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The object of research is motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft. One of the most problematic places is lack of statement and effective solution of the problem to ensure high-quality motion cues along angular degrees of freedom on flight simulators, which would correspond to motion cues along angular degrees of freedom in real flight with the same control actions. In the course of the research, on the basis of the peculiarities of human movement perception, a set of characteristic attributes of perception of motion cues is determined: character, direction, duration, intensity and time of motion perception (according to Gibson’s perception theory). Based on the system approach principles, the mathematical formulation of the solution to the problem of motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft is used. Such approach made it possible, taking into account the existing constructive resource of flight simulator motion system, to bring as close as possible motion cueing along angular degrees of freedom on flight simulators of non-maneuvering aircraft to motion cues along angular degrees of freedom in real flight with the same control actions. Due to this the character and direction of motion cues fully correspond to the real motion cues, the difference between the perception time of motion cues on airplane and simulator is minimal and meets the current requirements. The duration and intensity of the motion cue perception on simulator are proportional duration and intensity of motion cue perception on airplane. Such approach significantly improves the quality of training and retraining of pilots on flight simulators. Implementation of the developed problem formulation on aircraft simulators, in particular on An-74TK-200, showed its high efficiency. In the future, the proposed approach can be used on flight simulators of aircraft developed in Ukraine and modernization of operated flight simulators. [ABSTRACT FROM AUTHOR]

To migrate Loss of Control In-flight, the number one cause of aviation fatalities, pilots need to undergo upset prevention and recovery training with flight simulators. The fidelity of a moving base flight simulator is greatly dependent on the washout algorithm of the Stewart platform, which may reach the workspace limits when simulating the aircraft recovery from upset conditions. In this paper, a washout algorithm optimal design method based on the model predictive control technique is proposed for flight simulator upset prevention and recovery training. The parameters of the washout algorithm are calculated directly based on the platform model, and the system limits are explicitly taken into account. The human perception model is incorporated into the optimization problem, for which the objective is to minimize the pilot's perceived motion mismatch between the real flight and the simulator training. Simulations are conducted and compared with the classical filter-based washout algorithm. Responses of the flight simulator model show that the proposed method can improve the motion cueing effect when the aircraft is in upset conditions. [ABSTRACT FROM AUTHOR]

Compared to other information sources, motion cues provide a pilot with anticipatory information about spatial position and movement of aircraft. For motion cueing a flight simulator cockpit is installed on a motion system, movement of which motion cueing. Therefore, motion system is one of the most important components of full flight simulators. The problem of effective use of constructive resources of six-degrees of freedom synergistic motion system has been solved. But the problem of improving the motion cueing remained unsolved, due to the fact that location of motion system center of rotation is significantly different from location of aircraf’s center of gravity, and motion cues differ from real, flight one. The study subject is motion cueing on flight simulators. The problem was solved on the basis of simplified operator for transformation of motion system movements along individual degrees of freedom into jack movements, cubic spline functions to describe the dependence of the centers of rotation along pitch and yaw, and optimization theory using the deformable polyhedron method. The formulated and solved problem of taking into account of location of aircraft’s center of gravity during motion cueing along pitch and yaw increases an efficiency of using of constructive resource of a six-degrees of freedom synergistic motion system, a motion cueing fidelity and training realism on flight simulator.

