Motion Cueing Algorithm Research Articles

DESIGNING AN EVOLUTIONARY OPTIMAL WASHOUT FILTER BASED ON GENETIC ALGORITHM

This paper aims to design a reliable filter that can transform the actual motion of a flight simulator maneuver into a logical and understandable movement for its workspace. Motion cueing algorithms are used in scaling maneuvers to improve the user’s perception of real-world motion. As a unique algorithm, the washout-filter algorithm reduces the real motions where the user cannot understand the difference between the actual and simulated maneuvers. To design a proper washout filter, first, apply the inner ear model where humans can feel the motion to design a proper filter. The Otolith and semicircular systems were represented by two parts in this model. Second, an evolutionary theory based on a genetic algorithm is used to design a structure that minimizes human perception error and workspace boundaries. The issue is determining the coefficients in the model in order to create a high-performance flight simulator. The filtering algorithm, based upon the human vestibular model, compares human perception with flight simulator motion knowledge. The findings demonstrate an objective function that minimizes user perception error, and the flight simulator motion range can prepare a reliable washout filter for motion cueing.

Influence of sensory modality and control dynamics on human path integration.

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

Auto-Tuning Parameters of the Offline Optimal Motion Cueing Algorithm with Mean-Variance Mapping Optimization

A motion cueing algorithm (MCA) not only maintains the simulator within its physical limits but also generates such movements of the driving simulator that the necessary motion cues of drivers on the realistic vehicle are equivalently reproduced. The offline optimal MCA focuses on finding the best combination of the translational acceleration and tilt angles of the motion platform to maintain drivers’ motion perception. However, the best combination depends on the MCA’s parameters, tuned mainly by trial and error with experts in the loop. Moreover, for different amplitude input signals, the parameters are accordingly modified. This manual tuning procedure is so time-consuming that the generic optimization, named Mean-Variance mapping optimization, was proposed to search the suitable parameters for the optimal algorithm. This tuning method uses the specific cost function of constraint conditions such as workspace limits, avoiding false cues, and improving motion fidelity to achieve the best parameters for optimal MCA with the particular input signals.

An optimal washout filter for motion platform using neural network and fuzzy logic

To experience the motion sensation of a real vehicle through a motion simulator, a motion cueing algorithm (MCA) is required to transform the vehicle motions to the driving motion platform (DMP) while respecting the physical limitations of DMP. In this aspect, the optimal washout filter (WF) extracts the optimal motion signals including linear accelerations and angular velocities for the DMP with consideration of the human vestibular model and DMP motion states using the linear quadratic regulator (LQR) technique. The LQR technique is employed to obtain the optimal and pre-defined higher order transfer functions by solving the Riccati equation. However, the Riccati equation is solved using fixed weights, leading to an inconvenient usage of the DMP workspace. In this research, a new optimal WF model is designed and developed using a neural network (NN) and a fuzzy logic controller (FLC). The NN is introduced to solve the Riccati equation online while the FLC model is designed to extract the weighting matrices of the LQR technique. The proposed technique considers the physical DMP limitations online and reproduces accurate motion signals with a high degree of fidelity. The results demonstrate the efficiency of the developed optimal WF model as compared with those of existing optimal WF models.

Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments.

Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user’s requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to feed the motion cueing algorithms. For this reason, this paper aims to present a new method for generating non-real-time gravito-inertial cues with motion platforms using a system fed both with computer-generated—simulation-based—images and video imagery. It is a hybrid method where part of the gravito-inertial cues—those with acceleration information—are generated using a classical approach through the application of physical modeling in a VR scene utilizing washout filters, and part of the gravito-inertial cues—the ones coming from recorded images and video, without acceleration information—were generated ad hoc in a semi-manual way. The resulting motion cues generated were further modified according to the contributions of different experts based on a successive approximation—Wideband Delphi-inspired—method. The subjective evaluation of the proposed method showed that the motion signals refined with this method were significantly better than the original non-refined ones in terms of user perception. The final system, developed as part of an international road safety education campaign, could be useful for developing further VR-based applications for key fields such as driving assistance, vehicle automation and road crash prevention.

