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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR System: Overview and Applications.

Improved terrain characterization and navigation sensors and methods are needed to enhance crew safety, ISRU return, and scientific understanding of future landing sites. Specific to the Artemis Program and sustained exploration at the lunar South Pole, extreme low-angle solar illumination conditions pose significant challenges to existing photogrammetry-based robotic navigation. Additionally, a major challenge for navigation on the Moon and other planetary surfaces is the lack of Global Positioning and Navigation Systems (GPS or GNSS). Thus, there is a need for an alternative to image-based navigation that allow for precise and accurate mapping in GPS-denied environments on any planetary body. Here, we describe the Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR system; a backpack-mounted, mobile navigation and terrain mapping system that uses a velocity-sensing coherent light detection and ranging (LiDAR) system based on a frequency modulated continuous wave (FMCW) technique, contains minimal moving parts, and employs sophisticated positioning algorithms. During a traverse, this instrument emits light pulses to continually scan a scene to build a three-dimensional point cloud representation of topography. A measure of the Doppler-velocity at each of millions of range points sampled per second allows for a 6 degree of freedom (6- DoF) estimate of the sensor’s position and the development of novel position-from-velocity mapping and positioning algorithms for loop-closure in GPS denied environments. Included with paper is the video presentation for the Figure 2: FMCW-LiDAR sensor on Kinematic Navigation and Cartography Knapsack (KNaCK) (Aeva Aeries 1)

M. Zanetti↗
NASA NTRSDate not supplied

Mobile Lidar as A Tool for Terrestrial and Planetary Cave Exploration and Mapping

The Kinematic Navigation and Cartography Knapsack (KNaCK) team at NASA’s Marshall Space Flight Center in Huntsville, Alabama is developing tools that enable ultra-high resolution terrain mapping and navigation using mobile LiDAR (Light Detection and Ranging) and SLAM (Simultaneous Localization and Mapping) algorithms in fully GPS-denied and unilluminated environments [1,2]. The backpack mounted LiDAR instrument under development by our team demonstrates the potential of mobile SLAM LiDAR for use in challenging lunar and planetary surface environments as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.

Lunar Skylight↗
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SlLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile LiDAR (Light Detection and Ranging) system. It can map its surroundings in 3 dimensions and localize itself in space. The project is exploring how LiDAR can advance terrain mapping and navigation at the lunar south pole. KNaCK is lead by Dr. Michael Zanetti of NASA MSFC’s Heliophysics and Planetary Science Branch. - KNaCK serves as - A test article for GPS denied mapping and navigation. - A test bed for SLAM (Simultaneous Localization and Mapping) algorithms. - A test bed for commercial LiDAR units. - A tool for terrestrial science.

LiDAR↗
NASA NTRSDate not supplied

Caves as Terrestrial Analogs for Mapping and Exploration in Planetary Surface and Subsurface Environments.

The Kinematic Navigation and Cartography Knapsack (KNaCK) team is developing tools that will enable ultra-high resolution terrain mapping and navigation at the lunar surface. KNaCK uses mobile light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM) algorithms to operate in fully GPS-denied and unilluminated environments. Testing in relevant analogs is key to perfecting this technology for planetary applications. After utilizing desert and volcanic environments for multiple tests, the team is now using caves as an additional proving ground. Tests in terrestrial caves have demonstrated the potential of mobile SLAM LiDAR for use in challenging surface environments such as steep walled craters and permanently shadowed regions as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.

LiDAR↗
DOE OSTI2022

Autonomous aerial flight path inspection using advanced manufacturing techniques

Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.

42 ENGINEERING↗
NASA NTRS2015

Precision Time Protocol-Based Trilateration for Planetary Navigation

Progeny Systems Corporation has developed a high-fidelity, field-scalable, non-Global Positioning System (GPS) navigation system that offers precision localization over communications channels. The system is bidirectional, providing position information to both base and mobile units. It is the first-ever wireless use of the Institute of Electrical and Electronics Engineers (IEEE) Precision Time Protocol (PTP) in a bidirectional trilateration navigation system. The innovation provides a precise and reliable navigation capability to support traverse-path planning systems and other mapping applications, and it establishes a core infrastructure for long-term lunar and planetary occupation. Mature technologies are integrated to provide navigation capability and to support data and voice communications on the same network. On Earth, the innovation is particularly well suited for use in unmanned aerial vehicles (UAVs), as it offers a non-GPS precision navigation and location service for use in GPS-denied environments. Its bidirectional capability provides real-time location data to the UAV operator and to the UAV. This approach optimizes assisted GPS techniques and can be used to determine the presence of GPS degradation, spoofing, or jamming.

Murdock, Ron↗
NASA NTRS2018

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

Wedescribeanew,on-board,shortrangeperceptionsystem that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

Matthies, Larry↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
DOE OSTI2026

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗
NASA NTRS2012

Localization Using Visual Odometry and a Single Downward-Pointing Camera

Stereo imaging is a technique commonly employed for vision-based navigation. For such applications, two images are acquired from different vantage points and then compared using transformations to extract depth information. The technique is commonly used in robotics for obstacle avoidance or for Simultaneous Localization And Mapping, (SLAM). Yet, the process requires a number of image processing steps and therefore tends to be CPU-intensive, which limits the real-time data rate and use in power-limited applications. Evaluated here is a technique where a monocular camera is used for vision-based odometry. In this work, an optical flow technique with feature recognition is performed to generate odometry measurements. The visual odometry sensor measurements are intended to be used as control inputs or measurements in a sensor fusion algorithm using low-cost MEMS based inertial sensors to provide improved localization information. Presented here are visual odometry results which demonstrate the challenges associated with using ground-pointing cameras for visual odometry. The focus is for rover-based robotic applications for localization within GPS-denied environments.

