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Results for “multi-vehicle operations”

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 NTRS1991

Operator-assisted planning and execution of proximity operations subject to operational constraints

Future multi-vehicle operations will involve multiple scenarios that will require a planning tool for the rapid, interactive creation of fuel-efficient trajectories. The planning process must deal with higher-order, non-linear processes involving dynamics that are often counter-intuitive. The optimization of resulting trajectories can be difficult to envision. An interaction proximity operations planning system is being developed to provide the operator with easily interpreted visual feedback of trajectories and constraints. This system is hosted on an IRIS 4D graphics platform and utilizes the Clohessy-Wiltshire equations. An inverse dynamics algorithm is used to remove non-linearities while the trajectory maneuvers are decoupled and separated in a geometric spreadsheet. The operator has direct control of the position and time of trajectory waypoints to achieve the desired end conditions. Graphics provide the operator with visualization of satisfying operational constraints such as structural clearance, plume impingement, approach velocity limits, and arrival or departure corridors. Primer vector theory is combined with graphical presentation to improve operator understanding of suggested automated system solutions and to allow the operator to review, edit, or provide corrective action to the trajectory plan.

Grunwald, Arthur J.↗
NASA NTRS2020

Trajectory Generation for Noise-Constrained Autonomous Flight Operations

One of the major factors in acceptance of aircraft operating in urban areas is noise. In this work, we build on a framework for trajectory generation in order to account for limits on acousticmetrics at one ormore observer locations. The spatial trajectories are generated using Bézier polynomials and satisfy dynamic, acoustic, and mission constraints. The trajectories also guarantee spatial or temporal separation between vehicles for multi-vehicle operations. A simulation example is provided that demonstrates the reduction in noise levels at a set of measurement locations.

Ackerman, Kasey A.↗
NASA NTRSDate not supplied

Multi-Vehicle (m:N) Operations in the NAS - NASA's Research Plans

The Advanced Air Mobility movement is occurring across the world with goals of enabling, affordable, efficient, accessible, and safe air transportation at a much larger scale than today’s operations, largely enabled by electrification and automation. Transformative and disruptive innovations are emerging that will support an ecosystem designed to transport goods and people to locations not traditionally served by air transportation. To realize the full vision of AAM, technology will be needed to allow a few operators to operate many vehicles (m:N). The benefits of m:N operations are described, along with the current state-of-the-art, barriers, need, and NASA’s plans to address some of the barriers, including a Multi-Vehicle (m:N) Working Group with goals of producing a community-developed operational approval roadmap for various domains.

multi-vehicle↗
NASA NTRSDate not supplied

m:N Operations and Future

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N).This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT).This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the barriers and requirements for future m:N operations.

multi-vehicle control↗
NASA NTRS2018

Multi-Operator Multi-UAV (MOMU) Control: Exploring the Influence of Sensor Tools and Playbook Task Delegation

New concepts of operations for Unmanned Aerial Vehicles (UAVs) will require a change from the current 2:1 operator to vehicle crew configuration. One particular control paradigm, largely driven by logistics, manpower, and training burdens, as well as the desire to force multiply, involves a single operator simultaneously managing multiple UAVs. This mode of operations has shown to significantly increase cognitive workload and decrease situation awareness, as operators are required to simultaneously attend to multiple sources of information. One potential way to mitigate potential drawbacks of multi-vehicle control by a single operator is to migrate to a multi-operator multi-UAV (MOMU) crew configuration, whereby M operators control N (> M) vehicles. This type of crew configuration can be organized in several ways to dynamically manage cognitive workload, match operator qualifications and skills to mission requirements, increase utilization of available assets, and thereby achieve maximum force multiplication. The present experiment examined task performance in a simulated MOMU environment and evaluated the potential benefits of sensor management aids ("Tools") as well as integrated sensor and flight automation ("Plays") compared to a fully manual condition ("Manual"). Tools support the operator by facilitating rapid understanding and management of sensor information, while the Plays support the operator by offloading/automating subtasks. Six pairs of participants were recruited for this study and tasked with sharing a pool of UAVs in order to conduct reconnaissance, surveillance, and target acquisition (RSTA) missions in adjacent Areas of Operation (AOs). Participants were given four tasks to accomplish, in order of priority: 1) prosecute High Value Targets (HVTs); 2) identify / track targets (military vehicles); 3) identify / mark civilian vehicles; and 4) respond to chat messages. Performance on the mission tasks was measured in terms of accuracy and reaction time. A composite mission score was also calculated using a payoff matrix that weighted each task according to priority. The results indicate that Playbook demonstrated better performance overall with higher accuracy rates and the highest composite score compared to Tools and Manual. The implications of these results to supporting future MOMU concepts of operations is discussed.

