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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Safe2Ditch Steer-To-Clear Development and Flight Testing

This paper describes a series of small unmanned aerial system (sUAS) flights performed at NASA Langley Research Center in April and May of 2019 to test a newly added Steer-to-Clear feature for the Safe2Ditch (S2D) prototype system. S2D is an autonomous crash management system for sUAS. Its function is to detect the onset of an emergency for an autonomous vehicle, and to enable that vehicle in distress to execute safe landings to avoid injuring people on the ground or damaging property. Flight tests were conducted at the City Environment Range for Testing Autonomous Integrated Navigation (CERTAIN) range at NASA Langley. Prior testing of S2D focused on rerouting to an alternate ditch site when an occupant was detected in the primary ditch site. For Steer-to-Clear testing, S2D was limited to a single ditch site option to force engagement of the Steer-to-Clear mode. The implementation of Steer-to-Clear for the flight prototype used a simple method to divide the target ditch site into four quadrants. An RC car was driven in circles in one quadrant to simulate an occupant in that ditch site. A simple implementation of Steer-to- Clear was programmed to land in the opposite quadrant to maximize distance to the occupant’s quadrant. A successful mission was tallied when this occurred. Out of nineteen flights, thirteen resulted in successful missions. Data logs from the flight vehicle and the RC car indicated that unsuccessful missions were due to geolocation error between the actual location of the RC car and the derived location of it by the Vision Assisted Landing component of S2D on the flight vehicle. Video data indicated that while the Vision Assisted Landing component reliably identified the location of the ditch site occupant in the image frame, the conversion of the occupant’s location to earth coordinates was sometimes adversely impacted by errors in sensor data needed to perform the transformation. Logged sensor data was analyzed to attempt to identify the primary error sources and their impact on the geolocation accuracy. Three trends were observed in the data evaluation phase. In one trend, errors in geolocation were relatively large at the flight vehicle’s cruise altitude, but reduced as the vehicle descended. This was the expected behavior and was attributed to sensor errors of the inertial measurement unit (IMU). The second trend showed distinct sinusoidal error for the entire descent that did not always reduce with altitude. The third trend showed high scatter in the data, which did not correlate well with altitude. Possible sources of observed error and compensation techniques are discussed.

Petty, Bryan J.↗
NASA NTRS2020

In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

Owing to the frequency of occurrence and high risk associated with bearings, identification and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, amongst which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance or bearing faults. Such anomalies may not only pose risks to UAV's internal circuitry, components or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Portia Banerjee↗
NASA NTRS2004

Path following using visual odometry for Mars Rover in high-slip environments.

An architecture for autonomous operation of Mars rovers in high slip environments has been designed, implemented, and tested. This architecture is composed of several key technologies that enable the rover to accurately follow a designated path, compensate for slippage, and reach intended goals independent of the terrain over which it is traversing (within the mechanical constraints of the mobility system). These technologies include: visual odometry, full vehicle kinematics, a Kalman filter, and a slop compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs using a maximum likelihood motion estimation algorithm. The full vehicle kinematics for a rocker-bogie suspension system estimates motion, with a no-slip assumption, by measuring wheel, rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an Inertial Measurement Unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determining (tracking into account estimate uncertainties) if and how much slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used, in conjunction with the inverse kinematics, to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.

Roumeliotis, Stergios I.↗
NASA NTRS2013

A hybrid FPGA/Tilera compute element for autonomous hazard detection and navigation

To increase safety for future missions landing on other planetary or lunar bodies, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is developing an integrated sensor for autonomous surface analysis and hazard determination. The ALHAT Hazard Detection System (HDS) consists of a Flash LIDAR for measuring the topography of the landing site, a gimbal to scan across the terrain, and an Inertial Measurement Unit (IMU), along with terrain analysis algorithms to identify the landing site and the local hazards. An FPGA and Manycore processor system was developed to interface all the devices in the HDS, to provide high-resolution timing to accurately measure system state, and to run the surface analysis algorithms quickly and efficiently. In this paper, we will describe how we integrated COTS components such as an FPGA evaluation board, a TILExpress64, and multi-threaded/multi-core aware software to build the HDS Compute Element (HDSCE). The ALHAT program is also working with the NASA Morpheus Project and has integrated the HDS as a sensor on the Morpheus Lander. This paper will also describe how the HDS is integrated with the Morpheus lander and the results of the initial test flights with the HDS installed. We will also describe future improvements to the HDSCE.

