In Augmented Reality systems, see-through Head-Mounted Displays (HMDs) superimpose virtual three-dimensional objects on the real world. This technology has the potential to enhance a user's perception of and interaction with the real world. However, many Augmented Reality applications will not be accepted unless virtual objects are accurately registered with their real counterparts. Good registration is difficult, because of the high resolution of the human visual system and its sensitivity to small differences. Registration errors fall into two categories: static errors, which occur even when the user remains still, and dynamic errors caused by system delays when the user moves. Dynamic errors are usually the largest errors. This dissertation demonstrates that predicting future head locations is an effective approach for significantly reducing dynamic errors.
This demonstration is performed in real time with an operational Augmented Reality system. First, evaluating the effect of prediction requires robust static registration. Therefore, this system uses a custom optoelectronic head-tracking system and three calibration procedures developed to measure the viewing parameters. Second, the system predicts future head positions and orientations with the aid of inertial sensors. Effective use of these sensors requires accurate estimation of the varying prediction intervals, optimization techniques for determining parameters, and a system built to support real-time processes.
On average, prediction with inertial sensors is 2 to 3 times more accurate than prediction without inertial sensors and 5 to 10 times more accurate than not doing any prediction at all. Prediction is most effective at short prediction intervals, empirically determined to be about 80 milliseconds or less. An analysis of the predictor in the frequency domain shows the predictor magnifies the signal by roughly the square of the angular frequency and the prediction interval. For specified head-motion sequences and prediction intervals, this analytical framework can also estimate the maximum possible time-domain error and the maximum tolerable system delay given a specified maximum time-domain error.
Future steps that may further improve registration are discussed.

https://www.cs.unc.edu/techreports/95-007.pdf

Predictive tracking for augmented reality

To determine motion fidelity requirements, a series of piloted simulations was performed. Several key results were found. First, lateral and vertical translational platform cues had significant effects on fidelity. Their presence improved performance and reduced pilot workload. Second, yaw and roll rotational platform cues were not as important as the translational platform cues. In particular, the yaw rotational motion platform cue did not appear at all useful in improving performance or reducing workload. Third, when the lateral translational platform cue was combined with visual yaw rotational cues, pilots believed the platform was rotating when it was not. Thus, simulator systems can be made more efficient by proper combination of platform and visual cues. Fourth, motion fidelity specifications were revised that now provide simulator users with a better prediction of motion fidelity based upon the frequency responses of their motion control laws. Fifth, vertical platform motion affected pilot estimates of steady-state altitude during altitude repositioning. Finally, the combined results led to a general method for configuring helicopter motion systems and for developing simulator tasks that more likely represent actual flight. The overall results can serve as a guide to future simulator designers and to today's operators.

/pdf/helicopter-flight-simulation-motion-platform-requirements-38txzev5ap.pdf

Helicopter flight simulation motion platform requirements

System-identification methods compose a mathematical model, or series of models, from measurements of inputs and outputs of dynamic systems. The extracted models allow the characterization of the response of the overall aircraft or component subsystem behavior, such as actuators and on-board signal processing algorithms. This paper discusses the use of frequency-domain system-identification methods for the development and integration of aircraft flight-control systems. The extraction and analysis of models of varying complexity from nonparametric frequency-responses to transfer-functions and high-order state-space representations is illustrated using the Comprehensive Identification from FrEquency Responses (CIFER) system-identification facility. Results are presented for test data of numerous flight and simulation programs at the Ames Research Center including rotorcraft, fixed-wing aircraft, advanced short takeoff and vertical landing (ASTOVL), vertical/short takeoff and landing (V/STOL), tiltrotor aircraft, and rotor experiments in the wind tunnel. Excellent system characterization and dynamic response prediction is achieved for this wide class of systems. Examples illustrate the role of system-identification technology in providing an integrated flow of dynamic response data around the entire life-cycle of aircraft development from initial specifications, through simulation and bench testing, and into flight-test optimization.

/pdf/system-identification-methods-for-aircraft-flight-control-21ihzzn6k8.pdf

System identification methods for aircraft flight control development and validation

Trim defines conditions for both design and analysis based on aircraft models. In fact, we often define these analysis points more broadly than the conditions normally associated with trim conditions to facilitate that analysis or design. In simulations, these analysis points establish initial conditions comparable to flight conditions. Based on aerodynamic and propulsion systems models of an aircraft, trim analysis can be used to provide the data needed to define the operating envelope or the performance characteristics. Linear models are typically derived at trim points. Control systems are designed and evaluated at points defined by trim conditions. And these trim conditions provide us a starting point for comparing one model against another, one implementation of a model against another implementation of the same model, and the model to flight-derived data. In this paper we define what we mean by trim, examine a variety of trim conditions that have proved useful and derive the equations defining those trim conditions. Finally we present a general approach to trim through constrained minimization of a cost function based on the nonlinear, six-degree-of freedom state equations coupled with the aerodynamic and propulsion system models. We provide an example of how a trim algorithm is used with a simulation by showing an example from JSBSim.

