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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Power Sources for Micro-Autonomous Vehicles- Challenges and Prospects

Micro-autonomous vehicle systems are expected to have expanded role in military missions by providing full spectrum intelligence, surveillance and reconnaissance support on the battlefield, suppression of enemy defenses, and enabling co-operative (swarm-like) configurations. Of the numerous demanding requirements of autonomy, sensing, navigation, mobility, etc., meeting the requirement of mission duration or endurance is a very challenging one. This requirement is demanding because of the constraints of mass and volume that limit the quantity of energy that can be stored on-board. Energy is required for mobility, payload operation, information processing, and communication. Mobility requirements typically place an extraordinary demand on the specific energy (Wh/kg) and specific power (W/kg) of the power source; the actual distribution of the energy between mobility and other system functions could vary substantially with the mission type. The power requirements for continuous mobility can vary from 100-1000 W/kg depending on the terrain, ground speed and flight speed. Even with the power source accounting for 30% of the mass of the vehicle, the best of rechargeable batteries can provide only up to 1-2 hours of run-time for a continuous power demand at 100W/kg. In the case of micro-aerial vehicles with flight speed requirements in the range of 5-15 m s-1, the mission times rarely exceed 20 minutes [2]. Further, the power required during take-off and hover can be twice or thrice that needed for steady level flight, and thus the number and sequence of such events is also limited by the mass and size of the power source. For operations such as "perch and stare" or "silent watch" the power demand is often only a tenth of that required during continuous flight. Thus, variation in power demand during various phases of the mission importantly affects the power source selection.

fuel cells↗
NASA NTRS1987

SOLON: An autonomous vehicle mission planner

The State-Operator Logic Machine (SOLON) Planner provides an architecture for effective real-time planning and replanning for an autonomous vehicle. The highlights of the system, which distinguish it from other AI-based planners that have been designed previously, are its hybrid application of state-driven control architecture and the use of both schematic representations and logic programming for the management of its knowledge base. SOLON is designed to provide multiple levels of planning for a single autonomous vehicle which is supplied with a skeletal, partially-specified mission plan at the outset of the vehicle's operations. This mission plan consists of a set of objectives, each of which will be decomposable by the planner into tasks. These tasks are themselves comparatively complex sets of actions which are executable by a conventional real-time control system which does not perform planning but which is capable of making adjustments or modifications to the provided tasks according to constraints and tolerances provided by the Planner. The current implementation of the SOLON is in the form of a real-time simulation of the Planner module of an Intelligent Vehicle Controller (IVC) on-board an autonomous underwater vehicle (AUV). The simulation is embedded within a larger simulator environment known as ICDS (Intelligent Controller Development System) operating on a Symbolics 3645/75 computer.

Dudziak, M. J.↗
NASA NTRSDate not supplied

Trajectory Planning for Mission Survivability of Autonomous Vehicles in Moderately to Extremely Uncertain Environments

Trajectory planning is a particularly challenging task for autonomous vehicles when the uncertainties in their operating environment cannot be characterized statistically. In this paper, we propose a receding horizon control strategy with novel trajectory planning policies that enable dynamic updating of the planned trajectories of autonomous vehicles operating in environments where potential conflicts are, from a statistical perspective, either partially known or completely unknown. The proposed policies utilize two metrics: (1) the number of feasible trajectories; and (2) the robustness of the feasible trajectories. We measure the effectiveness of the suggested policies in terms of mission survivability, which is defined as the probability that the primary mission is accomplished or, if that is not possible, the vehicle lands safely at an alternative site. We show that a linear combination of both metrics is an effective objective function when there is a mix of partially known and unknown uncertainties. When the operating environment is dominated by unknown disturbances, maximizing the number of feasible trajectories results in the highest mission survivability. These findings have significant implications for achieving safe aviation. autonomy.

