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Complexity reduction in motion cueing algorithm for the ULTIMATE driving simulator

IFAC-PapersOnLine 2017年第1期50卷 10729-10734页

作者： Munir S. Hovd M. Fang Z. Olaru S. Kemeny A. Engineering Cybernetics Department Norwegian University of Science and Technology Trondheim N-7491 Norway Virtual Reality and Immersive Simulation Center RENAULT Paris France Automatic Control Department Supelec 3 rue Joliot Curie91192 France

The performance of a driving simulator depends on the efficiency of the embedded motion cueing algorithm. An explicit model predictive control was established recently as a pertinent design framework for the motion cueing algorithm. The complexity of the explicit solution increases manifold when the human vestibular model is considered. The present paper focuses on the complexity reduction of explicit solution using low complexity contractive sets for the motion cueing algorithm. The low-complexity explicit controller is formulated for the efficient control of the motion cueing system for the ULTIMATE driving simulator available in Renault. © 2017

关键词： Complexity reduction Contractive set Driving simulator Explicit model predictive control low complexity contractive sets motion cueing algorithm

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An efficient Model Predictive Control-based motion cueing algorithm for the driving simulator

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2016年第11期92卷 1025-1033页

作者： Fang, Zhou Kemeny, Andras RENAULT Virtual Real & Immers Simulat Ctr TCR AVA O 131 Ave Golf F-78288 Guyancourt France

Recently, a new technique using a MPC (Model Predictive Control)-based motion cueing algorithm has been successfully applied in driving simulation. However, the feasibility and the stability condition of MPC, a crucial criterion for high-performance simulators, has barely been addressed except in our previous works and in Dagdelen et al. (Model-based predictive motion cueing strategy for vehicle driving simulators. Contr Eng Pract 2009;17: 995-1003). In this paper, it is shown that based on an implicit MPC algorithm using qpOASES, the authors' proposed feasibility and stability condition not only guarantees the feasibility and stability of the MPC-based motion cueing algorithm, but also controls washout motion, taking into account the driver's perception threshold, thus resulting in a more robust and flexible motion cueing algorithm and a better motion feeling than that for the conventional motion cueing algorithms.

关键词： Topics driving simulation Model Predictive Control motion cueing algorithm washout

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MPC-based motion cueing algorithm with Short Prediction Horizon using Exponential Weighting

MPC-based Motion Cueing Algorithm with Short Prediction Hori...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Mohammadi, Arash Asadi, Houshyar Mohamed, Shady Nelson, Kyle Nahavandi, Saeid Deakin Univ IISRI Geelong Vic 3216 Australia

ISBN: (纸本)9781509018970

A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a prediction horizon while respecting the constraints. Reducing the prediction horizon is desirable to minimize the computational burden;however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short prediction horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

关键词： motion cueing algorithm Model Predictive Control Optimization Tuning

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Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

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VEHICLE SYSTEM DYNAMICS 2015年第4期53卷 526-545页

作者： Asadi, Houshyar Mohamed, Shady Zadeh, Delpak Rahim Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Australia

motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the

关键词： nonlinear high-pass filter washout filter genetic algorithm motion cueing algorithm human sensation

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NEW motion cueing algorithm FOR DRIVING SIMULATOR BASED ON VARIANT HARMONIC WAVELET

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2015年第1期16卷 117-126页

作者： Gong, X. M. Li, X. H. Wang, S. A. Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

As one of key technologies in movement perception simulation, motion cueing algorithm (MCA) is usually used for exploiting driving simulator within limited workspace and bounded dynamic characteristics. Several MCAs are investigated for movement perception simulation, however, the classical washout algorithm has shortage in time delay, the optimal washout algorithm completely depends on accurate vestibular model, while the adaptive washout algorithm needs long computational time. So the result of these defects is to reduce driving simulation fidelity. In order to meet the performances of high fidelity and real-time for driving simulator, this paper proposes a novel hybrid coordination motion cueing algorithm based on variant harmonic wavelet. The variant harmonic wavelet, mutating from traditional harmonic wavelet, has only three special wavelets. For its outstanding performances, box-shaped filter characteristics and zero phase shift, it would enhance driving sensation fidelity and real-time. Meanwhile, hybrid tilt and over-tilt coordination mechanism are introduced to design the new MCA for providing a special coordination strategy. Finally, the effectiveness and superiority of the new MCA are validated by simulation.

关键词： motion cueing algorithm Variant harmonic wavelet Hybrid coordination Driving simulator

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Optimal motion cueing algorithm Selection and Parameter Tuning for Sickness-Free Robocoaster Ride Simulations 1

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3rd Conference of the Mechanisms Transmissions and Applications

作者： Duc An Pham Roettgermann, Sebastian Flores, Francisco Geu Kecskemethy, Andres Univ Duisburg Essen Duisburg Germany

ISBN: (数字)9783319170671

ISBN: (纸本)9783319170671;9783319170664

Drive simulators using serial robots, such as the KUKA robot "Robocoaster", are becoming attractive for situations in which the workspace of traditional Stewart platforms is not suited to accommodate large target rotations, allowing for a wider range of possibilities. Nevertheless-even when using serial robots-the exact target motion can often not be exactly reproduced. In these cases, motion cueing algorithms (MCA) are used to produce a motion which feels as realistic as possible while remaining in the robot acceleration workspace. This paper analyzes the numerical properties of all currently existing MCA (classical, adaptive, optimal, and model predictive control) and selects the most suitable MCA using objective criteria. It also introduces a new procedure for tuning the optimal MCA such that it behaves as good as and even better than much more involved techniques based on the model predictive control (MPC). The new algorithm, termed ZyRo-K, shows best properties for reproducing the desired linear specific force while reducing the rotational false cues. While the work shown in this paper is restricted to numerical evaluation using state-of-the-art "goodness" metrics, the application and test of the algorithms for human passengers on a Robocoaster is currently being prepared and will be published in the near future.

