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IMU/GPS System Provides Position and Attitude Data

A special navigation system is being developed to provide high-quality information on the position and attitude of a moving platform (an aircraft or spacecraft), for use in pointing and stabilization of a hyperspectral remote-sensing system carried aboard the platform. The system also serves to enable synchronization and interpretation of readouts of all onboard sensors. The heart of the system is a commercially available unit, small enough to be held in one hand, that contains an integral combination of an inertial measurement unit (IMU) of the microelectromechanical systems (MEMS) type, Global Positioning System (GPS) receivers, a differential GPS subsystem, and ancillary data-processing subsystems. The system utilizes GPS carrier-phase measurements to generate time data plus highly accurate and continuous data on the position, attitude, rotation, and acceleration of the platform. Relative to prior navigation systems based on IMU and GPS subsystems, this system is smaller, is less expensive, and performs better. Optionally, the system can easily be connected to a laptop computer for demonstration and evaluation. In addition to airborne and spaceborne remote-sensing applications, there are numerous potential terrestrial sensing, measurement, and navigation applications in diverse endeavors that include forestry, environmental monitoring, agriculture, mining, and robotics.

Lin, Ching Fang↗

Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.

Huntsberger, Terrance L.↗

Sampling and Control Circuit Board for an Inertial Measurement Unit

Spacesuit navigation is one component of NASA s efforts to return humans to the Moon. Studies performed at the NASA Glenn Research Center (GRC) considered various navigation technologies and filtering approaches to enable navigation on the lunar surface. As part of this effort, microelectromechanical systems (MEMS) inertial measurement units (IMUs) were studied to determine if they could supplement a radiometric infrastructure. MEMS IMUs were included in the Lunar Extra-Vehicular Activity Crewmember Location Determination System (LECLDS) testbed during NASA s annual Desert Research and Technology Studies (D-RATS) event in 2009 and 2010. The testbed included one IMU in 2009 and three IMUs in 2010, along with a custom circuit board interfacing between the navigation processor and each IMU. The board was revised for the 2010 test, and this paper documents the design details of this latest revision of the interface circuit board and firmware.

Chelmins, David↗

Using Arm and Hand Gestures to Command Robots during Stealth Operations

Command of support robots by the warfighter requires intuitive interfaces to quickly communicate high degree-of-freedom (DOF) information while leaving the hands unencumbered. Stealth operations rule out voice commands and vision-based gesture interpretation techniques, as they often entail silent operations at night or in other low visibility conditions. Targeted at using bio-signal inputs to set navigation and manipulation goals for the robot (say, simply by pointing), we developed a system based on an electromyography (EMG) "BioSleeve", a high density sensor array for robust, practical signal collection from forearm muscles. The EMG sensor array data is fused with inertial measurement unit (IMU) data. This paper describes the BioSleeve system and presents initial results of decoding robot commands from the EMG and IMU data using a BioSleeve prototype with up to sixteen bipolar surface EMG sensors. The BioSleeve is demonstrated on the recognition of static hand positions (e.g. palm facing front, fingers upwards) and on dynamic gestures (e.g. hand wave). In preliminary experiments, over 90% correct recognition was achieved on five static and nine dynamic gestures. We use the BioSleeve to control a team of five LANdroid robots in individual and group/squad behaviors. We define a gesture composition mechanism that allows the specification of complex robot behaviors with only a small vocabulary of gestures/commands, and we illustrate it with a set of complex orders.

stealth operations↗

Reconstruction of Twist Torque in Main Parachute Risers

The reconstruction of twist torque in the Main Parachute Risers of the Capsule Parachute Assembly System (CPAS) has been successfully used to validate CPAS Model Memo conservative twist torque equations. Reconstruction of basic, one degree of freedom drop tests was used to create a functional process for the evaluation of more complex, rigid body simulation. The roll, pitch, and yaw of the body, the fly-out angles of the parachutes, and the relative location of the parachutes to the body are inputs to the torque simulation. The data collected by the Inertial Measurement Unit (IMU) was used to calculate the true torque. The simulation then used photogrammetric and IMU data as inputs into the Model Memo equations. The results were then compared to the true torque results to validate the Model Memo equations. The Model Memo parameters were based off of steel risers and the parameters will need to be re-evaluated for different materials. Photogrammetric data was found to be more accurate than the inertial data in accounting for the relative rotation between payload and cluster. The Model Memo equations were generally a good match and when not matching were generally conservative.