Playbook↗
NASA NTRSDate not supplied

Future Operations

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the future of GCS control stations and need for Human Systems Integration.

multi-vehicle control↗
NASA NTRSDate not supplied

NASA HAT Lab Activities

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust).

multi-vehicle control↗
NASA NTRS1992

Advanced Manned Launch System (AMLS) study

To assure national leadership in space operations and exploration in the future, NASA must be able to provide cost effective and operationally efficient space transportation. Several NASA studies and the joint NASA/DoD Space Transportation Architecture Studies (STAS) have shown the need for a multi-vehicle space transportation system with designs driven by enhanced operations and low costs. NASA is currently studying an advanced manned launch system (AMLS) approach to transport crew and cargo to the Space Station Freedom. Several single and multiple stage systems from air-breathing to all-rocket concepts are being examined in a series of studies potential replacements for the Space Shuttle launch system in the 2000-2010 time frame. Rockwell International Corporation, under contract to the NASA Langley Research Center, has analyzed a two-stage all-rocket concept to determine whether this class of vehicles is appropriate for the AMLS function. The results of the pre-phase A study are discussed.

Ehrlich, Carl F., Jr.↗
NASA NTRS2022

Applicability of Digital Flight to the Operations of Self-Piloted Unmanned Aircraft Systems in the National Airspace System

Unmanned Aircraft Systems (UAS) hold great promise for a new era of specialized missions, including personal air transportation, cargo flight operations, aerial surveys, inspections, firefighting and more. The anticipated market growth is significant. To unlock its scalability and incumbent benefits requires a human to oversee multiple flights simultaneously, focusing on multi-vehicle mission management and relinquishing to autonomous systems their active role in controlling the aircrafts’ flight paths. Key to the realization of these scalability benefits is minimally-encumbered access to the National Airspace System (NAS), which poses some unique challenges for self-piloted UAS aircraft operations. These include the requirement for compatibility with existing airspace structures and operations including Visual Flight Rules (VFR) and Instrument Flight Rules (IFR), neither of which were developed to accommodate the unique needs and capabilities of UAS. This paper explores the applicability of Digital Flight to the operations of self-piloted UAS. As proposed by NASA, Digital Flight is a flight operations capability, enabled by a set of cooperative procedures and digital technologies, in which flight operators ensure flight-path safety through automated separation and flight path management in lieu of visual procedures and Air Traffic Control separation services. Flights operating under potentially-forthcoming rules of Digital Flight employ advanced automation technologies, information sharing, connectivity to operational data, and cooperative behaviors through distributed decision-making to maintain safety and achieve mission objectives. Designed for integration with VFR and IFR operations in shared NAS airspace, potentially as a third set of flight rules, Digital Flight may provide the mechanism for UAS operators – and all aircraft operators – to scale and diversify their operations beyond what is achievable under current regulations.

DFR↗
NASA NTRS2015

Automated Cooperative Trajectories for a More Efficient and Responsive Air Transportation System

The NASA Automated Cooperative Trajectories project is developing a prototype avionics system that enables multi-vehicle cooperative control by integrating 1090 MHz ES ADS-B digital communications with onboard autopilot systems. This cooperative control capability will enable meta-aircraft operations for enhanced airspace utilization, as well as improved vehicle efficiency through wake surfing. This briefing describes the objectives and approach to a flight evaluation of this system planned for 2016.

