion capture or manual measurement. A new method is herein proposed for length scaling which can be done by measuring linear and angular accelerations at a desired point during isolated motion around a point of rotation, then calculating the distance between the desired point and point of rotation. When an Inertial Measurement Unit (IMU) device is placed at the distal point of a limb, the isolated motion is about that limb’s proximal joint. For example, to measure forearm length, an IMU is placed at the wrist, the point of rotation is at the elbow, and the isolated motion is forearm flexion and extension. These calculated lengths are then used to scale models to each unique astronaut’s size, thereby improving the applicability of the model. This method of scaling limb segments can be used for any limb that has an easily defined proximal joint for the limb to rotate around including hands, arms, legs, feet. Utilizing IMUs for data collection also provides the synergistic ability to record data without a dedicated space in a room with many cameras, therefore reducing the data collection footprint, or record data where optical motion capture is not possible, such as inside a spacesuit. METHODS AND RESULTS To test this method, upper body data collection was performed with 5 Xsens DOT IMUs on a single subject. IMUs consist of an accelerometer, a gyroscope, and a magnetometer which collect linear acceleration, angular velocity, and magnetic fluctuations, respectively. Before any ground-based laboratory collection, the magnetic fluctuations are used to correct the heading of the IMU in space relative to the Earth’s magnetic field. The direct measurement of angular velocity is integrated to calculate angular acceleration. Then the linear acceleration ( a ) and angular acceleration (α) are solved using r = at/α to calculate the radius, which in this case is the distance between the IMU and the point of rotation (i.e., segment length). The IMU must be placed at the most distal point of the limb being measured (i.e., ankle if measuring lower leg length) and the test plan must consist of an isolated motion about that limb’s proximal joint (i.e., knee flexion and extension if measuring lower leg length). The distances (radii) calculated at every time interval were filtered (bandpass filter keeping 5th-90th percentile data) to eliminate outliers and spurious data that occur when the isolated motion was stopped or nearly stopped. The remaining distances were averaged, resulting in the calculated limb length. Scaling factors were then computed by dividing the calculated limb length by the unscaled model’s length. These scale factors are plugged into the Scale Tool in OpenSim [1,2] to apply the scaling to the OpenSim Full Body Rajagopal Model [3,4]. Manual measurements of limb lengths were taken before data collection started and used for comparing against the calculated lengths. The Anthropometric Survey of US Army Personnel (ANSUR II) [5] was also used as a third source of reference for limb length measurements. The following measurements were retrieved from the subject before data collection: 34.5 cm from L1 to C7 (thorax), 25.7 cm from C7 Joint Center (JC) to head vertex (neck and head), 36.3 cm from shoulder JC to elbow JC (humerus), 29.5 cm from elbow JC to wrist JC (forearm), and 16.2 cm from clavicle to acromion (clavicle). Of those five, forearm and humerus lengths were calculated using this proposed method to obtain preliminary results. The forearm length after filtering and averaging was calculated to be 37.6 cm. This is a 28% overestimation from the measured forearm length (29.5 cm). The humerus length after filtering and averaging was calculated to be 47.8 cm. This is a 31% difference from the measured subject length (36.3 cm). Sources of error include imperfect isolated motion (method currently expects that motion should be perfectly circular in a 2D plane, include no rotation of the IMU, and be relatively smooth; a more secure IMU attachment method will help), unrefined filter techniques (removed highest and lowest values with 20% high and low pass filters and no smoothing filters), arbitrary removal of stopped or near stopped data (kept data for only a short range before and after the angular velocity peaking), and a more representative method for removing gravitational acceleration is needed (current method is to zero all accelerations against a baseline taken just before the isolated motion which does not account for the gravitational acceleration changed due to IMU rotation during movement). Addressing these error sources will improve the accuracy of the limb length calculation. Next steps include creating a method for whole-body scaling estimation using individual limb scale factors. Continued pursuit of these techniques is expected to enable acquiring anthropometric information using only IMUs in real-time.