A Design of Nonlinear Scaling and Nonlinear Optimal Motion Cueing Algorithm for Pilot’s Station

Motion cueing algorithms (MCA) are often applied in the motion simulators. In this paper, a nonlinear optimal MCA, taking into account translational and rotational motions of a simulator within its physical limitation, is designed for the motion platform aiming to minimize human’s perception error in order to provide a high degree of fidelity. Indeed, the movement sensation center of most MCA is placed at the center of the upper platform, which may cause a certain error. Pilot’s station should be paid full attention to in the MCA. Apart from this, the scaling and limiting module plays an important role in optimizing the motion platform workspace and reducing false cues during motion reproduction. It should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the MCA. A nonlinear scaling method is designed to accurately duplicate motions with high realistic behavior and use the platform more efficiently without violating its physical limitations. The simulation experiment is verified in the longitudinal/pitch direction for motion simulator. The result implies that the proposed method can not only overcome the problem of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation, but also reduce the false cues to improve dynamic fidelity during the motion simulation process.

Development and Evaluation of a Threshold-Based Motion Cueing Algorithm

In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into account when parameterizing the MCA. The washout is compensated by tilt coordination and means of feedback from the translational acceleration. The proposed MCA is compared with the classical washout algorithm and the compensation MCA based on selected qualitative metrics and their workspace demand. In addition, a subjective study on the evaluation of the MCA was conducted. The results show that even high washout rates are not noticeable by the test subjects. Overall, the MCA was rated as very good.

Impact of Human-Centered Vestibular System Model for Motion Control in a Driving Simulator

This study presents a driving simulator experiment to evaluate three different motion cueing algorithms based on model predictive control. The difference among these motion strategies lies in the type of mathematical model used. The first one contains only the dynamic model of the platform, while the others integrate additionally two different vestibular system models. We compare these three strategies to discuss the tradeoffs when including a vestibular system model in the control loop from the user’s viewpoint. The study is conducted in autonomous mode and in free driving mode, as both play an important role in motion cueing validation. A total of 38 individuals participated in the experiment; 19 drove the simulator in free driving mode and the remaining using the autonomous driving mode. For both driving modes, substantial differences is observed. The analysis shows that one of the vestibular system models is suitable for driving simulators, as it thoroughly restores high-frequency accelerations and is well noted by the participants, especially those in the free driving mode. Further tests are needed to analyze the advantages of integrating the chosen vestibular system model in the control design for motion cuieng algorithms. Regarding the autonomous mode, further research is needed to examine the influence of the vestibular system model on the motion performance, as the behavior of the autonomous model may implicitly interfere with subjective assessments.

False cue influence on motion cue quality for 10 motion cueing algorithms.

Motion simulators are becoming increasingly popular for many applications in which human sensation is important to replicate and optimize target motions. For the emulation of the perceived human acceleration, motion cueing algorithms (MCAs) have been proposed in the literature that mimics the motion sensation by a combination of actual acceleration and tilted gravity effects, termed g-force or specific force. However, their relative performance has not yet been analyzed. This paper reviews existing families of MCAs and compares their performance for a simple offline S-shaped planar test trajectory featuring only lateral acceleration. The comparison is carried out both numerically using two previously published objective measures, the "performance indicator" of Pouliot, Gosselin, and Nahon, and the "good criterion" of Schmidt, as well as subjectively by a preliminary passenger rating on a real motion platform-Robocoaster testbed. The results show that (a) the novel optimizing MCA group exploits more effectively the workspace of the motion platform than the traditional MCA group for reducing false cue with small scale error and shape errors, (b) path-dependent tuning of MCA parameters may improve motion sensation, (c) average subjective ratings can be made to correlate well with the "good criterion" when expanded with a penalty for false angular velocity cues, and (d) the scale error of specific force seems to play the most important role to the evaluation of test subject on the motion cue quality. However, still a strong variance in subjective ratings was observed, making further research necessary.