Day, Joshua D.↗

Development of a Protocol to Test Proprioceptive Utilization as a Predictor for Sensorimotor Adaptability

Astronauts returning from space flight show significant inter-subject variations in their abilities to readapt to a gravitational environment because of their innate sensory weighting. The ability to predict the manner and degree to which each individual astronaut will be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. We hypothesize participant's ability to utilize individual sensory information (vision, proprioception and vestibular) influences adaptation in sensorimotor performance after space flight. The goal of this study is to develop a reliable protocol to test proprioceptive utilization in a functional postural control task. Subjects "stand" in a supine position while strapped to a backpack frame holding a friction-free device using air-bearings that allow the subject to move freely in the frontal plane, similar to when in upright standing. The frame is attached to a pneumatic cylinder, which can provide different levels of a gravity-like force that the subject must balance against to remain "upright". The supine posture with eyes closed ensures reduced vestibular and visual contribution to postural control suggesting somatosensory and/or non-otolith vestibular inputs will provide relevant information for maintaining balance control in this task. This setup is called the gravity bed. Fourteen healthy subjects carried out three trials each with eyes open alternated with eyes closed, "standing" on their dominant leg in the gravity bed environment while loaded with 60 percent of their body weight. Subjects were instructed to: "use your sense of sway about the ankle and pressure changes under the foot to maintain balance." Maximum length of a trial was 45 seconds. A force plate underneath the foot recorded forces and moments during the trial and an inertial measurement unit (IMU) attached on the backpack's frame near the center of mass of the subject recorded upper body postural responses. Series of linear and non-linear analyses were carried out on several force plate and IMU data including stabilogram diffusion analysis on the center of pressure (COP) to find a subset of parameters that were sensitive to detect differences in postural performance between eyes open and closed conditions. Results revealed that seven parameters (root mean square (RMS) of medio-lateral (ML) COP, range of ML COP, RMS of roll moment, range of trunk roll, minimum time-to-boundary (TTB), integrated TTB, and critical mean square planar displacement (delta r (sup 2) (sub c)) were significantly different between eyes open and closed conditions. We will present data to show the efficacy of using performance in single leg stance with eyes closed on the gravity bed to assess individuals' ability to utilize proprioceptive information in a functional postural control task to predict re-adaptation for sensorimotor and functional performance.

Goel, R.↗

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee,a new free-flying robot designed to navigate autonomously on board the International Space Station (ISS). Astrobee will conduct experiments in microgravity, as well as assisst astronauts and ground controllers. Astrobee replaces the SPHERES robots which currently operate on the ISS, which were limited to operating in a small cube since their localization system relied on triangulation from ultrasonic transmitters. Astrobee localizes with only monocular vision and an IMU, enabling it to traverse the entire US segment of the station. Features detected on a previously-built map, optical flow information,and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise.Finally, we extensively evaluate the behavior of the filter on atwo-dimensional testing surface.

Coltin, Brian↗

GOES-R Active Vibration Damping Controller Design, Implementation, and On-Orbit Performance

GOES-R series spacecraft feature a number of flexible appendages with modal frequencies below 3.0 Hz which, if excited by spacecraft disturbances, can be sources of undesirable jitter perturbing spacecraft pointing. In order to meet GOES-R pointing stability requirements, the spacecraft flight software implements an Active Vibration Damping (AVD) rate control law which acts in parallel with the nadir point attitude control law. The AVD controller commands spacecraft reaction wheel actuators based upon Inertial Measurement Unit (IMU) inputs to provide additional damping for spacecraft structural modes below 3.0 Hz which vary with solar wing angle. A GOES-R spacecraft dynamics and attitude control system identified model is constructed from pseudo-random reaction wheel torque commands and IMU angular rate response measurements occurring over a single orbit during spacecraft post-deployment activities. The identified Fourier model is computed on the ground, uplinked to the spacecraft flight computer, and the AVD controller filter coefficients are periodically computed on-board from the Fourier model. Consequently, the AVD controller formulation is based not upon pre-launch simulation model estimates but upon on-orbit nadir point attitude control and time-varying spacecraft dynamics. GOES-R high-fidelity time domain simulation results herein demonstrate the accuracy of the AVD identified Fourier model relative to the pre-launch spacecraft dynamics and control truth model. The AVD controller on-board the GOES-16 spacecraft achieves more than a ten-fold increase in structural mode damping of the fundamental solar wing mode while maintaining controller stability margins and ensuring that the nadir point attitude control bandwidth does not fall below 0.02 Hz. On-orbit GOES-16 spacecraft appendage modal frequencies and damping ratios are quantified based upon the AVD system identification, and the increase in modal damping provided by the AVD controller for each structural mode is presented. The GOES-16 spacecraft AVD controller frequency domain stability margins and nadir point attitude control bandwidth are presented along with on-orbit time domain disturbance response performance.

