Effects of Automation Error Bias on Dependence and Trust in Remotely Operated Air Vehicle Operations
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Uncrewed Aerial Systems↗
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Control Concepts for Simplified Vehicle Operations of a Quadrotor eVTOL Vehicle
Urban Air Mobility (UAM) is a future mode of transportation that will require revolutionary new vehicle concepts and operations. One of the many challenges that these vehicles face is a complex flight control task in a challenging urban environment and in different flight regimes such as hover, forward flight as well as the transitions between both, with a focus on a low workload and minimum required pilot training. This paper discusses the development and evaluation of simplified vehicle operations concepts using representative aircraft models, controls, inceptors and displays. These four aspects are inseparably interconnected. This simplified vehicle operations concept will be evaluated by performing relevant operations within a realistic urban environment. Handling quality evaluations will be performed using modified Mission Task Elements, or Handling Quality Task Elements. This paper focuses on the initial development of controls, inceptors and displays.
Thomas Lombaerts↗
Streamlining Tactical Operator Handoffs During Multi-Vehicle Operations
Increased automation has shifted the operator control paradigm from a single operator controlling a single vehicle, to multiple operators collaborating to control multiple vehicles; this paradigm is known as m:N. Many questions remain unanswered in this new operational paradigm about the division of assets as workload for individual operators varies over time. This paper explores the management of workload by enabling operators to temporarily handoff vehicles among each other. A study was conducted to explore both a manual and assisted method for performing handoffs during manipulated contingency scenarios. The assisted handoff method allowed subjects to easily choose and group nominal and/or contingency vehicles. The number of contingencies was also manipulated to determine the effect workload had on how pilots utilized the ability to handoff vehicles. Results show subjects performed handoffs more often when there were more contingencies and when the assisted handoff tool was available. In addition, the assisted tool made subjects feel more comfortable, enabling them to feel like they could take longer to resolve contingency situations. Lastly, even during contingencies, subjects were able to successfully complete secondary tasks.
m:N operations↗
AS-202 launch vehicle operational flight trajectory
Launch vehicle operational flight trajectory for Saturn IB vehicle AS-202
SATURN IB LAUNCH VEHICLE↗
New technology and vehicle operation on roadways
Some concepts towards the optimum movement of vehicles on highways are presented. It is stressed that in proper perspective, the private automobile is one element of several modes of transportation. The modal mix in the long run is determined by proper land use planning, population density, and historic growth patterns. Discussion investigates the two main interacting elements of the highway in the dynamic state: the roadway and the driver operated vehicle, noting that the inefficiencies of the system are caused by the uncoordinated and disassociated actions of the two elements. Also discussed are vehicle operations on streets, highways, and freeways, on board communication, control, and guidance, systems considerations and traffic management, and benefits of optimized vehicle operations on roadways.
Humphreys, D. W.↗
Launch Vehicle Operations Simulator
The Saturn Launch Vehicle Operations Simulator (LVOS) was developed for NASA at Kennedy Space Center. LVOS simulates the Saturn launch vehicle and its ground support equipment. The simulator was intended primarily to be used as a launch crew trainer but it is also being used for test procedure and software validation. A NASA/contractor team of engineers and programmers implemented the simulator after the Apollo XI lunar landing during the low activity periods between launches.
Blackledge, J. W.↗
An Intelligent Propulsion Control Architecture to Enable More Autonomous Vehicle Operation
This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.
autonomy↗
An Intelligent Propulsion Control Architecture to Enable More Autonomous Vehicle Operation
This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.
autonomy↗
Concept, Design, & Implementation of a Remote Vehicle Operations Center for Autonomous Missions
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. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being 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. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.
CERTAIN↗
AS-204/LM-1 launch vehicle operational flight trajectory
Apollo Saturn-204/LM-1 launch vehicle operational flight trajectory
Barrett, B. B.↗
The AS-205/CSM-101 Launch Vehicle Operational Trajectory, Revision 1
AS-205/CSM-101 launch vehicle operational trajectory
Solmon, G. W.↗
System-Wide Error Attribution in Multi-Vehicle Operations: Theoretical Explanation, Implications, and Applications
In multi-vehicle aerial operation contexts, a single or several human operators (m) are responsible for managing multiple uncrewed vehicles (N). This is referred to as the m:N operational paradigm. When automation error occurs during m:N operations, especially if multiple vehicles exhibit automation error, a question is raised: What level of system globality will the error be attributed to? Globality refers to the levels within the conceptual hierarchy of a perceptual object. Significant costs could be incurred if the error is falsely attributed to a higher globality level than it should be (e.g., multiple groups of vehicles as opposed to a single vehicle), and those vehicles are subsequently subject to grounding and evaluation/maintenance. Likewise, it could be problematic if error is falsely attributed to a component (i.e., a lower globality level), resulting in ongoing, unsafe operation of vehicles that should be grounded or evaluated. Thus, to meet required safety standards and facilitate commercial viability, multi-vehicle operators must be capable of appropriately attributing the level of globality of automation error within the system they are responsible for.
