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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Automated Cooperative Trajectories

This presentation is an overview of the Automated Cooperative Trajectories project. An introduction to the phenomena of wake vortices is given, along with a summary of past research into the possibility of extracting energy from the wake by flying close parallel trajectories. Challenges and barriers to adoption of civilian automatic wake surfing technology are identified. A hardware-in-the-loop simulation is described that will support future research. Finally, a roadmap for future research and technology transition is proposed.

fuel comsumption↗
NASA NTRS2014

3D Visualization of Cooperative Trajectories

Aerodynamicists and biologists have long recognized the benefits of formation flight. When birds or aircraft fly in the upwash region of the vortex generated by leaders in a formation, induced drag is reduced for the trail bird or aircraft, and efficiency improves. The major consequence of this is that fuel consumption can be greatly reduced. When two aircraft are separated by a large enough longitudinal distance, the aircraft are said to be flying in a cooperative trajectory. A simulation has been developed to model autonomous cooperative trajectories of aircraft; however it does not provide any 3D representation of the multi-body system dynamics. The topic of this research is the development of an accurate visualization of the multi-body system observable in a 3D environment. This visualization includes two aircraft (lead and trail), a landscape for a static reference, and simplified models of the vortex dynamics and trajectories at several locations between the aircraft.

aerodynamic drag↗
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 NTRS2014

NASA Armstrong Status

Armstrong (formerly Dryden) Flight Research Center continues it's legacy of exciting work in the area of dynamics and control of advanced vehicle concepts. This status presentation highlights the research and technology development that Armstrong's Control and Dynamics branch is performing in the areas of Control of Flexible Structures and Automated Cooperative Trajectories.

Flexible Structures↗
NASA NTRS2014

Multi-Vehicle Cooperative Control Research at the NASA Armstrong Flight Research Center, 2000-2014

A brief introductory overview of multi-vehicle cooperative control research conducted at the NASA Armstrong Flight Research Center from 2000 - 2014. Both flight research projects and paper studies are included. Since 2000, AFRC has been almost continuously pursuing research in the areas of formation flight for drag reduction and automated cooperative trajectories. An overview of results is given, including flight experiments done on the FA-18 and with the C-17. Other multi-vehicle cooperative research is discussed, including small UAV swarming projects and automated aerial refueling.

group dynamics↗
NASA NTRS2015

Automated Meta-Aircraft Operations for a More Efficient and Responsive Air Transportation System

A brief overview is given of the on-going NASA Automated Cooperative Trajectories project. Current status and upcoming work is previewed. The motivating factors and innovative aspects of ACT are discussed along with technical challenges and the expected system-level impacts if the project is successful. Preliminary results from the NASA G-III hardware in the loop simulation are included.

flight paths↗
NASA NTRS2015

Automated Meta-Aircraft Operations for a More Efficient and Responsive Air Transportation System

A brief overview is given of the on-going NASA Automated Cooperative Trajectories project. Current status and upcoming work is previewed. The motivating factors and innovative aspects of ACT are discussed along with technical challenges and the expected system-level impacts if the project is successful. Preliminary results from the NASA G-III hardware in the loop simulation are included.

flight paths↗
NASA NTRS1992

A method for planning collision free trajectories for two cooperating robots

Robotic systems designed specifically for space applications often exhibit a high level of autonomy to reduce the costs associated with having humans in space. This paper presents a method for autonomously planning collision free paths for two cooperating robots in a static environment. The method performs selective mapping of configuration space while attempting to locate a safe path. The method is based around an algorithm aimed at transversing a space of arbitrary dimension which contains both safe and unsafe regions. While the procedure cannot guarantee finding a solution even if one may exist, it is intended to find a solution to many practical path planning problems within a reasonable amount of time. Results are presented for applying the procedure to two cooperating six degree of freedom manipulators (Puma 560s).

Weaver, Jonathan M.↗
NASA NTRS1988

KALI - An environment for the programming and control of cooperative manipulators

A design description is given of a controller for cooperative robots. The background and motivation for multiple arm control are discussed. A set of programming primitives which permit a programmer to specify cooperative tasks are described. Motion primitives specify asynchronous motions, master/slave motions, and cooperative motions. In the context of cooperative robots, trajectory generation issues are discussed and the authors' implementation briefly described. The relations between programming and control in the case of multiple robots are examined. The allocation of various tasks among a multiprocessor computer is described.

Hayward, Vincent↗
NASA NTRS1987

Design principles of a cooperative robot controller

The paper describes the design of a controller for cooperative robots being designed at McGill University in a collaborative effort with the Jet Propulsion Laboratory. The first part of the paper discusses the background and motivation for multiple arm control. Then, a set of programming primitives, which are based on the RCCL system and which permit a programmer to specify cooperative tasks are described. The first group of primitives are motion primitives which specify asynchronous motions, master/slave motions, and cooperative motions. In the context of cooperative robots, trajectory generation issues will be discussed and the implementation described. A second set of primitives provides for the specification of spatial relationships. The relations between programming and control in the case of multiple robot are examined. Finally, the paper describes the allocation of various tasks among a set of microprocessors sharing a common bus.