Active Vibration Damping↗

In-Flight Guidance, Navigation, and Control Performance Results for the GOES-16 Spacecraft

The Geostationary Operational Environmental Satellite-R Series (GOES-R), which launched in November 2016, is the first of the next generation geostationary weather satellites. GOES-R provides 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands for Earth observations compared with its predecessor spacecraft. Additionally, Earth relative and Sun-relative pointing and pointing stability requirements are maintained throughout reaction wheel desaturation events and station keeping activities, allowing GOES-R to provide continuous Earth and sun observations. This paper reviews the pointing control, pointing stability, attitude knowledge, and orbit knowledge requirements necessary to realize the ambitious Image Navigation and Registration (INR) objectives of GOES-R. This paper presents a comparison between low-frequency on-orbit pointing results and simulation predictions for both the Earth Pointed Platform (EPP) and Sun Pointed Platform (SPP). Results indicate excellent agreement between simulation predictions and observed on-orbit performance, and compliance with pointing performance requirements. The EPP instrument suite includes 6 seismic accelerometers sampled at 2 KHz, allowing in-flight verification of jitter responses and comparison back to simulation predictions. This paper presents flight results of acceleration, shock response spectrum (SRS), and instrument line of sight responses for various operational scenarios and instrument observation modes. The results demonstrate the effectiveness of the dual-isolation approach employed on GOES-R. The spacecraft provides attitude and rate data to the primary Earth-observing instrument at 100 Hz, which are used to adjust instrument scanning. The data must meet accuracy and latency numbers defined by the Integrated Rate Error (IRE) requirements. This paper discusses the on-orbit IRE results, showing compliance to these requirements with margin. During the spacecraft checkout period, IRE disturbances were observed and subsequently attributed to thermal control of the Inertial Measurement Unit (IMU) mounting interface. Adjustments of IMU thermal control and the resulting improvements in IRE are presented. Orbit knowledge represents the final element of INR performance. Extremely accurate orbital position is achieved by GPS navigation at Geosynchronous Earth Orbit (GEO). On-orbit performance results are shown demonstrating compliance with the stringent orbit position accuracy requirements of GOES-R, including during station keeping activities and momentum desaturation events. As we show in this paper, the on-orbit performance of the GNC design provides the necessary capabilities to achieve GOES-R mission objectives.

Chapel, Jim↗

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗

Overview of Dragonfly Entry Aerosciences Measurements (DrEAM)

