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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Overview and Initial Results of SAND-E: Semi-Autonomous Navigation for Detrital Environments

Unmanned aerial systems (UAS) and automated terrain analysis for science and navigation are new technologies for planetary exploration. The Mars Helicopter will fly with the Mars2020 rover, the Dragonfly quadcopter will explore Titan, and Soil Properties and Object Classification (SPOC) software will be used for path planning and navigation on the Mars2020 rover. Using an Argo J5 rover instrumented with stereo cameras and Autonomous Soil Assessment System (ASAS) software, and an off the shelf quadcopter, SAND-E tested the use of automated terrain analysis and UAS data for science operations in a Mars-analog environment in Iceland during July of 2019. Scientifically, we sought to determine changes in the physical and chemical properties of sediments along a glacial-fluvial-aeolian transport pathway. Operationally, we tested rover mission-like scenarios that included UAS images and classified terrain images. Here, we present the initial results for both the operations and science elements of the study. Site Selection: A goal of SAND-E is examine sorting and alteration of sediments in fluvial and aeolian environments in both mineral-dominated and glass-dominated basaltic settings. During the first year of the project we focused on a mineral-dominated environment. Selection of the location was based on prior publications that indicated our selected region had a greater abundance of crystalline sediments than other areas fluvial-aeolian settings in Iceland. Other criteria included the presence of both fluvial and aeolian landforms along a transport pathway such that the sediments in transport could be linked to their source rocks. We chose the Skjaldbreidauhraun glacial outwash plain, which sits at the base of Thórisjökull glacier. The site is 30 km north of Thingvellir National Park and ~2 hours from Reykjavik. The outwash plain is fed by two small catchments that drain from the base of the glacier and cut through hyaloclastite and shield volcano bedrock. The drainage progresses from steep alluvial fans near the glacier into a low-sloping fluvial braidplain that becomes confined by the Skjaldbreidur shield volcano and creates a shallow canyon cut into lava bedrock. The fluvial system was a typical braided alluvial environment composed pebble- and cobble-bedded longitudinal bars and sandy channel beds. The river remained active and fluctuated in response to diurnal runoff cycles near the glacier before disappearing into the sandy substrate downstream. The high concentration of suspended sediment in the river was evident by the cloudy water and the silt and clay-sized sediments that draped the channel beds after abandonment and created playas in the lowest sloping areas of the catchment. The entire fluvial system was affected by the winds generated by frontal systems and katabatic flows descending the glacier. This resulted in the formation of aeolian lag deposits and a wind-deflation plain where the fluvial system was not active. Wind ripples and drifts formed in abandoned fluvial channels from aeolian reworking of the sand-sized fluvial sediments. The silt- and clay-sized sediments found in fluvial channels, bar tops, and playas generated dust plumes during high wind events. Our operation sought to capture the variability in this system by sampling from the range of fluvial and aeolian features 6.3 km (proximal), 11.3 km (medial), and 14.4 km (distal) along the river from its origin at the base of glacier.

Ewing, R. C.↗
NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

The ability to detect and locate obstacles using on-board sensors and modify the nominal trajectory is necessary for safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects using a sequence of images from a passive sensor, and describes a Kalman filter approach to improve the range estimation to obstacles. The filter is also used to track features in the images leading to a significant reduction of search effort in the feature extraction step of the algorithm. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. An experiment is designed to acquire a sequence of images along with sensor motion parameters and the range estimation results using this imagery are presented.

Sridhar, B.↗
DOE OSTI2020

Dedicated short range communications used as fail-safe in autonomous navigation

Here in this paper, we have analyzed scenarios for leader-follower vehicle convoys that have the potential to be unsafe, hazardous, or even fatal in order to provide insight on dangerous driving conditions for autonomous vehicle platoons. These scenarios were created in a simulation software called Prescan, and are made to reflect the behavior of the vehicle dynamics and sensor characteristics of a real-world vehicle convoy that is currently being tested at the American Center for Mobility (ACM) and sponsored by the Department of Energy (DOE), National Renewable Energy Laboratory (NREL), the U.S. Army Combat Capabilities Development Command - Ground Vehicle Systems Center (U.S. Army CC-DEVCOM) and the Michigan Department of Transportation (MDOT). The follower vehicle in this study is tested under multiple scenarios, equipped with a Dedicated Short Range vehicle-to-vehicle communication system (DSRC), a radar-based adaptive cruise control system (ACC) and a precision global positioning system (GPS).

42 ENGINEERING↗
DOE OSTI2024

Self-driving laboratories to autonomously navigate the protein fitness landscape

Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.

Rapp, Jacob T. (ORCID:0000000226835204)↗
NASA NTRS2015

Autonomous Navigation, Dynamic Path and Work Flow Planning in Multi-Agent Robotic Swarms Project

Kennedy Space Center has teamed up with the Biological Computation Lab at the University of New Mexico to create a swarm of small, low-cost, autonomous robots, called Swarmies, to be used as a ground-based research platform for in-situ resource utilization missions. The behavior of the robot swarm mimics the central-place foraging strategy of ants to find and collect resources in an unknown environment and return those resources to a central site.