Trawny, Nikolas↗
NASA NTRSDate not supplied

Overview of the Dragonfly Entry Aerosciences Measurements (DrEAM) Suite

NASA Ames and Langley arepartnering with DLR to propose acomprehensive instrumentation suite known as the Dragonfly Entry Aerosciences Meas-urements (DrEAM). DrEAM being the first competed mission to fly EDL instrumentation as part of NASA’s Engineering Science Investigation (ESI).DrEAM will provide key aerothermodynamicdata and performance analysisfor Dragonfly’s forebody and backshell therma lprotection system (TPS),and also includes a DLR-provided Data Acquisition System(DAS).Titan’s atmosphere predominantly consists of nitro-gen (~98% by mole) with small amounts of methane(~2% by mole)and other trace gases. CN is a strong ra-diator and is found in nonequilibriumconcentrationsfor Titan entry. The accurate modeling of nonequilibrium CN radiation has proven to be a difficult task. Prompted by the Huygensmission, many experimental campaigns and analyses were performed to better understand the aerothermal environments experience by the probe dur-ing Titan entry[1].However, the Huygens probe carried no heatshieldinstrumentation. Therefore,the DrEAM instrumentation suite will significantly advance the state-of-the-art not only by documenting theenviron-ment and performance of Dragonfly’sentry system but also by making keymeasurements in Titan’s atmos-phere for thefirst time, thus providing new benchmark dataapplicable to entry science more generally. Current Measurement Goals: Aerothermal envi-ronments and TPS responsewill be measured using sen-sors similarto the Mars Entry, Descent, and Landing In-strumentation2 (MEDLI2) Integrated Sensor Plug(MISP) and the COMbined Aerothermaland Radiome-ter Sensor (COMARS)suite[2], with the latter supplied by DLR.For MEDLI2, MISP usedembedded thermo-couples (TCs) todirectly measure in-depth temperature of theTPS at several locations,which can also be used to infer surface environmentsvia inverse analysis. For DrEAM, the MISPstyle plugswill be known as Drag-onfly Sensors for Aero-Thermal Reconstruction (Drag-STR)plugs. On Schiaparelli, the COMARSsuite in-cludedthree total surface-mounted heatflux sensors, three pressure sensors, and one radiometer. For DrEAM, the COMARS package will be known as COmbined Sensor System for Titan Atmosphere (COSSTA). Atmospheric density measurements and capsuleaerodynamic data will be obtained throughthe onboard Inertial Measurement Unit (IMU),supple-mented by pressure transducers similar tothose used by the MEDLI Mars Entry AtmosphericData System (MEADS).The DrEAM pressure sensors will be known as Dragonfly Atmospheric Flight Transducers(DrAFT) Both DragSTR and DrAFThave flight heritagefrom MISP and MEADS on the MSLand Mars 2020mis-sions, and the COMARS suite successfullyflew on the ESA Schiaparelli EDMlander. A preliminary layout of the sensors is shown in Fig. 1. Because Dragonflyuses the same aeroshell provider (i.e., LockheedMartin) and materials for the TPS,with what are expected to be sim-ilar thicknesses as MSL and Mars 2020on both the heat shield and backshell, theDrEAMinstrumentation will look to utilize the same techniques and processes as de-veloped byMEDLI and MEDLI2for vehicle integra-tion. This commonality alsoenables DrEAMto lever-age the extensiveground test qualifications performed forMEDLI and MEDLI2 and claim substantial heritagefor this system.

