The NASA Advanced Concepts Flight Simulator - A unique transport aircraft research environment

Blake, Matthew

doi:10.2514/6.1996-3518

Cited by 23 publications

(9 citation statements)

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“…visual, auditory, or haptic cues); and they allow simulating the task in a flexible way to adapt it to users' needs and training goals (Derossis et al, 1998;Gosselin et al, 2010). VR systems are used to provide practical training in a number of human activities, including medical training (Albani & Lee, 2007;Basdogan et al, 2004;Derossis et al, 1998;Gosselin et al, 2010), rehabilitation (Alamri, Eid, Iglesias, Shirmohammadi, & El Saddik, 2008), flight (Blake, 1996), calligraphy (Henmi & Yoshikawa, 1998;Nishino et al, 2010), painting (Baxter et al, 2001), and industrial design (Portillo-Rodriguez et al, 2006). AR systems are powerful tools for learning because the virtual information is superimposed on the real, physical environment, which augments experience and interaction.…”

Section: Introductionmentioning

confidence: 99%

Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks

Gavish

Gutiérrez

Webel

et al. 2013

Interactive Learning Environments

524

240

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The current study evaluated the use of virtual reality (VR) and augmented reality (AR) platforms, developed within the scope of the SKILLS Integrated Project, for industrial maintenance and assembly (IMA) tasks training. VR and AR systems are now widely regarded as promising training platforms for complex and highly demanding IMA tasks. However, there is a need to empirically evaluate their efficiency and effectiveness compared to traditional training methods. Forty expert technicians were randomly assigned to four training groups in an electronic actuator assembly task: VR (training with the VR platform twice), Control-VR (watching a filmed demonstration twice), AR (training with the AR platform once), and Control-AR (training with the real actuator and the aid of a filmed demonstration once). A post-training test evaluated performance in the real task. Results demonstrate that, in general, the VR and AR training groups required longer training time compared to the Control-VR and Control-AR groups, respectively. There were fewer unsolved errors in the AR group compared to the Control-AR group, and no significant differences in final performance between the VR and Control-VR groups, probably due to a ceiling effect created by the use of two training trials in the selected task for participants who were expert technicians. The results suggest that use of the AR platform for training IMA tasks should be encouraged and use of the VR platform for that purpose should be further evaluated

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Section: Introductionmentioning

confidence: 99%

Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks

Gavish

Gutiérrez

Webel

et al. 2013

Interactive Learning Environments

524

240

View full text Add to dashboard Cite

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“…The corresponding state ( € x) is modeled following the command ( € x c ) as a first order system with a rate limit, as described in Eq. (9).…”

Section: Aircraft Modelingmentioning

confidence: 95%

“…7) is equipped with a six degree-of-freedom motion system, programmable FMS and flight displays, digital sound and aural cueing system, and a 180-degree field of view visual system. 9 The simulated aircraft is representative of a mid-size two-engine jet transport with general characteristics similar to a Boeing 757 aircraft.…”

Section: A Simulator Descriptionmentioning

confidence: 99%

Implementation of a Trajectory Prediction Function for Trajectory Based Operations

Benavides

Kaneshige

Sharma

et al. 2014

AIAA Atmospheric Flight Mechanics Conference

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This paper describes the implementation and evaluation of a trajectory prediction function. This function is a critical component of tactical flight management, a new concept that can increase the resiliency and robustness of trajectory based operations through a paradigm shift that improves Flight Management System (FMS) compatibility with tactical operations. The trajectory prediction function generates and continually updates the fourdimensional flight path that will be flown by the FMS. This motion-based trajectory represents an extension of the aircraft's current state, and incorporates control laws, mode transition logic, and drag estimation as part of the prediction. The predicted trajectory is then displayed on navigation and vertical situation displays in an effort to reduce mode confusion occurrences and increase situational awareness of what the automation is doing now and what it will do in the future. These display features were evaluated in the Advanced Concepts Flight Simulator at NASA Ames Research Center to investigate the impact on flight crew energy state awareness when operating in the highly constrained and dynamic environment of the Next Generation Air Transportation System. Commercial airline crews flew multiple optimized profile descents under two conditions. In one condition, crews were presented with standard navigation displays, including a Vertical Situation Display (VSD). In the second condition, trajectory predictions were added to both the lateral map display and the VSD. Results show that predictive trajectory displays have the potential to improve situational awareness of the future automation mode and energy state of the aircraft, and that prediction accuracy and computational times are sufficient to support more advanced use in tactical flight management.

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“…A detailed description of this facility can be found in Sullivan and Soukup 3 and Blake. 4 The Stewart platform is a complex and expensive mechanism because the control of its motion presents great technological difculties because of the telescopic actuators. It is a well-known fact in motion simulators circles that the operation of a telescopic actuator presents dif culties, especially at large amplitudes and large accelerations.…”

Section: Introductionmentioning

confidence: 99%

Tensegrity Flight Simulator

Sultan

Corless

Skelton

2000

Journal of Guidance, Control, and Dynamics

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In this paper we propose a new motionsimulatorbased on a tendon-controlledtensegrity structure. The simulator is equipped with a nonlinear controller that achieves robust tracking of desired motions. The controller parameters can be tuned to guarantee tracking to within a prespeci ed tolerance and with a prescribed rate of exponential convergence. The design is veri ed through numerical simulations for speci c longitudinal motions of a symmetric aircraft. Nomenclatureb = side length of the base and cabin triangles C d = aerodynamic coef cient of drag C l = aerodynamic coef cient of lift C M = aerodynamic coef cient of the pitching moment c = mean aerodynamic chord of aircraft wing D = drag D r = rest-length of diagonal tendons D 0 = length of diagonal tendons in a prestressable con guration g = gravitational acceleration constant of the Earth h g = overlap I = identity matrix J = inertia matrix of the simulator's second stage J a = aircraft moment of inertia around the pitch axis K = Hessian of the potential energy k = tendon stiffness k a i = stiffness of the i th active tendon k n = stiffness of the nth tendon L = lift l = length of bars l a i = length of the i th active tendon l n = length of the nth tendon l a ri = rest-length of the i th active tendon l rn = rest-length of the nth tendon M(q)= tensegrity simulator inertia matrix M t = mass of the second stage M y = aerodynamic pitching moment about the aircraft mass center m a = aircraft mass n u = number of active tendons P = pretension coef cient q = generalized coordinates vector for simulator q a = generalized coordinates vector for aircraft q g = simulator equilibrium con guration q 0 = simulator prestressable con guratioñ q = tracking error r = tracking tolerance S = lifting surface of the wing S r = rest-length of saddle tendons S 0 = length of saddle tendons in a prestressable con guration Engineering Mechanics. Fellow AIAA.T D = tension in diagonal tendons in a prestressable con guration T S = tension in saddle tendons in a prestressable con guration T n = tension in the nth tendon T r = aircraft thrust T a = vector of tensions in active tendons T p = vector of tensions in passive tendons t = time V = potential elastic energy v = aircraft speed X, Y, Z = Cartesian coordinates of the mass center of the second stage X a = aircraft horizontal range Z a = aircraft altitude a a = aircraft angle of attack c = rate of exponential convergence d e = elevator de ection angle d f = ap de ection angle h a = aircraft pitch angle q a = air density s = time constant w , u , h = Euler angles of the second-stage reference frame x y = aircraft pitch rate

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The NASA Advanced Concepts Flight Simulator - A unique transport aircraft research environment

Cited by 23 publications

References 1 publication

Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks

Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks

Implementation of a Trajectory Prediction Function for Trajectory Based Operations

Tensegrity Flight Simulator

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