NASA Ames Research Center (ARC) leads the Dragonfly Entry Aerosciences Measurements (DrEAM) project, which is an aeroshell instrumentation suite on the Dragonfly mission that fulfills the Engineering Science Investigation requirement for the New Frontiers mission. NASA ARC is partnering with NASA Langley Research Center (LaRC) and the German Aerospace Center (DLR) to provide a comprehensive sensor suite, including a DLR-provided Data Acquisition System (DAS). DrEAM will provide key aerothermodynamic data and performance analysis for Dragonfly’s forebody and backshell Thermal Protection System (TPS). Titan’s atmosphere predominantly consists of ni-trogen (~98% by mole) with small amounts of me-thane (~2% by mole) and other trace gases. CN is a strong radiator, and is found in nonequilibrium con-centrations for Titan entry. The accurate modeling of nonequilibrium CN radiation has proven to be a diffi-cult task. Prompted by the Huygens mission, many experimental campaigns and analyses were performed to better understand the aerothermal environments experienced by the probe during Titan entry [1]. How-ever, the Huygens probe carried no heatshield instru-mentation. Therefore, the DrEAM sensor suite will sig-nificantly advance the state-of-the-art not only by documenting the environment and performance of Dragonfly’s entry system but also by making key in situ measurements in Titan’s atmosphere for the first time. Aerothermal environments and TPS response will be measured using sensors whose flight heritage is tak-en from the Mars Entry, Descent, and Landing In-strumentation 2 (MEDLI2) thermocouple plugs and the COMbined Aerothermal and Radiometer Sensor (COMARS) suite [2], with the latter supplied by DLR. The MEDLI2 project used embedded thermocouples to directly measure the in-depth TPS temperature-time history at several locations on the heat shield and backshell of the Mars 2020 entry vehicle. These tem-perature measurements, in turn, can be used to infer surface environments via an inverse analysis proce-dure analogous to that used for MEDLI. For DrEAM, the thermocouple plug subsystem will be known as Dragonfly Sensors for Aero-Thermal Reconstruction (DragSTR). On Schiaparelli, the COMARS suite in-cluded three total surface-mounted heat flux sensors, three pressure sensors, and one radiometer. For DrEAM, the COMARS package will be known as the COmbined Sensor System for Titan Atmosphere (COSSTA). Since the methane concentration in the Titan atmos-phere is directly proportional to the radiative heat flux, the COSSTA measurements will be used to reduce the current uncertainty in the methane volume fraction. Atmospheric density measurements and capsule aero-dynamic data will be obtained through the onboard Inertial Measurement Unit (IMU), supplemented by pressure transducers similar to those used by the MEDLI and MEDLI2 projects. The DrEAM pressure sensors will be known as the Dragonfly Atmospheric Flight Transducers. (DrAFT). The pressure measure-ments, when combined with data from the on-board IMU, will allow for reconstruction of such quantities as vehicle Mach number, freestream density, and atmos-pheric winds. DrAFT measurements will enhance Dragonfly trajectory reconstruction and enable a sepa-ration of the aerodynamics from the atmosphere, as was done for MEDLI [3] and is currently in process for the MEDLI2 flight data set.

EDL↗

The Instrumented Walking and Turning Test to Evaluate Suited Gait Dynamics and Performance in Extravehicular Activity Training Environments

Background and aims: Walking will be required for many exploration tasks on the Moon during the Artemis program. Walking in a straight line on the confined floorspace of a testing area, and repetitive treadmill walking that requires no change in direction may not adequately reflect the balance and coordination required during ambulation. Also, performance of turning maneuvers may be affected differently in different extravehicular activity (EVA) training facilities that simulate partial gravity. For example, the Neutral Buoyancy Lab (NBL) simulates lunar gravity by adding weight to underwater subjects to alter buoyancy and achieve the equivalent ground reaction force of 1/6 of Earth’s gravity (1/6G), whereas the Active Response Gravity Offload System (ARGOS) uses a computer controlled overhead suspension system programmed to continuously offload a percentage of a subject’s weight to simulate 1/6G. The degree to which dynamic movements such as turning are comparable across these EVA training facilities has not yet been evaluated. The instrumented gait test helps NASA scientists and engineers evaluate gait dynamics and performance in suited conditions, and this test demonstrates the unique characteristics and limitations of EVA training facilities. We developed an instrumented walking and turning test using inertial measurement units (IMUs) and conducted the test at NASA’s EVA training facilities. Results were used to compare suited walking and turning characteristics in the ARGOS and the NBL. Methods: Subjects donned the Mark III space suit during offloading with the ARGOS spreader bar gimbal and donned the Z2.5 space suit while underwater in the NBL with weights and floatation added to achieve realistic suit center of gravity. The test team securely attached three Opal (APDM, OR, USA) wireless IMUs on the space suit for each test run: one on the middle of the hard upper torso, and one on the left and on the right ankle bearings. During the NBL tests, the IMUs were encased in a waterproof housing (GoPro) with foam added to create a tighter fit. At both testing facilities, 6.3 m x 1.0 m (LxW) walking lines were marked, and a cone for turning or walking around was located at the end of the walking path with another line on the other side of the cone to indicate the stopping point after walking around the cone. Under simulated 1/6G, subjects began by standing at the marked line with their arms folded across the chest, they then walked at a preferred speed along the straight walking path until they reached the end, turned 180 degrees around the cone, and finally stopped at the marked stopping point. All IMU data recorded during testing were automatically saved to the internal memory. Then, raw IMU signals were processed using custom MATLAB (Mathworks, MA, USA) code to compare gait parameters during both the walking and the turning components of the task. These parameters included time (s), speed (m/s for walking and rad/s for turning), step number (n) and walk:turn time ratio (% time spent straight walking versus turning). Results: Less time, faster gait, fewer steps, and higher walk:turn ratio during both walking and turning components were exhibited during tests performed at the ARGOS versus those performed at the NBL. During the NBL tests, the slower walking speed continued at the same rate throughout a U-shape turn. During the ARGOS tests, the subjects performed shorter and tighter turns at 4 times the speed of the NBL turns because they walked 30% faster and the vertical offloading system gave them more support. Conclusion: Our data show that the differences in walking and turning parameters during the NBL tests may be due to the high viscosity in the water environment where the motion of the lower limbs was slow and did not reach full flexion and extension. These tests improve the current knowledge of testing environments in preparation for EVAs on the lunar surface.