A Brandis↗
NASA NTRSDate not supplied

IMUFDIR Tuning In Response To Structural Deformation Of The Orion Capsule

During system level Thermal Vacuum (TVAC) testing it was discovered that the pressure differential of the crew cabin in a vacuum would deflect the capsule slightly, causing significant misalignment of Orion’s three Inertial Measurement Units (IMUs). This phenomenon has been likened to a balloon expanding. When this misalignment was modeled in simulation as a function of atmospheric pressure, it was determined that the rate of deflection on reentry was significant enough to cause a mis-compare of the angular rates measured by the IMUs. This mis-comparison of the rates caused Orion’s Fault Detection Isolation and Recovery (FDIR) algorithm to falsely fail one of the IMUs. This manuscript will discuss how the IMUFDIR algorithm was detuned to be insensitive to these ballooning effects, while still meeting FDIR requirements to capture real faults in an IMU. All testing to validate this detuning was performed in a Six Degree of Freedom (6 DOF) Monte Carlo Simulation Environment.

Nicholas Rahaim↗
NASA NTRSDate not supplied

A Modern Load Relief Guidance Scheme for Space Launch Vehicles

Launch vehicle load relief algorithms are concerned with realizing a reduction of transient bending moments near maximum dynamic pressure. Traditional approaches to load relief typically use inner-loop acceleration feedback to reduce the wind-induced angle of attack. When implemented in the inner loop, load relief bandwidth is necessarily limited by the achievable stability margins, and when acceleration feedback is employed, by the uncertainty associated with structural modes that couple with the body-mounted accelerometer. The structure of inner loop load relief increases the dimensionality of the flight control gain and filter optimization problem. Most importantly, classical load relief laws do not take advantage of high-rate and high-accuracy GPS-aided inertial velocity data that is readily available from modern strap down IMUs. In this paper, a novel load relief guidance scheme is described that uses direct angle-of-attack feedback in a clever mechanization. The steering commands are determined by examining the wind-perturbed dynamics of a launch vehicle with respect to a gravity turn ascent trajectory. An angle of attack estimate is derived from GPS-aided inertial data and pre-launch range wind measurements, and it is shown that a reduction worst-case rigid-body loads can be realized without requiring air data. The algorithm also includes a high-rate navigation data preprocessing scheme that operates directly on the IMU delta-theta and delta-velocity measurements in order to produce a filtered acceleration estimate at the vehicle center of mass. The outer-loop guidance scheme simplifies the design process for the classical inner-loop autopilot. Algorithm performance is demonstrated using Monte Carlo analysis of a representative liquid booster in a production high fidelity launch vehicle simulation.

NESC↗
NASA NTRS2020

Maximum Correntropy Kalman Filter for Orientation Estimation with Application to LiDAR Inertial Odometry

Robot navigation is a prerequisite to enable many autonomous robotic operations. Propioceptive inertial measurement units (IMUs) are widely used and commonly accepted sensing devices in robotic navigation. An IMU typically consists of two-triaxis sensors: an accelerometer and a gyroscope (gyro), measuring the accelerations (accelerated motion together with gravity) and angular velocities of the sensor, respectively. In addition, some IMUs incorporate a magnetic angular rate sensor, which is a triaxis magnetometer measuring the magnetic field of the Earth. In this work, we focus on these types of IMUs (comprised of accelerometer, gyro and magnetometer).