Wake Surfing↗
NASA NTRS2022

m:N Working Group

On November 29th and 30th, 2022, the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Ames Research Center in Mountain View, CA for an in person meeting. The option to dial in remotely and use Conference.IO to engage with questions was offered as well. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA and RTCA’s SC-228 Detect and Avoid. Each subgroup is run by a government/industry team.

multi-vehicle↗
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 NTRS1996

A Concept for the Use and Integration of Super-Conducting Magnets in Structural Systems in General and Maglev Guideway Mega-Structures in Particular

Recent breakthroughs in several different fields now make it possible to incorporate the use of superconducting magnets in structures in ways which enhance the performance of structural members or components of structural systems in general and Maglev guideway mega-structures in particular. The building of structural systems which connect appropriately scaled superconducting magnets with the post-tensioned tensile components of beams, girders, or columns would, if coupled with 'state of the art' structure monitoring, feedback and control systems, and advanced computer software, constitute a distinct new generation of structures that would possess the unique characteristic of being heuristic and demand or live-load responsive. The holistic integration of powerful superconducting magnets in structures so that they do actual structural work, creates a class of 'technologically endowed' structures that, in part - literally substitute superconductive electric power and magnetism for concrete and steel. The research and development engineering, and architectural design issues associated with such 'technologically endowed' structural system can now be conceptualized, designed, computer simulates built and tested. The Maglev guideway mega-structure delineated herein incorporates these concepts, and is designed for operation in the median strip of U.S. Interstate Highway 5 from San Diego to Seattle an Vancouver, and possibly on to Fairbanks, Alaska. This system also fits in the median strip of U.S. Interstate Highway 55 and 95 North-South, and 80 and 10, East-West. As a Western Region 'Peace Dividend' project, it could become a National or Bi-National research, design and build, super turnkey project that would create thousands of jobs by applying superconducting, material science, electronic aerospace and other defense industry technologies to a multi-vehicle, multi-use Maglev guideway megastructure that integrates urban mass transit Lower Speed (0-100 mph), High Speed (100-200 mph), Super Speed (200-400 mph), and Hypersonic evacuated tube (400-10,000 mph) Maglev systems.

Ussery, Wilfred T.↗
NASA NTRSDate not supplied

DAA Use Case for Auto Cargo m:N Operations

A detect and avoid use-case was developed to highlight detect and avoid issues associated with m:N operations in the auto cargo domain. This work is being done in conjunction with industry partners and developed for the Operational Scenario and Environmental Description (OSED) for RTCA SC-228. The detect and avoid function is critical, required technology for unmanned aircraft to operation in the national airspace. RTCA SC-228 has published MOPS (phase 1 & 2) detailing the requirements and methods of compliance. This work will help address additional operational aspects of how/when DAA will be employed by unmanned systems. Specifically, this work focuses on “auto cargo” operations. Auto cargo, in this context, refers to regularly scheduled cargo-size aircraft that are flown remotely. The Remote Pilot In Command (RPIC), in this case, is responsible for multiple aircraft, flown simultaneously. The use-case details the use of DAA in this context, the potential issues and gaps that exist.

multi-vehicle control↗
NASA NTRS1991

GN/C translation and rotation control parameters for AR/C (category 2)

Detailed analysis of the Automatic Rendezvous and Capture problem indicate a need for three different regions of mathematical description for the GN&C algorithms: (1) multi-vehicle orbital mechanics to the rendezvous interface point, i.e., within 100 n.; (2) relative motion solutions (such as Clohessy-Wiltshire type) from the far-field to the near-field interface, i.e., within 1 nm; and (3) close proximity motion, the nearfield motion where the relative differences in the gravitational and orbit inertial accelerations can be neglected from the equations of motion. This paper defines the reference coordinate frames and control parameters necessary to model the relative motion and attitude of spacecraft in the close proximity of another space system (Region 2 and 3) during the Automatic Rendezvous and Capture phase of an orbit operation.