Aviation autonomy↗
NASA NTRSDate not supplied

Trajectory Planning for Mission Survivability of Autonomous Vehicles in Moderately to Extremely Uncertain Environments

Trajectory planning is a particularly challenging task for autonomous vehicles when there are moderate to extreme uncertainties in their operating environment, i.e., where the trajectories of hazards are partially known to completely unknown. In this paper, we propose a receding horizon control strategy with novel trajectory planning policies that enable dynamic updating of the planned trajectories of autonomous vehicles. The proposed policies utilize two metrics: (1) the number of feasible trajectories; and (2) the robustness of the feasible trajectories. We measure the effectiveness of the suggested policies in terms of mission survivability, which is defined as the probability that the primary mission is accomplished or, if that is not possible, the vehicle lands safely at an alternative site. We show that a linear combination of both metrics is an effective objective function when there is a mix of partially known and unknown uncertainties. When the operating environment is dominated by unknown disturbances, maximizing the number of feasible trajectories results in the highest mission survivability. These findings have significant implications for achieving safe aviation autonomy.

aviation autonomy↗
NASA NTRSDate not supplied

Pathfinding for Airspace with Autonomous Vehicles (PAAV) + m:N Tabletop

The Pathfinding for Airspace with Autonomous Vehicles (PAAV) project is investigating how to integrate increasingly autonomous aircraft into the current air traffic management system. The project aims to help develop airspace procedures and technologies that are scalable to future autonomous operations. The purpose of this brief is to inform a working group on past and future PAAV efforts. The working group specializes in "m:N" operations, which refers to remote operations where one or more ground-based pilots cooperatively control multiple unmanned aircraft. The PAAV team will detail an upcoming tabletop exercise that is designed to elicit feedback from subject matter experts on current barriers to m:N operations for unmanned cargo operations and potential solutions to those obstacles. The working group will be given an opportunity to provide feedback on the objectives and methodology to be used in the PAAV tabletop exercise.

autonomous↗
NASA NTRS2019

A New Market for Terrestrial Single-Event Effects: Autonomous Vehicles

ISO-26262, the road vehicle functional safety standard, underwent a major overhaul that was released in December 2018. Radiation effects, and single-event effect (SEE) hazards in particular, play an important role in autonomous vehicle safety. This connection will only increase as the level of driving automation goes from "hands off," to "eyes off," to "mind off." This translates to increased coupling with space climate and weather in addition to other traditional terrestrial radiation sources like thorium and uranium contamination in process and packaging materials. We will focus on autonomous vehicle radiation effects and present both benefits and challenges to the space weather and radiation engineering communities.

Autonomous Vehicles↗
NASA NTRSDate not supplied

Pathfinding for Airspace with Autonomous Vehicles (PAAV) Multi-Vehicle Operations with Digital Trajectories

The Path Finding for Airspace with Autonomous Vehicles (PAAV) subproject under ATM-X is developing concepts and solutions to allow pilots to supervise multiple uncrewed flights at a time. This line of research stems from industry's desire to leverage Uncrewed Aircraft (UA) operations to achieve maximum productivity from pilot and aircraft resources, driven partially by pilot staffing shortages. Trajectory based solutions delivered by data link offer a potential solution to the multi-vehicle (m:N) problem by minimizing the number of instructions between remote pilots and air traffic controllers. Furthermore, such solutions avoid the need for pilots to monitor multiple voice frequencies at a time for air traffic control instructions to manage multiple flights. This panel presentation will give a brief introduction to PAAV followed by a description of NASA's AutoResolver capability for generating comprehensive air traffic control clearances and communicating them via data link for controller approval and flight execution.

multi vehicle control, trajectory based operations↗
NASA NTRS2023

Pathfinding for Airspace with Autonomous Vehicles (PAAV) Tabletop 4 Report

NASA's Pathfinding for Airspace with Autonomous Vehicles (PAAV) sub-project investigates procedures and technologies to facilitate seamless integration of future UAS operations into the NAS. The Tabletop 4 activity solicited subject matter expertise to identify solutions to the potential challenges expected when the remote pilot-to-vehicle ratio scales from 1:1 to m:N, where one or more ground-based pilots control multiple uncrewed aircraft. This report details the method and results of the PAAV Tabletop 4 activity.