Agha-mohammadi, Ali-akbar↗
NASA NTRS2021

LiDAR-Inertial Based Navigation and Mapping for Precision Landing

Future lander missions will travel to ambitious, scientifically interesting locations near rough and dangerous terrain. They will need to operate with limited prior information about the terrain, and under varying lighting conditions. Landing safely and precisely in the face of these challenges is difficult for existing vision-based landing systems, which require detailed orbital reconnaissance, a priori hazard maps, and impose time-of-day restrictions on landing to ensure similar lighting conditions in orbital and descent imagery. Advanced 3D imaging LiDAR systems currently under development, and originally intended for single-scan hazard detection, have the potential to be operated continuously from altitudes of up to 5 km. Used together with existing inertial measurement units (IMUs), these sensors open a path-to-flight for a full navigation and mapping system, which could replace or augment a traditional landing sensor suite. A landing system based around these sensors can perform accurate altimetry, map-relative localization (MRL), LiDAR-inertial odometry, and map refinement in an illumination-insensitive manner, over unknown or partially known terrain. This paper outlines preliminary work on a LiDAR-inertial landing system that: estimates the spacecraft trajectory during entry, descent, and landing (EDL); and maps the topography of the terrain below, for future use in hazard detection and avoidance. An incremental, factor graph based, smoothing approach is used to solve for the maximum a posteriori trajectory of spacecraft states. Integrated IMU measurements and features tracked in adjacent range and intensity images are used to estimate motion (LiDAR-inertial odometry). LiDAR scans are binned into motion-corrected digital elevation models (DEMs), which are matched to an existing orbital topographic map to provide absolute position information (MRL). The estimated trajectory is then used to project the LiDAR scans into the map frame, creating a variable-resolution quadtree topographic map suitable for hazard detection and avoidance. Existing topographic maps from throughout the solar system (i.e., Earth, the Moon, Mars, Ceres, Vesta, Europa, Enceladus, and Eros) are upsampled for use in EDL simulations. The Mars 2020 Lander Vision System Simulator (LVSS) is extended to simulate LiDAR-inertial data for realistic EDL trajectories. Results of the algorithm operating on the simulated data are presented. Estimated spacecraft trajectory and refined map are compared to ground truth to assess estimation accuracy.

Katake, Anup↗
NASA NTRSDate not supplied

AstroLoc: An Efficient and Robust Localizer for a Free-flying Robot

We present AstroLoc, an efficient and robust monocular visual-inertial graph-based localization system used by the Astrobee free-flying robots onboard the International Space Station (ISS). We provide a novel localization system that limits the traditionally higher computation times for graph-based localization systems and enables the resource constrained Astrobee robots to benefit from their increased accuracy. We also introduce methods for handling cheirality issues for visual odometry and localization factors that further increase localization robustness. We evaluate the performance of AstroLoc on a dataset of ISS activities and show that it greatly improves pose, velocity, and IMU bias estimation accuracy while efficiently running in a limited computation environment. The source code for AstroLoc is released to the public.

Localization↗
NASA NTRSDate not supplied

Developing An Earth-Fixed Visual Reference to Aid Stability, Readaptation and Egress After a Water Landing

INTRODUCTION Water landings present the worst possible sensory conditions for crews trying to orient and stabilize themselves immediately after long-duration spaceflight. Of the three sensory feedback systems involved in maintaining stability (i.e., proprioceptive, vestibular, and visual), none will provide reliable orientation information under the current water landing scenarios. The proprioceptive and vestibular systems are affected during spaceflight by disuse and adaptation to microgravity. Vision may not suffer the same degradation, but the visual environment within the enclosed space of a capsule, or interior room of a recovery ship, is disorienting when subject to wave-induced motion. The result is an increased risk of fall-related injury. In a prior study, 70% (21 of 30) of nonimpaired subjects reported that the presence of an Earth-fixed horizontal line helped them stabilize when their visually enclosed environment was exposed to wave motion. In addition to aiding stability, sensory re-adaptation occurs when inputs from the three sensory systems are synchronized with one another and aligned with Earth’s gravity. The earlier an Earth-fixed visual reference can be introduced the sooner the readaptation process can begin. The goal of this project is to identify the optimal features of a device that visually presents gravitational reference cues to support stability and readaptation. METHODS The capsule sensory-condition simulator is a three-sided enclosure atop a six degree-of-freedom motion platform. A sum-of-sines equation is used to drive the motion platform, producing the simulated wave motion. The equation was derived using the frequency range present in inertial measurement unit (IMU) data collected during open-water Orion mock-up testing. The enclosure creates the nature of a floating visual environment where the walls move in relation to the standing surface. To more accurately represent the condition of postflight crewmembers, galvanic vestibular stimulation (GVS) is applied to disrupt normal vestibular function and standing on a compliant surface attenuates proprioceptive feedback. An instrumented handhold allows the subjects to stabilize themselves and serves as the primary dependent variable. Subjects are instructed to minimize handhold use so the higher forces applied to the handhold correspond to greater instability. Various forms of a visual reference have been explored. Passive systems, such as mechanical gimbals and weighted plumb bobs were eliminated from consideration because of the intertia-induced oscillations that the wave-motion causes. Laser lines presented as horizontal, vertical, or the combination of both are being used to provide the Earth-fixed visual reference. These are presented in the central visual field or periphery from laser sources that are mounted either from within or outside the visual enclosure. RESULTS Data collection for this study is currently in progress. CONCLUSIONS This study will determine whether a visual reference system can help improve stability during challenging sensory conditions and define the optimal chacteristics for such a system.