Prediction of Motion Simulator Signals Using Time-Series Neural Networks

A motion cueing algorithm (MCA) is employed to transform the linear and angular motion signals generated from a motion simulator without violating the physical and dynamical boundaries of the motion platform. In this respect, the accurate prediction of the motion scenarios is essential to enhance the efficiency of the MCA using prepositioning or time-varying reference model predictive control. While a recent approach that utilizes a feedforward neural network (NN) to forecast the motion scenarios is useful, the feedforward NN model has only forward dynamics relating to the signals without any feedback loop. In this article, a time-delay feedforward NN, a recurrent NN, and a nonlinear autoregressive (NAR) models with three different training procedures, i.e., Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient, are exploited to predict the motion scenarios. As the NAR model employs the historical signals as the inputs, it can predict the motion scenarios with higher accuracy rates. Based on the series of empirical evaluations, NAR trained with Levenberg–Marquardt is able to outperform the other two counterparts in producing more accurate predictions of the motion signals. The NAR method has a lower computational load as compared with that of the recurrent NN, facilitating its real-time application. In addition to the MCA, the NAR method can be employed in other areas, including autonomous vehicles and motion sickness studies. It can also be easily implemented for air, sea, and/or land vehicle simulators for training purposes in virtual reality environments.

Eigenmode Distortion: A New Perspective on Motion Cueing Fidelity in Flight Simulation

Eigenmode distortion is a novel quantitative methodology developed to objectively evaluate motion cueing fidelity in flight simulation. It relies on an explicit coupling of linearized vehicle and Motion Cueing Algorithm dynamics. Modal analysis subsequently performed on this coupled system reveals the degree of distortion imposed by the Motion Cueing Algorithm on to the dynamics of the simulated vehicle. Eigenmode distortion thereby provides unprecedented insight into the combined dynamics of the two systems along modal coordinates. Compared with existing methods for motion cueing fidelity assessment, the eigenmode distortion method enables a systematic analysis of the coupled vehicle and Motion Cueing Algorithm dynamics. This is mainly because it does not consider the Motion Cueing Algorithm in isolation and does not inherently rely on assumptions regarding the excitation of the simulated vehicle dynamics. This paper outlines the theoretical foundation of the eigenmode distortion method and includes a case study on helicopter longitudinal dynamics and a sensitivity analysis to demonstrate its utility. The results presented in this paper shown that the eigenmode distortion method can reveal interactions between the Motion Cueing Algorithm and the vehicle dynamics that are currently not captured by other established methods, such as the Sinacori–Schroeder criteria and the Objective Motion Cueing Test.

Driving simulator study of the relationship between motion strategy preference and self-reported driving behavior

Faithful motion restitution in driving simulators normally focuses on track monitoring and maximizing the platform workspace by leaving aside the principal component—the driver. Therefore, in this work we investigated the role of the motion perception model on motion cueing algorithms from a user’s viewpoint. We focused on the driving behavior influence regarding motion perception in a driving simulator. Participants drove a driving simulator with two different configurations: (a) using the platform dynamic model and (b) using a supplementary motion perception model. Both strategies were compared and the participants’ data were classified according to the strategy they preferred. To this end, we developed a driving behavior questionnaire aiming at evaluating the self-reported driving behavior influence on participants’ motion cueing preferences. The results showed significant differences between the participants who chose different strategies and the scored driving behavior in the hostile and violations factors. In order to support these findings, we compared participants’ behaviors and actual motion driving simulator indicators such as speed, jerk, and lateral position. The analysis revealed that motion preferences arise from different reasons linked to the realism or smoothness in motion. Also, strong positive correlations were found between hostile and violation behaviors of the group who preferred the strategy with the supplementary motion perception model, and objective measures such as jerk and speed on different road segments. This indicates that motion perception in driving simulators may depend not only on the type of motion cueing strategy, but may also be influenced by users’ self-reported driving behaviors.