The object of the study is a six degrees-of-freedom motion system of the synergistic type of a flight simulator. The latter is the main technical means for training pilots and means of research and development of aircraft. The task of optimal utilization of its structural resources was solved, which provides an opportunity to improve the quality of motion cueing. The result is the deve)loped method, which ensures optimal use of structural resources of motion systems of flight simulators. This is explained, first of all, by the use of the developed simplified operator for converting the movements of jacks into the movement of a motion system along individual degrees of freedom on the basis of quadratic approxi)mation. Given this, it became possible to describe the coordinates of the centers of the axes of rotation of the motion system by cubic spline functions. Secondly, the solution of the task of estimating the structural resources of the motion system along linear degrees of freedom on the basis of the developed criterion was carried out by an effective modified method of the deformed polyhedron. This method combines the random search method in the first steps of the search and the gradient method in determining the global extremum. Thirdly, the problem of determining the dependence of the coordinates of the pitch and yaw axes along the pitch angle was stated and solved. Owing to the optimal utilization of the structural resources of motion systems, the coordinates of the axis of their rotation along pitch are as close as possible to the coordinate of the axis of the aircraft. Thus, the quality of motion cueing on the flight simulator is significantly increased and it is possible to use motion systems with shorter lengths of jacks, and therefore, to reduce the cost of their manufacture and operation. [ABSTRACT FROM AUTHOR]

Compared to other information sources, motion cues provide a pilot with anticipatory information about spatial position and movement of aircraft. For motion cueing a flight simulator cockpit is installed on a motion system, movement of which motion cueing. Therefore, motion system is one of the most important components of full flight simulators. The problem of effective use of constructive resources of six-degrees of freedom synergistic motion system has been solved. But the problem of improving the motion cueing remained unsolved, due to the fact that location of motion system center of rotation is significantly different from location of aircraf’s center of gravity, and motion cues differ from real, flight one. The study subject is motion cueing on flight simulators. The problem was solved on the basis of simplified operator for transformation of motion system movements along individual degrees of freedom into jack movements, cubic spline functions to describe the dependence of the centers of rotation along pitch and yaw, and optimization theory using the deformable polyhedron method. The formulated and solved problem of taking into account of location of aircraft’s center of gravity during motion cueing along pitch and yaw increases an efficiency of using of constructive resource of a six-degrees of freedom synergistic motion system, a motion cueing fidelity and training realism on flight simulator. [ABSTRACT FROM AUTHOR]

To migrate Loss of Control In-flight, the number one cause of aviation fatalities, pilots need to undergo upset prevention and recovery training with flight simulators. The fidelity of a moving base flight simulator is greatly dependent on the washout algorithm of the Stewart platform, which may reach the workspace limits when simulating the aircraft recovery from upset conditions. In this paper, a washout algorithm optimal design method based on the model predictive control technique is proposed for flight simulator upset prevention and recovery training. The parameters of the washout algorithm are calculated directly based on the platform model, and the system limits are explicitly taken into account. The human perception model is incorporated into the optimization problem, for which the objective is to minimize the pilot’s perceived motion mismatch between the real flight and the simulator training. Simulations are conducted and compared with the classical filter-based washout algorithm. Responses of the flight simulator model show that the proposed method can improve the motion cueing effect when the aircraft is in upset conditions.

Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

Motion simulators have been of significant importance for the aviation sector in training pilots. However, the present boom in the utilization of robotics for virtual reality (VR) gaming has given rise to a new application of motion simulators. Motion cueing algorithms (MCA) play a key role in mapping the motions from a gaming scenario to the workspace of a simulator. This workspace is small (as compared to the gaming world), and on reaching the boundary, it becomes necessary to saturate the motion. Each degree of freedom, in the Cartesian space, is saturated between two fixed extremities. This hampers the perception of motion of a user enjoying the scenario. In order to address this practical problem, we make an attempt to enlarge the workspace and develop a mathematical methodology to prevent the simulator from exiting a non-cuboidal workspace. To do so, we propose sliding mode-based cueing algorithm (SMCA), which makes the simulator to slide in close proximity across the boundary of workspace. We make use of discrete-time models to present this methodology in order to ensure straightforward implementation by researchers in the future. Veracity of SMCA is testified by means of experimentation on SP7 motion simulator. The experimental results give evidence of a 57% increase in the considered sub-workspace, thereby reducing the relative necessity to saturate the motions as compared to classical MCA. This leads to a better experience of a user enjoying the VR scenario. On the other hand, the following drawbacks are reported: (1) necessity to analytically model the workspace boundary and ensuring that it is smooth with nonzero gradient, (2) SMCA parameter selection is more cumbersome than classical MCA, thereby making its utility restricted to recorded scenarios. [ABSTRACT FROM AUTHOR]

