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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

This paper reports on a collaborative project between U.S. Army TARDEC and Jet Propulsion Laboratory (JPL) to develop a unmanned ground vehicle (UGV) simulation model using the ROAMS vehicle modeling framework. Besides modeling the physical suspension of the vehicle, the sensing and navigation of the HMMWV vehicle are simulated. Using models of urban and off-road environments, the HMMWV simulation was tested in several ways, including navigation in an urban environment with obstacle avoidance and the performance of a lane change maneuver.

unmanned ground vehicle (UGV)↗
NASA NTRS2015

Road-Following Formation Control of Autonomous Ground Vehicles

This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance.

Ono, Masahiro↗
NASA NTRSDate not supplied

Development of Urban Air Mobility (UAM) Vehicles for Ease of Operation

To date the air transportation system has been developed with the in-cremental introduction of new technology and with highly experienced air transport pilots and air traffic controllers overseeing flight operations. Thus, we currently have one of the safest commercial aviation systems in the world. General Aviation (GA) in the United States, however, has not always followed the same cautious and monitored approach to implementation; consequently, the GA safety record does not meet the high standards of commercial aviation. Recently, a new system known as Urban Air Mobility (UAM), is attracting considerable interest and investment from industry and government agencies. UAM refers to a system of passenger and small-cargo air transportation vehicles within an urban area with the goal of reducing the number of times we need to use our cars, thus improving urban traffic by moving people and cargo from crowded single pas-senger vehicles on our roads to personal and on-demand air vehicles. These UAM vehicles will be small and based on electric, Vertical-Take-Off-and-Landing (eV-TOL) systems. A significant component of UAM is offloading of flight-man-agement responsibilities from human pilots to newly-developed autonomy. Cur-rently, over 100 UAM vehicles are either in development or production. Most, if not all, have a goal of fully autonomous vehicle operations, but fully autonomous flying vehicles are not expected in the near future. Therefore, we are de-veloping concepts for UAM vehicles that will be easy to fly and/or manage by operators with minimal pilot training. In this paper we will discuss our human-automation teaming approach to develop an easy-to-operate VTOL aircraft, and some of the fly-by-wire technology needed to stabilize the vehicle so that a sim-ple ecological mental model of the flying task can be implemented. We will discuss the requirements for a stability augmentation system that must be developed to support our simple pilot input model, and also present design guidelines and requirements based on a pilot input and management model. Finally, our ap-proach to vehicle development will involve considerable operator testing and evaluation: improving pilot model, inceptors, displays and also work on a plan for how a UAM vehicle can be integrated with terminal area air traffic control airspace with minimal impact on controller workload.

UAM↗
NASA NTRS2024

A High-Performance Computing Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

This report offers analysis and design insights for leveraging High-Performance Computing (HPC) to predict line-of-sight (LOS) Global Navigation Satellite System (GNSS) availability in a city. This work is motivated by the emerging fields of Advanced and Urban Air Mobility (AAM/UAM), where regulatory authorities are seeking city-scale, meter-resolution risk forecasting in order to safely integrate new flight missions with existing urban life and infrastructure. This work addresses the technical challenge of efficiently computing urban GNSS satellite visibility to predict GNSS performance metrics under these requirements. We present a new HPC-optimized shadow casting algorithm variant as a ray-based approach to forecasting satellite visibility. We apply this algorithm variant in a software-defined prognostic service which generates a GNSS navigation risk-correlated map as a path planning-style potential field. We detail dominant computational burdens, viable simplifying assumptions, and different algorithmic implementations, intending to demonstrate a baseline of computation time needed by each stage in such a service. We conclude by analyzing the prototype service’s prediction accuracy compared to receiver data from Corpus Christi, Texas. This informs design trade-offs along the dimensions of hardware, computation time, and tolerable forecasting error (including proportions of false positives and false negatives).

GNSS↗
NASA NTRS2011

Intelligent Autonomy for Unmanned Surface and Underwater Vehicles

As the Autonomous Underwater Vehicle (AUV) and Autonomous Surface Vehicle (ASV) platforms mature in endurance and reliability, a natural evolution will occur towards longer, more remote autonomous missions. This evolution will require the development of key capabilities that allow these robotic systems to perform a high level of on-board decisionmaking, which would otherwise be performed by humanoperators. With more decision making capabilities, less a priori knowledge of the area of operations would be required, as these systems would be able to sense and adapt to changing environmental conditions, such as unknown topography, currents, obstructions, bays, harbors, islands, and river channels. Existing vehicle sensors would be dual-use; that is they would be utilized for the primary mission, which may be mapping or hydrographic reconnaissance; as well as for autonomous hazard avoidance, route planning, and bathymetric-based navigation. This paper describes a tightly integrated instantiation of an autonomous agent called CARACaS (Control Architecture for Robotic Agent Command and Sensing) developed at JPL (Jet Propulsion Laboratory) that was designed to address many of the issues for survivable ASV/AUV control and to provide adaptive mission capabilities. The results of some on-water tests with US Navy technology test platforms are also presented.