multi-vehicle operations↗
NASA NTRSDate not supplied

Pathfinding for Airspace with Autonomous Vehicles (PAAV) Overview for RTCA SC-228

The Pathfinding for Airspace with Autonomous Vehicles (PAAV) sub-project started in 2021 under NASA’s Air Traffic Management – eXploration (ATM-X) project. ATM-X as a project is conducting research to support the growth of traditional aviation and new entrants. This presentation discusses the background of the PAAV project as well as it’s current areas of focus around uncrewed aircraft system (UAS) operations. The areas include the development of a flight test ecosystem, detect and avoid, traffic pattern integration, aircraft-to-aircraft communications, and contingency management. The aim of these research areas is to help inform the development of performance standards and FAA-approved procedures for integrating UAS into the national airspace system (NAS). The presentation seeks to describe the PAAV research objectives in sufficient detail to allow the committee to provide feedback on the sub-project's approach and main areas of interest.

uncrewed aircraft systems↗
NASA NTRS2017

Computationally Efficient Motion Planning Algorithms for Agile Autonomous Vehicles in Cluttered Environments

Fast, real-time motion planning of an agile, autonomous vehicle in a cluttered environment, with many geometrically-fixed obstacles, is a very complex problem, especially because of the vehicle dynamics constraints and resource constrained computational capabilities onboard the vehicle. In this paper, we present computationally-efficient versions of our novel motion planning algorithm called the Spherical Expansion and Sequential Convex Programming (SE–SCP) algorithm. The SE–SCP algorithm first uses a spherical-expansion-based randomized sampling algorithm to explore the workspace. Oncea path is found from the start position to the goal position, the algorithm computes a locally optimal trajectory, within its homotopy class for a desired cost function, by solving a sequence of convex optimization problems. Thus, the SE–SCP algorithm is anytime locally optimal and the trajectory is globally optimal if the number of samples tends to infinity. In this paper, we further enhance the computational efficiency of the SE–SCP algorithm using uni-directional and bi-directional rewiring techniques. We also present a detailed proof of the local optimality characteristics of the new SE–SCP algorithms for aspecial case of vehicle dynamics. Simulation examples involving quadrotor and spacecraft help demonstrate the effectiveness of our new algorithms.

Bandyopadhyay, Saptarshi↗
NASA NTRSDate not supplied

Use of Autonomous Vehicles in Emergency Situations - Wildfire Planning and Mitigation

Unmanned vehicles can be useful in emergency situations for many applications such as surveillance, access to harsh environments, and delivery of supplies. However, efforts to use drones in these situations has not been well-coordinated. This project is looking at multiple aspects of the problem with sub-teams addressing: establishing a method and database network to identify and communicate resources for deployment during major incidents and disasters; identifying how drones and other aviation assets can be useful in wildfire-specific emergencies, including mitigation and prevention planning; and considering safety, rules, and behavior of autonomous vehicles.

Autonomous Vehicles↗
NASA NTRS2004

Proposing an International Collaboration on Lightweight Autonomous Vehicles to Conduct Scientific Traverses and Surveys over Antarctica and the Surrounding Sea Ice

We have continued to develop a concept for use of autonomous rovers, originally developed for use in planetary exploration, in polar science on Earth; the concept was the subject of a workshop, and this report summarizes and extends that workshop. The workshop on Antarctic Autonomous Scientific Vehicles and Traverses met at the National Geographic Society on February 14 and 15, 2001 to discuss scientific objectives and benefits of the use of autonomous rovers. The participants enthusiastically viewed rovers as being uniquely valuable for such tasks as data taking on tedious or repetitive routes, traverses in polar night, difficult or hazardous routes, extremely remote regions, routes requiring only simple instrumentation, traverses that must be conducted at low speed, augments of manned traverses, and scientific procedures not compatible with human presence or combustion engines. The workshop has concluded that instrumented autonomous vehicles, of the type being developed for planetary exploration, have the potential to contribute significantly to the way science in conducted in Antarctica while also aiding planetary technology development, and engaging the public's interest. Specific objectives can be supported in understanding ice sheet mass balance, sea ice heat and momentum exchange, and surface air chemistry processes. In the interval since the workshop, we have concluded that organized program to employ such rovers to perform scientific tasks in the Fourth International Polar Year would serve the objectives of that program well.