E. K. Marecki↗

Star Tracker Performance Estimate with IMU

A software tool for estimating cross-boresight error of a star tracker combined with an inertial measurement unit (IMU) was developed to support trade studies for the Integrated Radio and Optical Communication project (iROC) at the National Aeronautics and Space Administration Glenn Research Center. Typical laser communication systems, such as the Lunar Laser Communication Demonstration (LLCD) and the Laser Communication Relay Demonstration (LCRD), use a beacon to locate ground stations. iROC is investigating the use of beaconless precision laser pointing to enable laser communication at Mars orbits and beyond. Precision attitude knowledge is essential to the iROC mission to enable high-speed steering of the optical link. The preliminary concept to achieve this precision attitude knowledge is to use star trackers combined with an IMU. The Star Tracker Accuracy (STAcc) software was developed to rapidly assess the capabilities of star tracker and IMU configurations. STAcc determines the overall cross-boresight error of a star tracker with an IMU given the characteristic parameters: quantum efficiency, aperture, apparent star magnitude, exposure time, field of view, photon spread, detector pixels, spacecraft slew rate, maximum stars used for quaternion estimation, and IMU angular random walk. This paper discusses the supporting theory used to construct STAcc, verification of the program and sample results.

Error Budget↗

Trajectory Reconstruction of the ST-9 Sounding Rocket Experiment Using IMU and Landmark Data

This paper presents trajectory reconstruction of the ST-9 sounding rocket experiment using the onboard IMU data and descent imagery. The raw IMU accelerometer measurements are first converted into inertial acceleration and then used in trajectory integration. The descent images are pre-processed using a map-matching algorithm and unique landmarks for each image are created. Using the converted IMU data and descent images, the result from dead-reckoning and the kinematic-fix approaches are first compared with the GPS measurements. Then, both the IMU data and landmarks are processed together using a batch least-squares filter and the position, velocity, stochastic acceleration, and camera orientation of each image are estimated. The reconstructed trajectory is compared with the GPS data and the corresponding formal uncertainties are presented. The result shows that IMU data and descent images processed with a batch filter algorithm provide the trajectory accuracy required for pin-point landing.

Park, Ryan S.↗

Shuttle orbit IMU alignment. Single-precision computation error

The source of computational error in the inertial measurement unit (IMU) onorbit alignment software was investigated. Simulation runs were made on the IBM 360/70 computer with the IMU orbit alignment software coded in hal/s. The results indicate that for small IMU misalignment angles (less than 600 arc seconds), single precision computations in combination with the arc cosine method of eigen rotation angle extraction introduces an additional misalignment error of up to 230 arc seconds per axis. Use of the arc sine method, however, produced negligible misalignment error. As a result of this study, the arc sine method was recommended for use in the IMU onorbit alignment software.

Mcclain, C. R.↗

Onorbit IMU alignment error budget

The Star Tracker, Crew Optical Alignment Sight (COAS), and Inertial Measurement Unit (IMU) from a complex navigation system with a multitude of error sources were combined. A complete list of the system errors is presented. The errors were combined in a rational way to yield an estimate of the IMU alignment accuracy for STS-1. The expected standard deviation in the IMU alignment error for STS-1 type alignments was determined to be 72 arc seconds per axis for star tracker alignments and 188 arc seconds per axis for COAS alignments. These estimates are based on current knowledge of the star tracker, COAS, IMU, and navigation base error specifications, and were partially verified by preliminary Monte Carlo analysis.

Corson, R. W.↗

Orbit IMU alignment: Error analysis

A comprehensive accuracy analysis of orbit inertial measurement unit (IMU) alignments using the shuttle star trackers was completed and the results are presented. Monte Carlo techniques were used in a computer simulation of the IMU alignment hardware and software systems to: (1) determine the expected Space Transportation System 1 Flight (STS-1) manual mode IMU alignment accuracy; (2) investigate the accuracy of alignments in later shuttle flights when the automatic mode of star acquisition may be used; and (3) verify that an analytical model previously used for estimating the alignment error is a valid model. The analysis results do not differ significantly from expectations. The standard deviation in the IMU alignment error for STS-1 alignments was determined to the 68 arc seconds per axis. This corresponds to a 99.7% probability that the magnitude of the total alignment error is less than 258 arc seconds.

Corson, R. W.↗

The processing of IMU data in ENTREE implementation and preliminary results

It is demonstrated that the shuttle entry trajectory can be accurately represented in ENTREE with IMU data available postflight. The IMU data consist of platform to body quaternions, and accumulated sensed velocities in mean of fifty (M50) coordinates approximately every second. The preprocessing software required to incorporate the IMU data in ENTREE is described as well as the relatively minor code changes to the ENTREE program itself required to process the IMU data. Code changes to the ENTREE program and input tape data format and content changes are described.