Henderson, David M.↗
NASA NTRS2013

Advanced Modular Power Approach to Affordable, Supportable Space Systems

Recent studies of missions to the Moon, Mars and Near Earth Asteroids (NEA) indicate that these missions often involve several distinct separately launched vehicles that must ultimately be integrated together in-flight and operate as one unit. Therefore, it is important to see these vehicles as elements of a larger segmented spacecraft rather than separate spacecraft flying in formation. The evolution of large multi-vehicle exploration architecture creates the need (and opportunity) to establish a global power architecture that is common across all vehicles. The Advanced Exploration Systems (AES) Modular Power System (AMPS) project managed by NASA Glenn Research Center (GRC) is aimed at establishing the modular power system architecture that will enable power systems to be built from a common set of modular building blocks. The project is developing, demonstrating and evaluating key modular power technologies that are expected to minimize non-recurring development costs, reduce recurring integration costs, as well as, mission operational and support costs. Further, modular power is expected to enhance mission flexibility, vehicle reliability, scalability and overall mission supportability. The AMPS project not only supports multi-vehicle architectures but should enable multi-mission capability as well. The AMPS technology development involves near term demonstrations involving developmental prototype vehicles and field demonstrations. These operational demonstrations not only serve as a means of evaluating modular technology but also provide feedback to developers that assure that they progress toward truly flexible and operationally supportable modular power architecture.

Oeftering, Richard C.↗
DOE OSTI2025

Development and Experimental Validation of a High-Power DC Distribution Testbed for Advanced Charging Infrastructure and Energy Management

This paper presents the development of a hardware testbed for DC-distributed high-power charging (HPC) stations. As DC distributed solutions emerge as a viable solution to optimize HPC site operations, challenges such as interoperability, protection, and seamless integration of distributed energy resources (DER) persist. These issues underscore the need for a robust testing facility to investigate compliance of available commercial off-the-shelf (COTS) market devices. The developed testbed features a DC-distributed charging hub including a charger, emulated energy storage system (ESS), and site level communication and controller implementation. It facilitates the testing of COTS hardware, charger prototypes, standards validation and site energy management system (SEMS) controllers at rated power. This paper details the development of the charging infrastructure platform, implementation of communication system, validation of different SEMS algorithms, and understanding improvements required for future expansion. Using the developed testbed, interoperability gaps for SEMS implementation with multi-vehicle concurrent charging via a multi-port charger are experimentally observed. Aimed at supporting the transition to large-scale EV charging infrastructure deployment and DER integration, this testbed plays a crucial role in conformity testing of COTS device interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Development of a Crosslink Channel Simulator for Simulation of Formation Flying Satellite Systems

Multi-vehicle missions are an integral part of NASA s and other space agencies current and future business. These multi-vehicle missions generally involve collectively utilizing the array of instrumentation dispersed throughout the system of space vehicles, and communicating via crosslinks to achieve mission goals such as formation flying, autonomous operation, and collective data gathering. NASA s Goddard Space Flight Center (GSFC) is developing the Formation Flying Test Bed (FFTB) to provide hardware-in- the-loop simulation of these crosslink-based systems. The goal of the FFTB is to reduce mission risk, assist in mission planning and analysis, and provide a technology development platform that allows algorithms to be developed for mission hctions such as precision formation flying, synchronization, and inter-vehicle data synthesis. The FFTB will provide a medium in which the various crosslink transponders being used in multi-vehicle missions can be plugged in for development and test. An integral part of the FFTB is the Crosslink Channel Simulator (CCS),which is placed into the communications channel between the crosslinks under test, and is used to simulate on-orbit effects to the communications channel due to relative vehicle motion or antenna misalignment. The CCS is based on the Starlight software programmable platform developed at General Dynamics Decision Systems which provides the CCS with the ability to be modified on the fly to adapt to new crosslink formats or mission parameters.

Hart, Roger↗