Swank, Aaron J.↗
NASA NTRSDate not supplied

Voltage-Controlled Differential Faraday Rotation from FMNWs and Magnetoelastic Silicone-Nickel Composites

Differential Faraday rotation has been observed from strain-Differential Faraday rotation has been observed from strain-modulated magnetoelastic membranes using millimeter-wave polarimetry at 61.25 GHz. The membranes, consisting of aligned chains of nickel micro-particles embedded in silicone rubber, were manufactured using a roll-to-roll electromagnetic processing line, mounted onto piezoelectric ceramic annuli, and immersed in externally-generated static magnetic fields. Experiments were conducted in which the membranes were periodically stretched by operating the piezoelectric crystals close to their radial resonant frequencies—thereby modulating nickel particle density as well as magnetic material properties. A customized quasi-optical apparatus was used to determine the Faraday-rotation difference between the strained and unstrained states of the magnetoelastic membranes. Possible applications that could emerge from this polymer-based technology include nonreciprocal millimeter-wave components with voltage-controlled frequency response and bio-inspired geomagnetic sensors for GPS-denied navigation. Investigations were conducted to estimate the maximum attainable differential Faraday rotation and the minimum detectable static magnetic field.

nickel↗
NASA NTRS2022

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↗
NASA NTRSDate not supplied

Topographic Quintet: Comparing Five Methods for Measuring Ultra-High Resolution Topography

We compare different methods for collecting ultra-high resolution topography data within an analog planetary, human landing site scale area. Our aim is to investigate the cost and benefits of different 3D terrain mapping techniques, their associated data collection methods, and how their different specifications (e.g., range, spatial resolution, scanning-time, mobility, operating constraints, GPS-Denied operation, etc.) might be applied to landing-site characterization and mission operations. We compare 3D terrain data collected during a field campaign in November 2021 from an outcrop at Kilbourne Hole in southern New Mexico using different Light Detection and Ranging (LiDAR) sensors on the ground and stereo-derived 3D data from framing cameras mounted on small uncrewed aerial systems (sUAS).Our foci for this experiment are ground-based, surveying, and autonomous vehicle-type 3D scanning sensors that might be used for planetary surface exploration from landed assets (e.g., lander, rover, astronaut-mounted sensors, decent imaging, hoppers, or drones).[e.g. 1]This test is not meant to benchmark these scanners against one another, nor provide a recommendation for a specific make or model. Rather, our goal is to quantify time, effort, resolution, and operational trade-offs that are important for selecting a topographic instrument/methodology for a given scope of terrain characterization. Our results indicate that each technique is capable of exceptional quality terrain characterization for planetary exploration and scientific inquiry, but we hypothesize the appropriate technique is highly dependent on the scope of operational specifications and science requirements.

P Whelley↗
NASA NTRS2021

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

We propose a framework for resilient autonomous navigation in perceptuallychallenging unknown environments with mobility-stressing elements such asuneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffsand holes, and narrow passages. Environments are GPS-denied and perceptuallydegradedwith variable lighting from dark to lit and obscurants (dust, fog, smoke).Lack of prior maps and degraded communication eliminates the possibility of prioror off-board computation or operator intervention. This necessitates real-time onboardcomputation using noisy sensor data. To address these challenges, we proposea resilient architecture that exploits redundancy and heterogeneity in sensing modalities.Further resilience is achieved by triggering recovery behaviors upon failure.We propose a fast settling algorithm to generate robust multi-fidelity traversabilityestimates in real-time. The proposed approach was deployed on multiple physicalsystems including skid-steer and tracked robots, high-speed RC car and legged robotsand as a part of Team CoSTAR’s effort to theDARPASubterranean Challenge, wherethe team won 2nd and 1st place in the Tunnel and Urban Circuit, respectively.

Agha-mohammadi, Ali-akbar↗
NASA NTRSDate not supplied

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↗
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 NTRSDate not supplied

Lunar Surface Position Determination using Perceived Signal Strength

The purpose of this project is to evaluate the feasibility of transmitters and receivers on the lunar surface for Position Determination (PD) without any form of lunar Global Positioning System (GPS). The early Artemis program may lack GPS satellites orbiting the Moon, and it is critical that activities with the lander, rover, and crew EVA identify their position on the lunar surface at all times. This project creates a prototype system that trilaterates user position based upon the perceived signal from at least 3 nearby transmission towers, called “Lunar Access Points”. The application of perceived signal strength for surface PD has historically been used in terrestrial systems such as Long Range Navigation (LORAN), which was popular with the maritime industry prior to the Global Positioning System (GPS). The ease of installing such a local system for early Artemis missions provides a critical resource until satellite-based position determination systems are deployed. A surface-based PD can also be used in GPS-denied environments such as deep craters or lava tubes where satellite visibility is compromised. By demonstrating the basic capability of surface PD, this student team has learned about issues with power, distance, thermal, dust, radiation, data processing, and communication problems applicable to the lunar surface. This knowledge can feed into future NASA requirements to improve the capability of a LunaNET implementation for the Artemis program. This project follows 10 years of successful collaboration between NASA JSC/ARES, Texas Space, Technology, Applications and Research (T STAR) and Texas A&M University in a Public, Private, Academic (PPA) Partnership. NASA funds T STAR to mentor undergraduate Capstone teams in the College of Engineering Department to design, built, and test prototypes meeting NASA requirements. TAMU faculty lead the student teams in their academic class, and NASA Subject Matter Experts (SMEs) provide T STAR and students insight on requirements evolution, prior design projects, and future development goals.

Position Determination↗