Kyoung Jae Kim↗

Vision-Based Precision Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require perception systems for precision approach and landing systems (PALS) in urban, suburban, rural, and regional environments. The current state-of-the-art methods approved for automated approach and landing will be difficult to utilize in support of AAM operational concepts. However, there are technology and systems from other applications and lower-TRL research that use vision, IR, radar, and GPS methods to provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL to demonstrate a closed-loop baseline controller while adhering to the Federal Aviation Administration requirements and regulations. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter. Combining IMU with vision creates a sensor fusion navigation solution for GPS-denied environments. The state estimate leads to glideslope and localizer error computations, which will be pertinent for designing and deriving guidance laws and control laws for AAM PALS. The IMU and vision navigation solution provides promising simulation results for AAM PALS, and higher fidelity simulations will include computer graphics rendering and feature correspondence.

Evan Kawamura↗

Distributed Sensing and Computer Vision Methods for Advanced Air Mobility Approach and Landing

Advanced Air Mobility (AAM) aircraft require precision approach and landing systems (PALS) in several types of environments such as urban, suburban, and rural. It is difficult to implement current state-of-the-art methods approved for automated approach and landing for AAM operations. However, existing technology and systems that use vision, IR, radar, and GPS methods provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL and computer vision feature correspondence methods to demonstrate a baseline navigation system while adhering to the Federal Aviation Administration requirements and regulations. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines IMU with vision to create a sensor fusion navigation solution for GPS-denied environments. The state estimate leads to glideslope and localizer error computations, which will be pertinent for designing and deriving guidance laws and control laws for AAM PALS. The IMU and vision navigation solution provides promising simulation results for AAM PALS. This paper builds on previous work by incorporating high fidelity simulations with computer graphics rendering to demonstrate a distributed sensor network to track an AAM aircraft during approach and landing to compare with the aircraft's onboard navigation solution.

Evan Kawamura↗

Evaluating Sensory Augmentation as A Non-Pharmaceutical Tool to Mitigate Motion Sickness and Enhance Sensorimotor Task Performance: A Pilot Study Using Simulated Capsule Wave Motion

Wave motion during capsule recovery operations can result in motion sickness and performance decrements exacerbating reentry sickness following long duration spaceflight. The purpose of this pilot study was (1) to validate a capsule wave motion simulation as a platform to evaluate motion sickness countermeasures and (2) to evaluate a sensory augmentation belt providing vibrotactile feedback of gravitational upright. Ten healthy subjects ages 38.0 ± 10.1 (6M|4F) were exposed to complex wave motions on a six degree-of-freedom platform that included pitch, roll, and heave at provocative stimulus frequencies (0.1-0.25 Hz) while seated in an illuminated cabin deprived of external visual cues. Subjects reported acute symptoms for up to three consecutive 15 min trials or until they reached a motion sickness endpoint of 8 pts on the Pensacola Diagnostic Index (PDI). Five subjects were randomly assigned to the Sensory Augmentation (SA) group while the other five served as controls (CN group). Based on a Motion Sickness Susceptibility Questionnaire, the two groups had similar motion sickness histories (susceptibility percentile ranking of CN group = 27.5 ± 63.6 in the CN group versus 27.5 ± 37.0 in the SA group, median ± IQR). The vibrotactile feedback consisted of a single array of 8 electromechanical tactors positioned around the torso on an adjustable belt (Engineering Acoustics, Inc) that utilized an integrated inertial measurement unit (IMU) to indicate the direction of upright (e.g., subject’s back tactor on during forward tilt). During each wave motion trial subjects performed a battery of four different tasks: tracking Earth vertical using a joystick with and without a secondary task (Paced Auditory Serial Addition Test), an eye-hand target acquisition task on a cabin-fixed tablet, and the psychomotor vigilance test (PVT). All ten subjects reported varying levels of motion sickness with 6 of 10 reaching a symptom endpoint. Interestingly, subjects anecdotally reported that engagement in the joystick tracking task was less provocative than tasks involving the cabin-fixed tablet or periods of no activity. Sensory augmentation appeared to delay symptom onset, with 2 of 5 subjects reaching an endpoint within the first 15 min trial in the CN group versus none in the SA group (PDI after 15 min = 6.0 ± 2.5 in the CN group versus 3.4 ± 2.5 in the SA group, mean ±std). Sensory augmentation also improved performance on the joystick tracking task at lower stimulus frequencies (0.1 Hz in roll and 0.2 Hz in pitch). Both CN and SA groups maintained a consistent level of performance on the eye-hand target acquisition and PVT throughout the baseline (no motion) and wave motion periods. Our results validated that the simulated capsule wave motion paradigm provides an effective motion sickness stressor. This paradigm is currently being used to investigate the efficacy of intranasal scopolamine to mitigate motion sickness. Sensory augmentation using vibrotactile feedback appears to improve spatial awareness and delay symptom onset during complex passive motion. One advantage of this portable belt design is that it incorporates all tactor drive and IMU circuitry and therefore could continue to be worn by crewmembers and serve as a balance aid during egress and ambulation with recovery operations.