Brian T Peters↗
NASA NTRSDate not supplied

Simulated Vision-based Approach and Landing System for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require precision approach and landing systems (PALS) in several environments, such as urban, suburban, and rural. It is challenging to implement current state-of-the-art methods approved for automated approach and landing for AAM operations with challenges such as GPS degradation in urban environments and visual navigation aids like the glideslope and localizer being narrow and not allowing alternative incoming landing angles at vertiports. However, existing technology and systems, i.e., the instrument landing system (ILS) with glideslope and localizer indicators that use vision, IR, radar, or 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 about heliport design (FAA AC 150/5390-2C), which is one of the closest references for vertiport requirements and regulations. The coplanar pose from orthography and scaling with iterations (COPOSIT) algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines IMU with vision to create a vision-based approach and landing (VAL) sensor fusion navigation solution for GPS-denied environments. The VAL navigation solution provides promising simulation results for AAM PALS with Hough circle detection and feature correspondence, which demonstrate robustness to false positives. This paper incorporates moderately high- fidelity simulations with computer graphics rendering to show a distributed sensor network to track an AAM aircraft during approach and landing to compare with the aircraft’s onboard vision-based navigation solution.

distributed sensing↗
NASA NTRS2023

Extended Kalman Filter Performance on the Artemis-1 Mission

The Artemis Program is NASA’s campaign to explore the Moon and beyond. Artemis-1, the uncrewed exoLEO test flight of the Orion spacecraft, was completed in 2022. There were four navigation Extended Kalman Filters (EKFs) that are part of the Orion navigation system. The Atmospheric Extended Kalman Filter (ATMEKF) estimates the vehicle position, velocity, and attitude (referred to as the vehicle state) during the ascent and entry phases of flight. Once Orion is outside of Earths atmosphere, the Earth Orbit Extended Kalman Filter (EOEKF) and CisLunar Extended Kalman Filter (CLEKF) estimate the translational states, depending on the phase of flight, while the Attitude Extended Kalman Filter (ATTEKF) estimates the rotational state of the vehicle. The Kalman filters propagate the vehicle state forward in time using a combination of dynamics models and the output data from the Inertial Measurement Unit (IMU). The filters update the vehicle states and associated uncertainties, in the form of the covariance matrix, using pseudorange measurements from GPS (in ATMEKF/EOEKF), optical navigation measurements of the Earth or Moon (in CLEKF), and star tracker measurements (in ATTEKF). Simultaneously, the Kalman filters estimate error sources in the sensors, which are included in the state vectors as Exponentially Correlated Random Variables (ECRVs). This paper will summarize the performance of these filters during the Artemis-1 mission.