Motion platforms and motion cueing algorithms (MCA) have been included in virtual reality applications for several decades. They are necessary to provide suitable inertial cues in vehicle simulators. However, the great number of operational constraints that these devices and algorithms suffer, namely limited physical space, elevated costs, absence of sufficient power, difficulty of tuning and lack of standardized assessment methods, have hindered their widespread use. This work tries to clarify open questions in the field, such as: How important is MCA tuning? How much does size, number of DOF and power/latency matter? Can the absence of motion be better than poor motion cueing? What are the key factors that should be addressed to enhance the design of these devices? Although absolute certain answers cannot be given, this paper tries to clarify these research questions by performing massive experiments with simulated motion platforms of different types, sizes and powers. The information obtained from these experiments will be important to customize the design of real devices for this particular use. Ideally, subjective experiments with human experts would have been preferred. However, the use of simulated devices allows comparing many different motion platforms. In this paper, forty of these devices are simulated, optimized by means of a heuristic algorithm and compared with objective indicators in order to measure their relative performance using the classical MCA, something that would require an unreasonable amount of effort with real users and real devices. The obtained results show that MCA tuning is of the utmost importance in motion cueing. They also suggest that high power can usually compensate for lack of size and that a 6-DOF motion platform slightly improves the performance of a 3-DOF motion platform. [ABSTRACT FROM AUTHOR]

This article investigates the potential of a motion-based driving simulator in the field of vehicle dynamics testing, specifically for heavy vehicles. For this purpose, a case study was prepared embodying the nature of a truck dynamics test setup. The goal was to investigate if the drivers in the simulator could identify the handling differences owed to changes in vehicle parameters, while driving the simulated trucks. Results show that the drivers could clearly identify the differences in vehicle behaviour for most of the performed tests, which motivates further investigative work in this area and exposes the feasibility of heavy vehicle dynamics testing in simulators. [ABSTRACT FROM AUTHOR]

• Optimal trajectories for motion simulators can be calculated offline. • Motion simulator parameters can be optimized simultaneously with the trajectories. • Human sensory model can be taken into account in the optimization. • The software to perform such optimization is developed and made publicly available. This paper presents a method to simultaneously compute optimal simulator motions and simulator parameters for a predefined set of vehicle motions. The optimization can be performed with a model of human motion perception or sensory dynamics taken into account. The simulator dynamics, sensory dynamics, and optimality criterion are provided by the user. The dynamical models are defined by implicit index-1 differential-algebraic equations (DAE). The direct collocation method is used to find the numerical solution of the optimization problem. The possible applications of the method include calculating optimal simulator motion for scenarios when the future motion is perfectly known (e.g., comfort studies with autonomous vehicles), optimizing simulator design, and evaluating the maximum possible cueing fidelity for a given simulator. To demonstrate the method, we calculated optimal trajectories for a set of typical car maneuvers for the CyberMotion Simulator at the Max-Planck Institute for Biological Cybernetics. We also optimize the configurable cabin position of the simulator and assess the corresponding motion fidelity improvement. The software implementation of the method is publicly available. [ABSTRACT FROM AUTHOR]

• Simulator replicates the leans illusion when the pilot's head position is varied. • Findings can help to choose the kinematic characteristics of an actual SD simulator. • Human-perception-based approach can be useful to evaluate motion cueing algorithms. • The proposed approach is independent of the technology within the motion system. Spatial disorientation (SD) is one of the major threats to aircraft pilots, therefore a ground-based SD training is an important factor in reducing the SD‐related accidents. This paper examines some of the kinematic issues relating to the motion control of the SD simulator. The problem of kinematics concerns the evaluation of the impact of the pilots' head position on their own perceived motion cues. It was interesting whether the simulator was able to replicate the leans illusion with the same quality when the translational position of the head has been changed. This problem was solved by numerical calculations, in which the pilot's head kinematics were used. The SD simulator was modeled as a three-joint revolute robotic manipulator, where the head is the end effector. We found that the simulator was able to replicate the leans illusion with the same quality when the head position was changed. Obtained results can help to choose the kinematic characteristics of an actual SD simulator, and could be useful in developing an illusion at novel flight profile. [ABSTRACT FROM AUTHOR]