/pdf/a-general-solution-to-the-aircraft-trim-problem-51wv4cdfzj.pdf

A General Solution to the Aircraft Trim Problem

Measuring Time Delays in Simulator Displays

In the problem of pure transport delay in a low-pass system, a trade-off exists with respect to performance within and beyond a frequency bandwidth. When activity beyond the band is attenuated because of other considerations, this trade-off may be used to improve the performance within the band. Specifically, transport delay in computer-generated imagery systems is reduced to a manageable problem by recognizing frequency limits in vehicle activity and manual-control capacity. Based on these limits, a compensation algorithm has been developed for use in aircraft simulation at NASA Ames Research Center. For direct measurement of transport delays, a beam-splitter experiment is presented that accounts for the complete flight simulation environment. Values determined by this experiment are appropriate for use in the compensation algorithm. The algorithm extends the bandwidth of high-frequency flight simulation to well beyond that of normal pilot inputs. Within this bandwidth, the visual scene presentation manifests negligible gain distortion and phase lag. After a year of utilization, two minor exceptions to universal simulation applicability have been identified and subsequently resolved.

/pdf/transport-delay-compensation-for-computer-generated-imagery-3i187eclxd.pdf

Transport delay compensation for computer-generated imagery systems

Computer-generated graphics in real-time helicopter simulation produces objectionable scene-presentation time delays. In the flight simulation laboratory at Ames Research Center, it has been determined that these delays have an adverse influence on pilot performance during aggressive tasks such as nap-of-the-earth (NOE) maneuvers. Using contemporary equipment, computer-generated image (CGI) time delays are an unavoidable consequence of the operations required for scene generation. However, providing that magnitide distortions at higher frequencies are tolerable, delay compensation is possible over a restricted frequency range. This range, assumed to have an upper limit of perhaps 10 or 15 rad/sec, conforms approximately to the bandwidth associated with helicopter handling qualities research. A compensation algorithm is introduced here and evaluated in terms of tradeoffs in frequency responses. The algorithm has a discrete basis and accommodates both a large, constant transport delay interval and a periodic delay interval, as associated with asynchronous operations.

/pdf/cgi-delay-compensation-58ypt3e7xz.pdf

CGI Delay Compensation

Real-time piloted aircraft simulations with digital computers have been performed at Ames Research Center (ARC) for over two decades. For the simulation of conventional aircraft models, the establishment of initial vehicle and control orientations at various operational flight regimes has been adequately handled by either analog techniques or simple inversion processes. However, exotic helicopter configurations have been introduced recently that require more sophisticated techniques because of their expanded degrees of freedom and environmental vibration levels. At ARC, these techniques are used for the backward solutions to real-time simulation models as required for the generation of trim points. These techniques are presented in this paper with examples from a blade-element helicopter simulation model.

Trimming an aircraft model for flight simulation

An aircraft landing may be described as a controlled crash because a runway surface is intercepted. In a simulation model the transition from aerodynamic flight to weight on wheels involves a single computational cycle during which stiff differential equations are activated; with a significant probability these initial conditions are unrealistic. This occurs because of the finite cycle time, during which large restorative forces will accompany unrealistic initial oleo compressions. This problem was recognized a few years ago at Ames Research Center during simulation studies of a supersonic transport. The mathematical model of this vehicle severely taxed computational resources, and required a large cycle time. The ground strike problem was solved by a described technique called anticipation equations. This extensively used technique has not been previously reported. The technique of anticipating a significant event is a useful tool in the general field of discrete flight simulation. For the differential equations representing a landing gear model stiffness, rate of interception and cycle time may combine to produce an unrealistic simulation of the continuum.

Anticipation of the landing shock phenomenon in flight simulation

Computer-generated imagery processed with corrective phase shifts at lower frequencies. Algorithm developed from transfer functions predicting lower-frequency components of response of aircraft short time into future. Effects of delay at lower frequencies counteracted by introducing compensatory phase shifts, opposite those caused by delay, into signals driving computer-generated imagery.

Richard E. Mcfarland

Papers

Transport delay compensation for computer-generated imagery systems

CGI Delay Compensation

Trimming an aircraft model for flight simulation

Anticipation of the landing shock phenomenon in flight simulation

Compensating For Computational Delay In Flight Simulation