polar science↗
NASA NTRSDate not supplied

Pathfinding for Airspace with Autonomous Vehicles (PAAV) ACAS Xr Terminal Area Simulation Overview

This presentation provides an overview of an upcoming human-in-the-loop simulation planned by the Pathfinding for Airspace with Autonomous Vehicles (PAAV) subproject, as part of the Air Traffic Management eXploration project. The study will investigate Detect and Avoid (DAA) concepts in and around the DAA terminal area (DTA). The DTA has received little investigation in real-time, pilot-in-the-loop simulation. This study proposes to use current remotely piloted aircraft system (RPAS) pilots to investigate the effectiveness of current DTA requirements using the FAA's Airborne Collision Avoidance System X for Rotorcraft (ACAS Xr) as the DAA system under test. The findings of this stufy will be used to inform DTA and ACAS Xr standards development. This presentation will be provided to RTCA Special Committee 228 as an overview and as an opportunity to collect feedback from the larger RPAS/DAA community on the study design and key scenarios and metrics. The presentation provides a brief overview of open areas of research with ACAS Xr, the experimental design, the simulation environment and the simulation schedule.

advanced air mobility↗
NASA NTRS1991

INS integrated motion analysis for autonomous vehicle navigation

The use of inertial navigation system (INS) measurements to enhance the quality and robustness of motion analysis techniques used for obstacle detection is discussed with particular reference to autonomous vehicle navigation. The approach to obstacle detection used here employs motion analysis of imagery generated by a passive sensor. Motion analysis of imagery obtained during vehicle travel is used to generate range measurements to points within the field of view of the sensor, which can then be used to provide obstacle detection. Results obtained with an INS integrated motion analysis approach are reviewed.

Roberts, Barry↗
NASA NTRS2016

Semi-Autonomous Vehicle Project

The primary objective this summer is "evaluating standards for wireless architecture for the internet of things". The Internet of Things is the network of physical objects or "things" embedded with electronics, software, sensors and network connectivity which enables these objects to collect and exchange data and make decisions based on said data. This was accomplished by creating a semi-autonomous vehicle that takes advantage of multiple sensors, cameras, and onboard computers and combined them with a mesh network which enabled communication across large distances with little to no interruption. The mesh network took advantage of what is known as DTN - Disruption Tolerant Networking which according to NASA is the new communications protocol that is "the first step towards interplanetary internet." The use of DTN comes from the fact that it will store information if an interruption in communications is detected and even forward that information via other relays within range so that the data is not lost. This translates well into the project because as the car moves further away from whatever is sending it commands (in this case a joystick), the information can still be forwarded to the car with little to no loss of information thanks to the mesh nodes around the driving area.

Stewart, Christopher↗
NASA NTRSDate not supplied

Distributed Pressure Sensing for Enabling Self-Aware Autonomous Aerial Vehicles

Autonomous aerial transportation will be a fixture of future robotic societies, simultaneously requiring more stringent safety requirements and fewer resources for characterization than current commercial air transportation. More robust, adaptable, self-state estimation will be necessary to create such autonomous systems. We present a modular, scalable, distributed pressure sensing skin for aerodynamic state estimation of a large, flexible aerostructure. This skin used a network of 22 nodes that performed in-situ computation and communication of data collected from 74 pressure sensors, which were embedded into the skin panels of an ultra-lightweight 14-foot wingspan made from commutable, lattice-based subcomponents, and tested at NASA Langley Research Center's 14X22 wind tunnel. The density of the pressure sensors allowed for the use of a novel distributed algorithm to generate estimates of the wing lift contribution that were more accurate than the direct integration of the pressure distribution over the wing surface.

Daniel Cellucci↗