• Developed a new objective method of assessing driving simulator fidelity. • Showed that the method is sensitive to different simulator configurations. • A large motion base can be effective for reproducing real world low mu conditions. • Proper representation of lateral friction is critical to reproduce low mu conditions. Driving simulators can be used to test vehicle designs earlier, prior to building physical prototypes. One area of particular interest is winter testing since testing is limited to specific times of year and specific regions in the world. To ensure that the simulator is fit for purpose, an objective assessment is required. In this study a simulator and real world comparison was performed with three simulator configurations (standard, no steering torque, no motion) to assess the ability of a utility triplet of analyses to be able to quantify the differences between the real world and the different simulator configurations. The results suggest that the utility triplet is effective in measuring the differences in simulator configurations and that the developed "Virtual Sweden" environment achieved rather good behavioural fidelity in the sense of preserving absolute levels of many measures of behaviour. The main limitation in the simulated environment seemed to be the poor match of the dynamic lateral friction limit on snow and ice when compared to the real world. [ABSTRACT FROM AUTHOR]

Highlights • We propose a method to for deriving kinematic requirements of a driving simulator. • An optimized motion is calculated by using a MCA based on Model Predictive Control. • Kinematic requirements correspond to extreme positions, velocities and accelerations. • Large lateral, longitudinal ranges are needed to replicate two pre-recorded maneuvers. • Large yaw range, velocity and acceleration are needed. Abstract In motion-base driving simulators, the simulator kinematic design is a critical factor for achieving a desired simulation fidelity. This paper presents a method to derive kinematic requirements that a theoretical simulator should meet to best replicate reference maneuvers within a predefined workspace. This method uses a Motion Cueing Algorithm based on Model Predictive Control to calculate the optimal motion of the theoretical simulator. Then, extreme positions, velocities and accelerations of the theoretical simulator are used to derive kinematic requirements. The method was applied to two maneuvers previously recorded in a real car. The quality of the resulting simulator motion and kinematic requirements were evaluated for different workspaces. Results showed that the theoretical simulator was able to replicate the two maneuvers with high quality when the lateral and longitudinal ranges were at least 10 m. Interestingly, there was no evidence that a large vertical range improved the quality of the replicated motion. Furthermore, large yaw range, velocity and acceleration were needed for replicating the recorded maneuvers. These results can help to choose the kinematic characteristics of a real driving simulator. [ABSTRACT FROM AUTHOR]

Highlights • A Model Predictive Control (MPC) based Motion Cueing Algorithms (MCA) is implemented. • The MPC predictive feature is exploited to virtually extend the workspace. • A real time implementation is provided, suitable for a 100 Hz control frequency. Abstract The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called Motion Cueing Algorithms (MCAs). Model Predictive Control (MPC) has been successfully applied to MCAs, being well suited to solve constrained optimal control problems. However, the predictive aspect of the algorithm has not been exploited effectively yet, mainly due to the hard real-time requirement when using a significantly long prediction window. In this paper, a real time implementation of the so called Look-Ahead (LA) strategy is presented, that is based on an effective manipulation of the reference along the prediction horizon, and on an on-line switching policy to a Non-Look-Ahead strategy when the expected driver behavior is not reliable. An optimal tuning of the MCA is computed by means of a multi-objective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behavior are taken into account. Finally, a characterization of the driver skill level is proposed and validated in experimental environment. [ABSTRACT FROM AUTHOR]