An Optimal Motion Cueing Algorithm Using the Inverse Kinematic Solution of the Hexapod Simulation Platform

The vehicle motion signals is not able to be replicated using the simulation-based motion platform (SBMP) because of the workspace boundaries. The workspace limitations are determined based on the displacement, velocity and acceleration limitations of the actuators. The motion cueing algorithms (MCAs) are introduced to reproduce the motion sensation for the driver of the SBMP same as the real vehicle's driver while keeping the SBMP inside the actuators' limitations. The optimal MCA was developed to decrease the human motion sensation error between the real vehicle's driver and the SBMP's user based on the human vestibular system model using the linear quadratic regulator method. However, the inverse acceleration kinematics model of the SBMPs are not considered in developing optimal MCA to control the displacement, velocity and acceleration of actuators. The lack of inverse acceleration kinematics consideration inside the optimal MCA causes the poor consumption of the SBMP's workspace, as the optimal MCA only considers the boundaries of the SBMP in the Cartesian coordinate system. In this paper, the new optimal MCA based on the inverse acceleration kinematic solution of the SBMP is designed and developed to regenerate the more realistic motion sensation. The validation of the new optimal MCA is performed using Simulink/MATLAB. The outcomes demonstrate that using the new optimal MCA will reach a better motion sensation with less false motion signals in contrast with the existing optimal MCAs.

Adaptive Motion Cueing Algorithm Based on Fuzzy Logic Using Online Dexterity and Direction Monitoring

Motion cueing algorithm (MCA) is used to reproduce the realistic driving motion feeling for the user of the virtual vehicle using linear acceleration and angular velocity motion signals while respecting the physical constrains of the simulation-based motion platform. Classical washout filter is the most common and commercial type of the MCA because of easy tuning, short processing time, and simplicity. The classical washout filter is a conservative method in using workspace because of the worst-case scenario tuning technique. Also, the existing adaptive washout filter based on time-varying cut-off frequency does not consider the dexterity of the motion platform as considering this factor leads to use the high acceleration areas of the platform workspace. In this article, an adaptive washout filter is designed and developed based on time-varying cut-off frequency using dexterity and direction monitoring factors to use the available high acceleration areas of the platform for better generation of the high-frequency part of the motion signal. The proposed adaptive washout filter is composed of three fuzzy logic units for each mode including longitudinal, lateral, yaw, and heave and each unit has two inputs including motion sensation error between the real vehicle driver and user of the motion platform and the weight of the end-effector which is a parameter based on dexterity and current position of the end-effector. Each fuzzy logic unit has also one output which is the selected cut-off frequency of the related washout filter. The proposed adaptive washout filter uses the high dexterous areas of the workspace because it considers the dexterity of the motion platform in calculation of the end-effector weight as the second input of the fuzzy logic units. The proposed adaptive washout filter is validated in MATLAB/Simulink programming language and the experimental results indicate that the proposed adaptive washout filter performed better than existing adaptive and classical washout filters in terms of motion sensation error between the driver of real vehicle and the user of motion platform as well as usage of the platform workspace.

Motion Cueing Control Design Based On A Nonlinear Mpc Algorithm

Abstract This paper deals with the analysis and design of motion cueing algorithms which can be formulated in terms of constrained control problems. Automotive manufacturers using driving simulation for ADAS development need to design their controllers, called Motion Cueing Algorithms (MCA), in order to render the expected feelings to the subjects. Ultimately, the goal is to bring the virtual driving environment to the realism of the driving scenes. Our contribution in the present work is focused on the development of two nonlinear MPC schemes that include the inherent physical constraints but consider also the real-time requirements. The first one privileges the lateral linear movement of the platform while the other focuses on the nonlinear rotational part. The paper summarizes this line of development with a horizon-based design of MCA using the trade-off between computational effort and tracking performance.

MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is nonlinear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and use maximum workspace. Furthermore, adaptive weights-based tuning is used to smooth the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace use.

Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations

Motion simulation platforms (MSPs) are widely used to generate driving/flying motion sensations for the users. The MSPs have a restricted workspace area due to the dynamical and physical restrictions of the Motion Platforms active joints as well as the physical limitations of its passive joints. The motion cueing algorithm (MCA) is the reproduction of the motion signal including linear accelerations and angular velocities. It aims to simultaneously respect the MSP's workspace limitations and make the same motion feeling for the user as a real vehicle. The Classical washout filter (WF) is a well-known type of MCA. The classical WF is easy to set-up, offers a low computational burden and high functionality but has some major drawbacks such as fixed WF parameters tuned according to worst-case scenarios and no consideration of the human vestibular system. As a result, adaptive WFs were developed to consider the human vestibular system and enhance the efficiency of the method using time-varying filters. The existing adaptive WFs only cogitate the boundaries of the end-effector in the Cartesian coordinate space as a substitute for the active and passive joints limitations, which is MSP's main limiting factor. This conservative assumption reduces the available workspace area of the MSP and increases the motion sensation error for the MSPs user. In this study, a fuzzy logic-based WF is developed, to consider the dynamical and physical boundaries of the active joints as well as the physical boundaries of the passive joints. A genetic algorithm is used to select the membership functions' values of the active and passive joints' boundaries. The model is designed using MATLAB /Simulink and the outcomes demonstrate the efficiency of the proposed method versus existing adaptive WFs.

Prepositioning of a Land Vehicle Simulation-Based Motion Platform Using Fuzzy Logic and Neural Network

The motion cueing algorithms (MCAs) is the method that reproduces the motion sensation of the real vehicle for the users of simulation-based motion platforms (SBMPs). Classical MCA is the most common type of MCA due to its simplicity, easy tunning procedure, and higher speed. The fixed neutral position of the SBMP restricts the available workspace and makes the motions of the platform conservative. Then, the high frequency motion inputs cannot be reproduced precisely using SBMP and it increases the motion sensation error between the SBMP and real vehicle driver. The main objective of this study is to enlarge the reachable workspace of the SBMP adaptively using the new prepositioning technique based on fuzzy logic and artificial neural network (ANN). Prepositioning of the SBMPs to an off-centre point can increase the available linear space of the SBMPs based on different driving scenarios to decrease the motion sensation error. The fuzzy logic-based units are working based on the current position of the SBMP and the predicted motion signals. Therefore, the ANN is employed to forecast the motion signal of the SBMP on the finite history of input. The prepositioning of the SBMP using fuzzy logic and ANN is established for the first time in this research with consideration of the road information and vehicle motion information. The proposed method is performed and validated using MATLAB/Simulink and the outcomes show that using the new prepositioning method will lead to better driving sensation with less wrong motion sensation errors compared with the non and previous prepositioning methods.

A Motion Cueing Algorithm Based on Model Predictive Control Using Terminal Conditions in Urban Driving Scenario

The motion cueing algorithm (MCA) is in charge of the real vehicle motion feeling regeneration for the driver of the simulation-based motion platform (SBMP) with respect to its limitations. The model predictive control (MPC) has been newly employed in developing MCAs to calculate the optimal input signals for delivering the best motion feeling to the SBMP's drivers while respecting the boundaries of the platform. The stability of the MCA based on MPC has become one of the main issues for some scenarios, such as an urban driving scenario, which involves sudden decelerations/accelerations (stop and start moving), sharp and large turn, and slalom movement. The urban driving scenario destabilizes the current MCA based on MPC and leads the undesired motion fluctuations, which create an unpleasant motion artifact for the SBMP drivers. Therefore, the displacement of the SBMP should be penalized conservatively to respect the workspace boundaries for all driving scenarios. This will make the motion conservative and can cause some motion-feeling error. In this article, the concept of terminal conditions (weights and states) are employed for the first time to design and develop a new generation of MCA based on MPC to enhance the performance of the model for different scenarios, such as the heavy-traffic scenario in the urban areas. Also, the stability of the MPC by considering the terminal conditions is investigated in the MCA domain. Then, the MCA based on MPC by considering terminal conditions is developed using the MATLAB software with a presentation of the urban motion scenario. The outcomes demonstrate the effectiveness of the designed model with a common MCA based on MPC without the consideration of the terminal conditions.

A sliding mode-based approach to motion cueing for virtual reality gaming using motion simulators

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.