Artemis-1↗
NASA NTRS2021

Mars 2020 Perseverance Entry Controller Design and Flight Reconstruction

On February 18th, 2021, NASA landed Perseverance on the Jezero crater (on Mars). An entry Guidance, Navigation, and Control (GNC) system delivered the vehicle to the desired landing ellipse. The navigation filter propagated position and attitude states initialized from cruise using Inertial Measurement Unit (IMU) measurements. The entry guidance modulated the lift vector through bank commands to reach the parachute deploy conditions. The entry controller commanded the propulsive Reaction Control System (RCS) to track the bank commands while doing rate damping on angle-of-attack and sideslip. This paper describes the design and the as-flown performance of the entry controller.

Way, David W.↗
NASA NTRS2022

Camera Simulation for the Perseverance Rover’s Lander Vision System

On February 18, 2021, the Perseverance Rover safely landed on Mars at Jezero Crater. Part of the successful landing was due to the Lander Vision System (LVS), which takes descent images from the LVS Camera (LCAM) and IMU measurements and estimates the lander position relative to a map of the Jezero landing site. The LVS Simulation LCAM (LVSS LCAM) model is an image rendering program developed to test the LVS in a variety of scenarios to ensure performance amid uncertainty. The LVSS LCAM model includes a pointing misalignment model, an exposure timing model, shadowing, a terrain reflectance model, atmospheric attenuation from dust, and sensor effects. This model was used for performance analysis, verification, and validation of the LVS algorithms in a Mars-like simulation prior to landing. This paper describes the LVSS LCAM rendering algorithm and compares flight images from LVS operation during the Perseverance landing with their rendered counterparts.

Zheng, Jason↗
NASA NTRS2022

Mars 2020 Lander Vision System Flight Performance 1

The Mars 2020 Entry Descent and Landing (EDL) system delivered the Perseverance rover to the surface of Mars on February 18th, 2021. A large fraction of the Jezero Crater landing site was covered with landing hazards including cliffs, inescapable dune fields and rocks. These hazards were identified or inferred using orbital imagery before launch so that they could be avoided using Terrain Relative Navigation (TRN) which was composed of two parts: the Lander Vision System (LVS) and Safe Target Selection (STS). During EDL, the LVS successfully estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with Inertial Measurement Unit (IMU) data. This position estimate was used by STS to identify the safest target for landing that was also reachable given fuel and other constraints. The EDL system then used the powered descent phase to retarget to this location and land safely. The overall error between the targeted location and actual landing location was 5m which was an order of magnitude less than the 60m touchdown error requirement. This paper will describe the final tests of the LVS before launch, the checkout of the LVS during operations and the LVS performance during EDL.

Zheng, Jason↗
NASA NTRSDate not supplied

Overview and Performance of the LOFTID Instrumentation Suite

NASA’s Hypersonic Inflatable Aerodynamic Decelerator (HIAD) is an enabling technology that facilitates atmospheric entry of heavy payloads to planets such as Earth and Mars using a deployable aeroshell. The deployable nature of the HIAD technology allows it to overcome the size constraints imposed on current rigid aeroshell entry systems. This enables use of larger aeroshells resulting in increased entry system performance (e.g. higher payload mass and/or volume, higher landing altitude at Mars). On November 10th, 2022 the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) was launched out of Vandenberg Air Force Base as a secondary payload on an Atlas V rocket. After the primary payload was delivered to its orbit, the LOFTID reentry vehicle was inflated, positioned, and then separated to reenter Earth’s atmosphere at a velocity of 8.1km/s, ultimately splashing down safely in the Pacific Ocean. The flight successfully demonstrated a 6m diameter, 70-deg sphere-cone HIAD on a high-energy orbital reentry. This demonstration has provided invaluable fight data essential to characterize the vehicle performance and support the ongoing effort to further scale the HIAD technology to vehicles of 10m in diameter or greater. Aeroshells of this scale are applicable to near-term commercial applications and future NASA robotic and human exploration missions. LOFTID incorporated an extensive instrumentation suite totaling over 150 science measurements. This included thermocouples, total heat flux sensors, and a radiometer to characterize the aeroheating environment and aeroshell thermal response. An Inertial Measurement Unit (IMU), Global Positioning System (GPS), and flush air data system was included to allow post-flight reconstruction of the vehicle trajectory including a decoupling of the aerodynamics from the atmospheric density. Loadcells were used to measure HIAD structural response during entry, and cameras (both visual-spectrum and infrared) were mounted on the aft segment looking at the aeroshell to monitor structural deflection and surface temperature distribution. Finally, a single up-look camera was included which has provided a surprising amount of science potential from the spectacular footage. In addition to the primary instrumentation suite, a new Fiber Optic Sensing System (FOSS) was used to provide global temperature distributions as a technology demonstration. The system provided over 200 thermal measurements creating a thermal map for the backside of the nose Flexible Thermal Protection System. Another pair of FOSS cables were run along the rigid center structure and measured the temperature response to the vehicle wake environment. The LOFTID instrumentation suite leveraged Agency-wide expertise, with hardware development occurring at Ames Research Center, Langley Research Center, Marshall Space Flight Center, and Armstrong Flight Research Center. This presentation will discuss the instrumentation selected for LOFTID to capture the HIAD performance during the high-energy orbital reentry flight test, provide examples of data products, and snippets of the spectacular reentry video.