Technology Portfolio System↗
DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part II - Autonomous Navigation

Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2020

Autonomous Navigation over Europa Analogue Terrain for an Actively Articulated Wheel-on-Limb Rover

The ocean world Europa is a prime target forexploration given its potential habitability [1]. We proposea mobile robotic system that is capable of autonomouslytraversing hundreds of meters to visit multiple sites of intereston a Europan analogue surface. Due to the topology of Europanterrain being largely unknown, it is desired that this mobilitysystem traverse a large variety of terrain types. The mobilitysystem should also be capable of crossing unstructured terrainin an autonomous manner given the communications limitationsbetween Earth and Europa.A wheel-on-limb robotic rover is presented that may activelyconform to terrain features up to 1.5 wheel diameters tall whiledriving. The robot uses a sampling-based motion planner togenerate paths that leverage its unique locomotive capabilities.The planner assesses terrain hazards and wheel workspacelimits as obstacles. It may also select a mobility mode basedon predicted energy usage and the need for limb articulationon the terrain being traversed. This autonomous mobility wasevaluated on the chaotic salt-evaporite terrain found in DeathValley, CA, an analogue to the Europan surface. Over the courseof 38 trials, the rover autonomously traversed 435m of extremeterrain while maintaining a rate of 0.64 traverse ending failuresfor every 10m driven.

Meirion-Griffith, Gareth↗
NASA NTRS2019

Towards Resilient Autonomous Navigation of Drones

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a ying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge.

Agha-mohammadi, Ali-akbar↗
NASA NTRS1979

A digital scene matching technique for geometric image correction and autonomous navigation

A technique is described for precise registration of two images of the same area, taken under different conditions. The technique, called AUTO-MATCH, involves digital preprocessing of the images to extract edge contours, followed by a correlation of corresponding edge end points. The availability of an array of endpoints makes possible subpixed registration accuracy. The technique is applied to a system for assessment of geometric image quality, to be installed at NASA-Goddard. This system measures the registration vectors over an array of window pairs from LANDSAT images, so as to determine the distortion between them. Similar measurements could be used to infer relative positioning of the spacecraft. The technique is considered for updating the inertial navigation systems of missiles or aircraft. Scene matching between an image obtained aboard the vehicle and a stored reference can eliminate the drift of an inertial platform in order to demonstrate this capability in real time. A laboratory demonstration of AUTO-MATCH is described.

Tisdale, G. E.↗
NASA NTRS1981

Autonomous navigation system

An inertial navigation system utilizing a servo-controlled two degree of freedom pendulum to obtain specific force components in the locally level coordinate system is described. The pendulum includes a leveling gyroscope and an azimuth gyroscope supported on a two gimbal system. The specific force components in the locally level coordinate system are converted to components in the geographical coordinate system by means of a single Euler transformation. The standard navigation equations are solved to determine longitudinal and lateral velocities. Finally, vehicle position is determined by a further integration.

Merhav, S. J.↗
NASA NTRS1987

Fuzzy sets and autonomous navigation

The use of fuzzy sets in modeling the human expert for certain Space Shuttle navigation problems is discussed with particular reference to onboard and ground console data monitoring tasks traditionally performed by astronauts and engineers. Specific problems include determining the quality of sensor data and of the filter state. The results obtained in this study indicate that fuzzy sets can be successfully used in modeling human reaction to rules in decision-making processes. They can also be used within software systems where guidelines have traditionally been used to set strict tolerances.

Lea, Robert N.↗
NASA NTRS1989

Autonomous navigation and mobility for a planetary rover

This paper presents an overview of the onboard subsystems that will be used in guiding a planetary rover. Particular emphasis is placed on the planning and sensing systems and their associated costs, particularly in computation. Issues that will be used in evaluating trades between the navigation system and mobility system are also presented.

Miller, David P.↗
NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

Rotorcraft operating in high-threat environments fly close to the surface of the earth to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by the enemy. Two basic requirements for obstacle avoidance are detection and range estimation of the object from the current rotorcraft position. There are many approaches to the estimation of range using a sequence of images. The approach used in this analysis differes from previous methods in two significant ways: an attempt is not made to estimate the rotorcraft's motion from the images; and the interest lies in recursive algorithms. The rotorcraft parameters are assumed to be computed using an onboard inertial navigation system. Given a sequence of images, using image-object differential equations, a Kalman filter (Sridhar and Phatak, 1988) can be used to estimate both the relative coordinates and the earth coordinates of the objects on the ground. The Kalman filter can also be used in a predictive mode to track features in the images, leading to a significant reduction of search effort in the feature extraction step of the algorithm. The purpose is to summarize early results obtained in extending the Kalman filter for use with actual image sequences. The experience gained from the application of this algorithm to real images is very valuable and is a necessary step before proceeding to the estimation of range during low-altitude curvilinear flight. A simple recursive method is presented to estimate range to objects using a sequence of images. The method produces good range estimates using real images in a laboratory set up and needs to be evaluated further using several different image sequences to test its robustness. The feature generation part of the algorithm requires further refinement on the strategies to limit the number of features (Sridhar and Phatak, 1989). The extension of the work reported here to curvilinear flight may require the use of the extended Kalman filter.

Sridhar, Banavar↗
NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, Son H.↗
NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, S. H.↗