Highlights • Optimization-based cueing algorithms show improvements w.r.t. filter-based cueing. • Continuous and overall rating result in similar perceived motion mismatch measures. • Roundabout simulations remain difficult even with optimization-based cueing. • Small detectable rotation errors are preferred over large scaling errors. • Cueing mechanism (e.g. prepositioning, scaling) usage per algorithm was identified. Abstract This paper describes a driving simulation experiment, executed on the Daimler Driving Simulator (DDS), in which a filter-based and an optimization-based motion cueing algorithm (MCA) were compared using a newly developed motion cueing quality rating method. The goal of the comparison was to investigate whether optimization-based MCAs have, compared to filter-based approaches, the potential to improve the quality of motion simulations. The paper describes the two algorithms, discusses their strengths and weaknesses and describes the experimental methods and results. The MCAs were compared in an experiment where 18 participants rated the perceived motion mismatch, i.e., the perceived mismatch between the motion felt in the simulator and the motion one would expect from a drive in a real car. The results show that the quality of the motion cueing was rated better for the optimization-based MCA than for the filter-based MCA, indicating that there exists a potential to improve the quality of the motion simulation with optimization-based methods. Furthermore, it was shown that the rating method provides reliable and repeatable results within and between participants, which further establishes the utility of the method. [ABSTRACT FROM AUTHOR]

The use of robotic motion platforms in vehicle simulators is relatively common. However, the process of testing and tuning the so-called washout algorithms, used for motion cueing generation in motion-based vehicle simulators, is complex. This process can be reduced in cost, simplified, improved, shortened and performed safer if virtual motion platforms are used instead of real devices. This paper deals with identifying a method to perform a fast but reliable simulation of parallel mechanisms to be used for motion cueing generation. The method relies on the use of Laplacian polynomial transfer function models by means of using AM-FM bi-modulated signals as reference inputs to achieve system identification. The proposed simulation scheme is implemented and assessed using optical tracking methods by testing a real 2-DOF pitch-roll parallel robot, although the method is not intrinsically restricted to this type of device. The performance of the proposed solution is also assessed with the classical washout algorithm. Results show that the accuracy of the method is satisfactory and the performance of the proposed simulation scheme is very high, being suitable to test and tune washout algorithms. [ABSTRACT FROM AUTHOR]

The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms . These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fidelity indicators, so that the process can be automated. A genetic algorithm (GA) has been recently proposed to perform this process, with promising results. Following this approach, this paper proposes applying Particle Swarm Optimization (PSO) to solve the MCA tuning problem. The PSO-based proposed solution is assessed using the classical washout MCA, comparing its performance, convergence and correctness against the GA-based solution. Results show that a PSO-based tuning of MCA can provide better results and converges faster than a GA-based one. In addition, PSO is easier to set-up than GA, since only one parameter of the optimization algorithm itself (the number of particles) needs to be set-up, instead of a minimum of four in the case of the GA. [ABSTRACT FROM AUTHOR]

Most state-of-the-art works in trajectory forecasting for automotive target predicting the pose and orientation of the agents in the scene. This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation. Also, multi-step dynamic simulation of complex multibody models does not seem to be a viable solution for real-time long-term prediction, due to the high computational time required. To bridge this gap, we present a deep-learning framework to model and predict the evolution of the coupled driver-vehicle system dynamics jointly on a complex road geometry. It consists of two components. The first, a neural network predictor, is based on Long Short-Term Memory autoencoders and fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. The second, a Bayesian optimiser, is proposed to tune some significant hyperparameters of the network. These govern the network complexity, as well as the features importance. The result is a self-tunable framework with real-time applicability, which allows the user to specify the features of interest. The approach has been validated with a case study centred on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry has been employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works. • Deep learning can be used to forecast complex driver-vehicle dynamics jointly. • 3D road geometries can be encoded using Long Short-Term Memory autoencoders. • Neural networks can be faster than multibody models in long-term vehicle simulation. • Bayesian optimisation can balance model complexity for real-time applicability. [ABSTRACT FROM AUTHOR]