Hayward, Vincent↗
NASA NTRS2018

Cooperative Automation Supporting Pilot-Dispatch Negotiation of Enroute Trajectory Change Requests

The advent of advanced technologies in communication, navigation, and surveillance is enabling more integration between the aircraft and the ground systems in managing air traffic operations. As a result, automation has evolved to provide the flight crew, air traffic controllers, and traffic flow managers with capabilities for collaborating on information access, analysis, and decision making. In this paper, we investigate different cooperative schemes between these agents, supported by automation, in managing dynamic trajectory changes while the flight is en route to improve flight and system performance. The analysis was conducted using an abstract cognitive tasking framework to identify trajectory change tasks independently from the agent performing them. Cooperation schemes were then derived by assessing different levels of cooperation on each task between the air and ground agents and their automation. The assessment was based on which automation-supported agent is more capable of performing the task and the expected benefit mechanisms that result from cooperating. The cooperation schemes were compared based on a qualitative, but objective, assessment of the benefits expected from cooperation.

air-ground integration↗
NASA NTRS1988

Optimal cooperative time-fixed impulsive rendezvous

A method has been developed for determining optimal, i.e., minimum fuel, trajectories for the fixed-time cooperative rendezvous of two spacecraft. The method presently assumes that the vehicles perform a total of three impulsive maneuvers with each vehicle being active, that is, making at least one maneuver. The cost of a feasible 'reference' trajectory is improved by an optimizer which uses an analytical gradient developed using primer vector theory and a new solution for the optimal terminal (rendezvous) maneuver. Results are presented for a large number of cases in which the initial orbits of both vehicles are circular but in which the initial positions of the vehicles and the allotted time for rendezvous are varied. In general, the cost of the cooperative rendezvous is less than that of rendezvous with one vehicle passive. Further improvement in cost may be obtained in the future when additional, i.e., midcourse, impulses are allowed and inserted as indicated for some cases by the primer vector histories which are generated by the program.

Mirfakhraie, Koorosh↗
NASA NTRS2013

Center-TRACON Automation System (CTAS) En Route Trajectory Predictor Requirements and Capabilities

This requirements framework document is designed to support the capture of requirements and capabilities for state-of-the-art trajectory predictors (TPs). This framework has been developed to assist TP experts in capturing a clear, consistent, and cross-comparable set of requirements and capabilities. The goal is to capture capabilities (types of trajectories that can be built), functional requirements (including inputs and outputs), non-functional requirements (including prediction accuracy and computational performance), approaches for constraint relaxation, and input uncertainties. The sections of this framework are based on the Common Trajectory Predictor structure developed by the FAA/Eurocontrol Cooperative R&D Action Plan 16 Committee on Common Trajectory Prediction. It is assumed that the reader is familiar with the Common TP Structure.1 This initial draft is intended as a first cut capture of the En Route TS Capabilities and Requirements. As such, it contains many annotations indicating possible logic errors in the CTAS code or in the description provided. It is intended to work out the details of the annotations with NASA and to update this document at a later time.

framework↗
NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

This paper examines strategic conflict resolution strategies for pair-wise high-altitude balloon, airship encounters. By strategic is meant that the conflict is identified (detected) at the time of flight plan submission or flight plan alteration through a service, such as an ETM Service Supplier (ESS). We investigate optimal control solutions for two classes of problems: non-cooperative, where the balloon must alter its ascent trajectory in order to avoid the airship and cooperative, where both vehicles make maneuvers through a negotiation protocol.

airship balloon negotiation optimal control↗
NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

This paper examines strategic conflict resolution strategies for pair-wise high-altitude balloon, airship encounters. By strategic is meant that the conflict is identified (detected) at the time of flight plan submission or flight plan alteration through a service, such as an ETM Service Supplier (ESS). We investigate optimal control solutions for two classes of problems: non-cooperative, where the balloon must alter its ascent trajectory in order to avoid the airship and cooperative, where both vehicles make maneuvers through a negotiation protocol.

airship balloon negotiation optimal control↗
NASA NTRSDate not supplied

Lucy Optical Navigation Performance During The (152830) Dinkinesh Encounter

The Lucy Jupiter-Trojan asteroid mission launched in November 2021. Its original mission concept included six small-body encounters over its 12-year primary mission. In the fall of 2022, an additional target of opportunity encounter was proposed to be executed in the fall of 2023. The encounter with (152830) Dinkinesh (previously 1999 VD57) presented myriad imaging, navigation, engineering, and planning challenges, as well as a chance to exercise and further refine the Optical NavigationSystem concept of operations, interfaces, and tools. Dinkinesh would be the smallest and dimmest target Lucy would encounter, with a higher uncertainty in these physical parameters than for other targets. While the Op Nav system and instruments carried a high amount of heritage from the New Horizons and OSIRIS-REx missions, this would be the first use of these systems on Lucy for navigation purposes. Despite these additional challenges, the Lucy Dinkinesh encounter was a resounding success throughout which the navigational system exceeded requirements.1Optical Navigation was successfully performed and fed into the orbit determination and trajectory maneuver activities up to the final knowledge update. The Dinkinesh encounter also proved to be greatly scientifically interesting, if not additionally challenging, as the Dinkinesh system was discovered to be a binary system through imaging during closest approach, and the secondary body was itself found to be a contact binary. This added complexity notwithstanding, the OpNav and OD teams were able to re-construct the close-approach trajectory of Dinkinesh in cooperation and concert with the Lucy Science Team’s shape modelling efforts.

Erik Lessac-Chenen↗