Heck, M. L.↗

Shuttle onboard IMU alignment methods

The current approach to the shuttle IMU alignment is based solely on the Apollo Deterministic Method. This method is simple, fast, reliable and provides an accurate estimate for the present cluster to mean of 1,950 transformation matrix. If four or more star sightings are available, the application of least squares analysis can be utilized. The least squares method offers the next level of sophistication to the IMU alignment solution. The least squares method studied shows that a more accurate estimate for the misalignment angles is computed, and the IMU drift rates are a free by-product of the analysis. Core storage requirements are considerably more; estimated 20 to 30 times the core required for the Apollo Deterministic Method. The least squares method offers an intermediate solution utilizing as much data that is available without a complete statistical analysis as in Kalman filtering.

Henderson, D. M.↗

A detailed description of the sequential probability ratio test for 2-IMU FDI

The sequential probability ratio test (SPRT) for 2-IMU FDI (inertial measuring unit failure detection/isolation) is described. The SPRT is a statistical technique for detecting and isolating soft IMU failures originally developed for the strapdown inertial reference unit. The flowchart of a subroutine incorporating the 2-IMU SPRT is included.

Rich, T. M.↗

Orbit IMU alinement interpretation of onboard display data

The space shuttle inertial measurement unit (IMU) alinement algorith was examined to determine the most important alinement starpair selection criterion. Three crew displayed parameters were considered: (1) the results of the separation angle difference (SAD) check for each starpair; (2) the separation angle of each starpair; and (3) the age of each star measurement. It was determined that the SAD for each pair cannot be used to predict the IMu alinement accuracy. If the age of each star measurement is less than approximately 30 minutes, time is a relatively unimportant factor and the most important alinement pair selection criterion is the starpair separation angle. Therefore, when there are three available alinement starpairs and all measurements were taken within the last 30 minutes, the pair with the separation angle closest to 90 degrees should be selected for IMU alinement.

Corson, R.↗

Miniaturized GPS/MEMS IMU integrated board

This invention documents the efforts on the research and development of a miniaturized GPS/MEMS IMU integrated navigation system. A miniaturized GPS/MEMS IMU integrated navigation system is presented; Laser Dynamic Range Imager (LDRI) based alignment algorithm for space applications is discussed. Two navigation cameras are also included to measure the range and range rate which can be integrated into the GPS/MEMS IMU system to enhance the navigation solution.

Lin, Ching-Fang↗

Vision-based Velocimetry over Unknown Terrain with a Low-Noise IMU

This paper presents a novel approach to terrain-relative navigation with a visual camera and Inertial Measurement Unit (IMU). The proposed algorithm uses an Extended Kalman Filter (EKF) to combine an IMU propagated state estimate with batch correction estimates computed over a sliding window of measurements. The batch correction algorithm follows the Maximum Likelihood Estimation (MLE) approach used in other Bundle Adjustment systems. Unlike other systems, the proposed system parameterizes the state over the entire window in terms of the state at a single epoch. By ignoring IMU error over the window duration, we obtain a state epoch MLE that jointly estimates the epoch state and terrain parameters with drastically reduced computationally cost. This paper presents the general architecture which can be adapted for various state parameterizations and measurement inputs. For space applications with high-accuracy IMUs, the reduction in computational cost comes with only a modest increase in estimation errors. The increase in error is quantified via a linear covariance analysis presented in this paper. Furthermore, we present simulation results which show the applicability of this algorithm to planetary landing problems.

San Martin, A. Miguel↗

Multiple IMU system test plan, volume 4

Operating procedures for this redundant system are described. A test plan is developed with two objectives. First, performance of the hardware and software delivered is demonstrated. Second, applicability of multiple IMU systems to the space shuttle mission is shown through detailed experiments with FDI algorithms and other multiple IMU software: gyrocompassing, calibration, and navigation. Gimbal flip is examined in light of its possible detrimental effects on FDI and navigation. For Vol. 3, see N74-10296.