The use ofdynamicdriving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate bothrealisticandfeasibleinputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200 Hz control frequency. [ABSTRACT FROM PUBLISHER]

The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race-car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race-car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance. [ABSTRACT FROM PUBLISHER]

This paper adds data to help establish fidelity criteria to accompany the simulator motion system diagnostic test specified by the International Civil Aviation Organisation. Twelve airline transport pilots flew three tasks in the NASA Vertical Motion Simulator under four different motion conditions. The experiment used three different hexapod motion configurations, each with a different trade-off between motion filter gain and break frequency, and one large motion configuration that utilised as much of the simulator's motion space as possible. The motion condition significantly affected (1) pilot motion fidelity ratings, and sink rate and lateral deviation at touchdown for the approach and landing task, (2) pilot motion fidelity ratings, roll deviations, maximum pitch rate, and number of stick shaker activations in the stall task, and (3) heading deviation after an engine failure in the take-off task. Significant differences in pilot-vehicle performance were used to define initial objective motion cueing criteria boundaries. These initial fidelity boundaries show promise but need refinement. [ABSTRACT FROM PUBLISHER]

Vehicle simulators aim to provide the user with a driving experience that replicates the sensations experienced in a real vehicle. An important aspect is the replication of motion cues. The replication of motion cues in the vehicle simulator is limited by platform workspace and the selected position control systems performance. In this work a Matlab/Simulink setup that incorporates the various aspects involved in replication of motion sensations was created. This includes input data scaling, classical washout algorithm and inverse kinematic implementations. By adjusting the input data scaling and classical washout algorithm filter parameters platform motion that adheres to the actuator motion constraints was assured. Therefore this simulation system makes the design process easier since no parameter adjusting was done on the actual motion platform. This simulation system also ensures safe operation of the vehicle simulator system for the user and ensures safety of the vehicle simulator mechanical structure. Additionally the system allowed for the derived inverse kinematic equations to be verified and evaluated the effectiveness of the classical washout motion cueing algorithm. [ABSTRACT FROM AUTHOR]

4D film is an immersive entertainment system that presents various physical effects with a film in order to enhance viewers’ experiences. Despite the recent emergence of 4D theaters, production of 4D effects relies on manual authoring. In this paper, we present algorithms that synthesize three classes of motion effects from the audiovisual content of a film. The first class of motion effects is those responding to fast camera motion to enhance the immersiveness of point-of-view shots, delivering fast and dynamic vestibular feedback. The second class moves viewers as closely as possible to the trajectory of slowly moving camera. Such motion provides an illusional effect of observing the scene from a distance while moving slowly within the scene. For these two classes, our algorithms compute the relative camera motion and then map it to a motion command to the 4D chair using appropriate motion mapping algorithms. The last class is for special effects, such as explosions, and our algorithm uses sound for the synthesis of impulses and vibrations. We assessed the subjective quality of our algorithms by user experiments, and results indicated that our algorithms can provide compelling motion effects. [ABSTRACT FROM AUTHOR]

Parallel robotic mechanisms are generally used in flight simulators with a motion-cueing algorithm to create an unlimited motion feeling of a simulated medium in a bounded workspace of the simulator. A major problem in flight simulators is that the simulation has an unbounded space and the manipulator has a limited one. Using a washout filter in the motion-cueing algorithm overcomes this. In this study, a low-cost six degrees of freedom (DoF) desktop parallel manipulator is used to test a classical motion-cueing algorithm; the algorithm's functionality is confirmed with a Simulink real-time environment. Translational accelerations and angular velocities of the simulated medium obtained from FlightGear flight simulation software are processed through a generated washout filter algorithm and the simulated medium's motion information is transmitted to the desktop parallel robotic mechanism as a set point for each leg. The major issues of this paper are designing a desktop simulation system, controlling the parallel manipulator, communicating between the flight simulation and the platform, designing a motion-cueing algorithm and determining the parameters of the washout filters. [ABSTRACT FROM AUTHOR]