Error↗
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↗
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↗
Achieving Resilient In-Flight Performance for Advanced Air Mobility through Simplified Vehicle Operations
A research and development (R&D) approach is proposed for developing and validating concepts and technologies to achieve vehicle autonomy goals of Advanced Air Mobility (AAM) through Simplified Vehicle Operations (SVO). The approach applies resilience-engineering and human-automation teaming (HAT) principles to a framework for defining vehicle-based functions for the management of missions and flight trajectories, focusing initially on the en route flight domain. To achieve the SVO goal of reducing pilot training requirements and thereby increasing the pilot pool for AAM, while at the same time promoting ever-safer operations, a framework for identifying essential functions is proposed. In this framework, functions are first categorized by high-level functional purpose (mission management, flightpath management, tactical operations, and vehicle control) and then subcategorized by attributes of resilient-performing systems (abilities to monitor, respond, learn, and anticipate). The categorization by functional purpose provides structure within which HAT designs can be holistically explored and total levels of human vs. automation responsibility can be varied. The subcategorization by resilient-system attributes provides a mechanism for capturing safety-critical functions that may not be codified in current operational procedures and training curricula, particularly those where humans proactively enhance safety in currently undocumented ways. An R&D approach consisting of seven strategies is proposed in which automation engineering and human-factors communities can collaborate in the research, development, and design of an SVO roadmap to enable the ambitious objectives of AAM.
AAM↗
NASA Lessons Learned on Reusable and Expendable Launch Vehicle Operations and Their Application Towards DARPA's Experimental Spaceplane (XS-1) Program
Outline: Selected Historic Shuttle Operations Data; Shuttle Lessons Learned Recommendations for Lower Cost, Operationally Efficient Launch Vehicle Systems; Selected Expendable launch vehicle experiences; Past NASA Launch Vehicle Development Programs, Studies (1985 to present); Discussion: Suggested applications of NASA Lessons Learned to already-baselined contractor XS-1 Phase I concepts.
Reusable launch vehicle↗
Remote Science at Sea With Remotely Operated Vehicles
Conducting sea-going ocean science no longer needs to be limited to the number of berths on a ship given that telecommunications, computing, and networking technologies onboard ships have become familiar mechanisms for expanding scientists’ reach from onshore. The oceanographic community routinely works with remotely operated vehicles (ROVs) and pilots to access real-time video and data from the deep sea, while onboard a ship. The extension of using an ROV and its host vessel’s live-streaming capabilities has been popularized for almost 3 decades as a telepresence technology. Telepresence-enabled vessels with ROVs have been employed for science, education, and outreach, giving a greater number of communities viewing access to ocean science. However, the slower development of technologies and social processes enabling sustained real-time involvement between scientists on-ship and onshore undermines the potential for broader access, which limits the possibility of increasing inclusivity and discoveries through a diversity of knowledge and capabilities. This article reviews ocean scientists’ use of telepresence for ROV-based deep-sea research and funded studies of telepresence capabilities. The authors summarize these studies findings and conditions that lead to defining the use of telepresence-enabled vessels for “remote science at sea.” Authors define remote science at sea as a type of ocean expedition, an additional capability, not a replacement for all practices by which scientists conduct ocean research. Remote science for ocean research is an expedition at-sea directed by a distributed science team working together from at least two locations (on-ship and onshore) to complete their science objectives for which primary data is acquired by robotic technologies, with connectivity supported by a high-bandwidth satellite and the telepresence-enabled ship’s technologies to support the science team actively engaged before, during, and after dives across worksites. The growth of productive ocean expeditions with remote science is met with social, technical, and logistical challenges that impede the ability of remote scientists to succeed. In this article, authors review telepresence-enabled ocean science, define and situate the adjoined model of remote science at sea, and some infrastructural, technological and social considerations for conducting and further developing remote science at sea.
marine technology↗
ASTP (SA-210) launch vehicle operational flight trajectory dispersion analysis, volume 1
In order to establish realistic deviation limits for the ASTP (SA-210) Launch Vehicle Operational Flight Trajectory, a dispersion analysis was conducted. The nominal trajectory prescribed for this analysis is the ASTP (SA-210) Launch Vehicle 500 Pound Launch Window Opening OT. The error sources considered are those associated with predictions of vehicle characteristics, vehicle systems performance, and flight environment. The nominal vehicle, the boost trajectory simulations, the error sources, the analytic procedures utilized, and the results are discussed. Launch vehicle guidance system inaccuracies were determined from the guidance error analysis. These data are composed of individual error source trajectory parameter dispersion envelopes.
Williams, N. D.↗