environmental sensing↗
NASA NTRS2009

Research for Lunar Exploration: ADVANCE Program

This viewgraph presentation reviews the work that the author has been involved with in her undergraduate and graduate education and the ADVANCE Program. One project was the Lunar Entry and Approach Platform For Research On Ground (LEAPFROG). This vehicle was to be a completely autonomous vehicle, and was developed in successive academic years with increases in the perofmamnce and capability of the simulated lander. Another research project for the PhD was on long-term lunar radiation degradation of materials to be used for construction of lunar habitats. This research has concentrated on developing and testing light-weight composite materials with high strength characteristics, and the ability of these composite materials to withstand the lunar radiation environment.

Rojdev, Kristina↗
NASA NTRS2000

Design of a 10.8 kWh, 28V Ni-MH Battery Using Commercial Ni-MH Cells

This paper describes the design of a 10.8 kWh, 28V, Ni-MH battery using commercial off-the shelf (COTS) 4/3A Ni-MH cells for the X-38 vehicle, an experimental version of the Crew Return Vehicle (CRY). This will be an autonomous vehicle that will enable International Space Station crews to return to earth in the event of a medical, or other, emergency. The X-38 will be powered by 3 batteries: a 32 V primary battery, which will power the vehicle avionics for up to 7 hours for a loiter and de-orbit phase of the descent; a 28 V Ni-MH battery which will take over for the primary battery after de-orbit until landing, and a 270V Ni-Cd battery, which will be used to power electromechanical actuators and the winches controlling a parachute for landing.

Hellen, Robert M.↗
NASA NTRSDate not supplied

Simple Pattern Traffic Generation for Automated Flight Research in Non-Towered Traffic Patterns

Research efforts into autonomous air traffic will necessitate tools for testing algorithm capabilities. Testing will require flexible methods for creating large quantities of artificial data to verify the safety of automated systems. The traffic generation method in this work was developed to test traffic prediction and replanning algorithms for an autonomous vehicle attempting to land at a non-towered airport. The traffic generation method produces airport approach trajectories supporting a wide range of pattern entry types and typical pattern modification maneuvers for multiple aircraft types with varying performance capabilities. For each aircraft there are options for choosing the approach type, modifying how the approach is flown, and imposing scenario-driven temporal constraints, such as spacing between pairs of aircraft. The tool uses simplified aircraft dynamics to produce position and velocity profiles for traffic vehicles. Additionally, the tool supports standalone simulation tests or batch/bulk testing efforts, multiple output data options, and facilitates post-processing analysis.

Autonomous Vehicle↗
NASA NTRS2010

NASA Docking System (NDS) Interface Definitions Document (IDD)

The NASA Docking System (NDS) mating system supports low approach velocity docking and provides a modular and reconfigurable standard interface, supporting crewed and autonomous vehicles during mating and assembly operations. The NDS is NASA's implementation for the emerging International Docking System Standard (IDSS) using low impact docking technology. All NDS configurations can mate with the configuration specified in the IDSS Interface Definition Document (IDD) released September 21, 2010. The NDS evolved from the Low Impact Docking System (LIDS). The acronym international Low Impact Docking System (iLIDS) is also used to describe this system. NDS and iLIDS may be used interchangeability. Some of the heritage documentation and implementations (e.g., software command names) used on NDS will continue to use the LIDS acronym. The NDS IDD defines the interface characteristics and performance capability of the NDS, including uses ranging from crewed to autonomous space vehicles and from low earth orbit to deep space exploration. The responsibility for developing space vehicles and for making them technically and operationally compatible with the NDS rests with the vehicle providers. Host vehicle examples include crewed/uncrewed spacecraft, space station modules, elements, etc. Within this document, any docking space vehicle will be referred to as the host vehicle. This document defines the NDS-to-NDS interfaces, as well as the NDS-to-host vehicle interfaces and performance capability.

Source record↗
NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗
NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗
NASA NTRS2019

Autonomy @ NASA for Aeronautics

NASA has been exploring the use of artificial intelligence technologies to improve vehicle and airspace capabilities for over 20 years. This research has ranged from the use of neural nets to allow adaptive control, to autonomous rotorcraft operations, to real-time prognostics, to an eventual goal of autonomous vehicles operating in an autonomous airspace, in the context of smart communities. Examples of past research and future directions in autonomy for aeronautics will be presented.

Van Dalsem, William R.↗
NASA NTRS2004

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include real-time, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identity other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

egomotion↗
NASA NTRS2004

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include realtime, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify & other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

egomotion↗