Landey, M.↗

Equipping an FPGA-Based Mars Rover With an LN-200 IMU

The Mars Exploration Rovers (MER) currently navigating the surface of Mars are outfitted with an advanced stereovision correlation algorithm which allows them to "see" three-dimensionally and autonomously avoid obstac'les in their path. A bottleneck of this system is that it is computationally intense and requires 3 minutes of processing for every correlated image and path choice. Taking advantage of the optimization and reprogrammability of FPGAs, the Mobility Avionics lab has reduced this process to under a second. The lab is demonstrating the advancement with a prototype rover, complete with an LN-200 inertial measurement unit (IMU), which is a flight spare from MER. The LN-200 is a space-grade, six degrees-of-freedom IMU using three fiber-optic gyroscopes and three silicon accelerometers and no moving parts. It has particular power-sequencing needs and communicates with a specialized serial protocol (SDLC over RS-422), requiring specific hardware and software for proper functionality and interfacing with an FPGA. The process of incorporating the LN-200 into the system is described herein.

Zola, Nicholas J.↗

Thermal Assessment of Sunlight Impinging on OSIRIS-REx OCAMS PolyCam, OTES, and IMU-Sunshade MLI Blankets in Flight

The NASA Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) spacecraft was successfully launched into orbit on September 8, 2016. It is traveling to a near-Earth asteroid (101955) Bennu, study it in detail, and bring back a pristine sample to Earth for scientific analyses. At the Outbound Cruise nominal spacecraft attitude, with Sun on +X, sunlight impinges on the OSIRIS-REx camera suite (OCAMS) PolyCam sunshade multilayer insulation (MLI) with microporous black polytetrafluoroethylene (PTFE), a portion of the PolyCam optics support tube (MLI with germanium black Kapton (GBK)), a portion of the OSIRIS-REx Thermal Emission Spectrometer (OTES) sunshade (MLI with GBK), the Inertia Measurement Unit (IMU) sunshade (MLI with GBK), and the OSIRIS-REx Laser Altimeter (OLA) sunshade (MLI with GBK). Sunlight is reflected or scattered by the above MLIs to the other components on the forward (+Z) deck. It illuminates the forward deck. A detailed thermal assessment on the solar impingement has been performed for the Proximity Ops at the asteroid, Touch-and-Go (TAG) sample acquisition, and Return Cruise mission phases.The OSIRIS-REx Outbound Cruise flight temperature telemetry and USM_3_DPC_0_CURRENT flight currenttelemetry data have been analyzed. It is evident that at the nominal Outbound Cruise spacecraft Sun-pointing attitude(i.e., Sun on +X), sunlight impinging on the PolyCam, OTES, IMU-sunshade and OLA-sunshade MLIs is reflected orscattered to the forward deck and components on the forward deck. It illuminates the forward deck. The StowCam imageof Day 265 2016 also provided an evidence. The reflected or scattered sunlight cause warming to the forward deck andcomponents on its +Z side. It may also contribute to degradation of thermal coatings over the mission life. It is a factorthat the OVIRS detector operating temperature exceeds the 105K maximum AFT limit. The OVIRS PrincipalInvestigator indicated that it is not optimum but acceptable for science. With exception of the OVIRS detector, thecorrelated flight system thermal model predictions for the components on the forward deck have adequate margins in theProximity Ops, TAG and Return Cruise phases. The margins are expected to cover the warming caused by the solarimpingement and the contribution to degradation of thermal coatings. The solar impingement is not expected to be athermal risk to the OSIRIS-REx mission. The second SRC Optical Properties characterization will be repeated in theReturn Cruise to provide a good characterization of any changes in optical properties that might have occurred duringthe TAG, or during several years in space. If the SRC battery runs much warmer than that of the first characterization inthe Outbound Cruise, it will be necessary to make some changes to the SRC Release timeline to assure the SRC batterytemperature are within limits. If GBK, instead of microporous black PTFE, were used on the PolyCam sunshade MLI,much more sunlight would have been reflected or scattered to the forward deck and components on its +Z side.Microporous black PTFE should be considered to mitigate the optical and thermal issues of sunlight reflected/scatteredby MLI blankets in future missions.