G T Swanson↗
NASA NTRSDate not supplied

Overview and Performance of the LOFTID Instrumentation Suite

NASA’s Hypersonic Inflatable Aerodynamic Decelerator (HIAD) is an enabling technology that facilitates atmospheric entry of heavy payloads to planets such as Earth and Mars using a deployable aeroshell. The deployable nature of the HIAD technology allows it to overcome the size constraints imposed on current rigid aeroshell entry systems. This enables use of larger aeroshells resulting in increased entry system performance (e.g. higher payload mass and/or volume, higher landing altitude at Mars). On November 10th, 2022 the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) was launched out of Vandenberg Air Force Base as a secondary payload on an Atlas V rocket. After the primary payload was delivered to its orbit, the LOFTID reentry vehicle was inflated, positioned, and then separated to reenter Earth’s atmosphere at a velocity of 8.1km/s, ultimately splashing down safely in the Pacific Ocean. The flight successfully demonstrated a 6m diameter, 70-deg sphere-cone HIAD on a high-energy orbital reentry. This demonstration has provided invaluable fight data essential to characterize the vehicle performance and support the ongoing effort to further scale the HIAD technology to vehicles of 10m in diameter or greater. Aeroshells of this scale are applicable to near-term commercial applications and future NASA robotic and human exploration missions. LOFTID incorporated an extensive instrumentation suite totaling over 150 science measurements. This included thermocouples, total heat flux sensors, and a radiometer to characterize the aeroheating environment and aeroshell thermal response. An Inertial Measurement Unit (IMU), Global Positioning System (GPS), and flush air data system was included to allow post-flight reconstruction of the vehicle trajectory including a decoupling of the aerodynamics from the atmospheric density. Loadcells were used to measure HIAD structural response during entry, and cameras (both visual-spectrum and infrared) were mounted on the aft segment looking at the aeroshell to monitor structural deflection and surface temperature distribution. Finally, a single up-look camera was included which has provided a surprising amount of science potential from the spectacular footage. In addition to the primary instrumentation suite, a new Fiber Optic Sensing System (FOSS) was used to provide global temperature distributions as a technology demonstration. The system provided over 200 thermal measurements creating a thermal map for the backside of the nose Flexible Thermal Protection System. Another pair of FOSS cables were run along the rigid center structure and measured the temperature response to the vehicle wake environment. The LOFTID instrumentation suite leveraged Agency-wide expertise, with hardware development occurring at Ames Research Center, Langley Research Center, Marshall Space Flight Center, and Armstrong Flight Research Center. This paper will discuss the instrumentation selected for LOFTID, a summary of sensor in-flight performance, and will provide examples of data products from the post-flight analysis effort.

Gregory T Swanson↗