A M Bollinger↗

VSLAM and Vision-based Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft have many challenges in landing accurately and safely in urban, suburban, and rural environments. Localization in large and open rural environments could utilize GPS, but AAM aircraft in urban environments will encounter GPS degradation. Another challenge involves flight operation time, i.e., flying during the day or at night. There are different guidelines, landmarks, and landing light configurations at runways, heliports, and vertiports for daytime and nighttime applications. Tailoring feature detection methods for AAM approach and landing during the day and night pose different issues and challenges. It is easier to detect edges, lines, and other runway markers during the day than at night. Conversely, it is easier to see landing light configurations and patterns at nighttime than daytime. Consequently, utilizing the same feature detector for daytime and nighttime operations may not be feasible. This paper focuses on a vision-based precision approach and landing (PAL) by comparing ORB SLAM 2, a Vision Simultaneous Localization and Mapping (VSLAM) algorithm, and a novel EKF that combines onboard IMU measurements with coplanar pose from orthography and scaling with iterations (COPOSIT). Conducting unmanned aerial system (UAS) flight tests at NASA Armstrong Flight Research Center (AFRC) with landmarks and fiducials distributed around the landing zone provides a simulated AAM approach and landing data to test vision-based PAL methods to provide Alternative Position, Navigation, and Timing (APNT) solutions for AAM PAL applications. The novel vision-based PAL EKF with IMU and COPOSIT provides accurate state estimation when distributed landmarks and fiducials are in the field of view.

distributed sensing↗

Surface Gravimetry Using Rover Navigation Systems

This prototype seeks to demonstrate the utility of repurposing a Micro-ElectroMechancical (MEMS) Inertial measurement Unit (IMU) to perform surface gravimetry on a rover. Gravimetry is a common analytical tool used for probing density distributions in the subsurface of a planetary body. Historically, extraterrestrial gravimetry has been confined to orbital platforms. While orbital surveys allow for the construction of global gravity models, the spatial resolution of the data is constrained by the platform’s orbital altitude and high inherent speed. Data collected at or near the surface would increase spatial resolution and allow finer-scale crustal structure to be resolved. To date, there have been only two extraterrestrial surface gravity surveys: the Apollo 17 Traverse Gravimeter Experiment and a survey using the MEMS accelerometers contained within the Curiosity rover’s IMUs. The Curiosity survey highlighted the potential of using MEMS technology to perform planetary gravimetry, albeit with lower sensitivities than traditional surface gravimeters. MEMS accelerometers are included on every rover platform as part of the IMU navigation systems. MEMS accelerometers have low mass, cost, and power requirements while being robust across a range of environments, whereas traditional gravimeters are fragile, costly, and relatively massive (≥8kg versus ≈50g for MEMS IMUs). Thus, the emergence of MEMS gravimeters provides a low-risk and cost-effective method for performing planetary surface gravimetry [3,4]. Here, we present a method to recalibrate the MEMS accelerometers in rover IMUs to collect gravimetric measurements. Such measurements could assist current and future rover missions and support wider efforts to mature MEMS gravimeters.

C S Lawson↗