Choi, Michael K.↗

Toward an IMU-based Space Suit Motion Capture System

Spacesuits are complex engineering systems that sustain human health and enable performance outside of Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA on the lunar surface during the Artemis program is expected to be more frequent and require higher physical workloads than previous EVAs during the ISS, Shuttle, or Apollo programs. Hence it is important to optimize future as well as current spacesuits to be efficient and comfortable for the success of space and planetary missions. To enable this, an efficient method is needed to test these spacesuits on the ground.When testing spacesuits in ground environments, it is often necessary to understand the kinematics of the suit to validate the design against relevant requirements or characterize the physical workload necessary to operate the suit. This is a challenging task for traditional optical motion capture (OMC) approaches: suit-mounted OMC markers are easily occluded by the subject or environment and may become detached during testing. Controlling lighting and reflectivity of objects in the motion capture volume is also difficult. Fixed-position OMC cameras also constrain testing to a small and contrived laboratory environment, disallowing kinematics capture in field environments.One promising alternative is the use of suit-mounted inertial measurement units (IMUs). These sensors are small, unobtrusive, and portable, but come at the cost of increased sensor noise and complexity of the software and mathematics to analyze the collected data. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture methodand inverse kinematics solver which relies solely on a network of wireless IMUs attached to the major kinematic segments of the spacesuit. The ASIK modeling language allows for the simple inclusion of probabilistic priors such as suit size and shape or IMU positions and rotations. Furthermore, to increase accuracy and reduce operational overhead to use this motion capture approach, the developed inverse kinematics solver exploits so-called self-calibratingalgorithmic techniques, which reduce the need for precise alignment of the sensors on the segments or scripted functional calibration procedures. The ASIK system was tested in a 7-subject pilot study. Each subject donned NASA’s new prototype exploration spacesuit in the Active Response Gravity Offload System (ARGOS) facility at the NASA Johnson Space Center. The subjects were outfitted with a set of 14 APDM (Portland, OR, USA) Opal IMUs, 12 of which were used in the ASIK model to estimate lower body and trunk kinematics. The subjects were also outfitted with a set of reflective OMC markers and traditional OMC data was collected and processed. Presented results will include characterization of ASIK-derived suit joint angles accuracy against an optical motion capture datum. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.If successful, IMU-based motion capture will enable testing and validation of spacesuits more frequently, with less overhead, in more extreme environments. Future work will apply these techniques to common spacesuit testing tasks, such as gait, mobility, and balance assessment, physical workload characterization, and ergonomics evaluations.

Timothy Mcgrath↗

Subsurface Gravimetry Using Rover IMU Navigation Systems Data

Gravimetry is a valuable analytical tool for probing density distributions in the subsurface. Extraterrestrial data is currently limited to orbital collection, restricting spatial resolution to tens to hundreds of kilometers. Therefore, surface-based gravity surveys are necessary to study the gravity field and- lateral density variations at a finer scale. Traditional gravity sensors (gravimeters) are rare on science payloads owing to their relatively high mass, fragility, and cost. Inertial measurement units (IMU); however, are included on every rover platform to perform navigation and attitude determination. This project aims to solve this issue by demonstrating the utility in recalibrating the accelerometers contained within an IMU to measure the static acceleration of gravitational fields and, by extension, gravity anomalies induced by subsurface density variations.

Chandler Lawson↗

Toward an IMU-Based Space Suit Motion Capture System

Spacesuits are complex engineering systems that sustain human health and enable performance outside Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA operations on the Lunar surface are expected to be more frequent and require higher physical workloads than previously during the ISS, Shuttle, and Apollo programs. To characterize the workloads and ergonomics needs a suit must support, the kinematics of the space suit must be measured during operationally-relevant tasks in ground analog environments. Kinematics capture of the suit is challenging for traditional optical motion capture (OMC) approaches due to marker occlusion, harsh lighting or environmental conditions, and tests with suit surrogates in outdoor field environments. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture method and inverse kinematics solver which relies solely on a network of wireless inertial measurement units (IMUs) attached to the major kinematic segments of the spacesuit. The ASIK modeling language allows for the simple inclusion of probabilistic priors such as suit size and shape or IMU poses. The ASIK system was tested in a 7-subject pilot study. Each subject donned NASA’s new prototype exploration spacesuit in the Active Response Gravity Offload System (ARGOS) facility at Johnson Space Center in Houston, TX. The suits were outfitted with 12 IMUs to estimate lower body and trunk kinematics. The suits were also outfitted with a set of reflective OMC markers, and traditional OMC data was collected and processed. Characterization of the ASIK-derived suit joint angles’ accuracy against an optical motion capture datum will be presented. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.

IMU↗