关键词： motion cueing algorithm Robocoaster Drive simulator Simulator sickness

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Comparison of motion cueing algorithms based on numerical assessment of motion fidelity 1

Comparison of motion cueing algorithms based on numerical as...

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1st International Conference on Health Science and Technology (ICHST)

作者： Duc An Pham Hai Hoang Hong Hanoi Univ Sci & Technol Sch Mech Engn Hanoi Vietnam

ISBN: (数字)9798350315714

ISBN: (纸本)9798350315714;9798350315721

motion cueing algorithms (MACs) are essential in driving simulation systems to generate realistic motion feedback based on the driver interaction or the simulated environment. These algorithms aim to replicate the sensation of motion that corresponds to the visual and auditory cues experienced by a driver in a real-world scenario. Different algorithms, such as washout filters, adaptive and optimal algorithms, or predictive models, may be incorporated to enhance the fidelity of the motion cues. motion fidelity assessment in a driving simulator involves evaluating the accuracy and realism of the motion cues provided to the driver. It ensures that the simulated motion aligns with the visual and auditory cues, creating a convincing and immersive driving experience. A comprehensive numerical assessment of motion fidelity in driving simulators involves a combination of metrics related to amplitude, timing, spectral characteristics, and driver experience. In the article, the comparison of the response of the MCAs applied into the motion platform based on KUKA robocoaster according to numerical criteria of motion fidelity (MF). The MCAs are first tuned parameters according to various levels of MF criteria and then the MCAs response are evaluated with another numerical method related to the cross-correlation analysis.

关键词： Numerical assessment motion fidelity motion cueing algorithm comparison motion sickness

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Auto-Tuning parameters of motion cueing algorithms for high performance driving simulator based on Kuka Robocoaster

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SCIENCE PROGRESS 2022年第2期105卷 368504221104333-368504221104333页

作者： Duc-An Pham Duc-Toan Nguyen Hanoi Univ Sci Hanoi Vietnam

Driving simulators have been utilized to test and evaluate products and services for a long time. Their complexity and price range from extremely simple low-cost simulators with a fixed base to very complex high-end and pricey six-degree-of-freedom simulators with the XY table. The recent novel technique that uses an industrial robot - KUKA Robocoaster - as an interactive motion simulator platform, allowing for a highly flexible workspace as well as significantly lower prices due to mass production of the fundamental mechanics. In the constrained workspace of driving simulators, motion cueing algorithms (MCAs) are commonly employed to merge the tilt gravity and translational acceleration components for simulating the linear acceleration in the real vehicle. However, there is a few MCAs developed for the motion platform, almost MCAs were implemented for the standard six-degree-of-freedom simulators in the Cartesian coordinate. The classical MCA in the cylindrical coordinate (ClCy) MCA was first developed for the novel motion platform to take advantage of enormous rotational motion to simulate lateral acceleration while compensating for the bothersome longitudinal acceleration (due to centrifugal acceleration appearing in the rotational motion) with a proper pitch tilted angle. The process of tuning MCAs for the novel motion platform is time-consuming due to both trial and error method and the disturbing motion cues generated by rotational motion, thus it needs the involvement of experts. Although there are several auto-tuning approaches for classical, optimal, and model-predictive control MCA based on fuzzy control theory or genetic optimization method, the methods were purely applied for Cartersian coordinate without taking the bothersome longitudinal acceleration into account. Therefore, this paper firstly presents the process of integrating MCAs in the novel motion platform utilizing rotational motion for simulating lateral acceleration. For the case, besides the

关键词： motion cueing algorithm KUKA Robocoaster motion platform auto-tuning parameters MVMO genetic optimization method

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A New Prepositioning Technique of a motion Simulator Platform Using Nonlinear Model Predictive Control and Recurrent Neural Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 23268-23277页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Zhang, Li Tabarsinezhad, Farzin Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia Royal Holloway Univ London Dept Comp Sci Egham TW20 0EX Surrey England Univ Tehran Fac New Sci & Technol Tehran 14174 Iran Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform's constraints. The classical washout filter is one of the popular types of MCA, which is used in air and land vehicle motion simulators. The fixed home position of the simulator platform is always cogitated in the MCA to washout the motion simulator after generating each motion. Unfortunately, considering the fixed home position reduces the efficient consumption of the workspace in the linear directions. The linear motion of the motion simulator is due to the production of the high-pass frequency part of the motion scenarios. Prepositioning is used to tackle this assumption by varying the home position rather than the fixed position. The linear motion limitations of the motion simulator can virtually be enlarged using the prepositioning method. The efficient regeneration of the high-pass motion cues using a new propositioning technique is the main goal of this study to increase the motion realism of the simulator and remove any false motion cues due to the platform limitations. The proposed model utilised the recurrent neural network (RNN) to estimate the motion scenario along the prediction horizon. The nonlinear model predictive control (MPC) uses the estimated motion signals to extract the best optimal off-centre position of the motion simulator platform. The newly developed prepositioning technique is developed in the simulation environment of MATLAB to validate the proposed technique in terms of efficiency and applicability. The outcomes prove the capability of the proposed technique against the recently developed prepositioning technique using fuzzy logic and RNN.

关键词： End effectors Mathematical models Predictive control Prediction algorithms Angular velocity Roads Recurrent neural networks Prepositioning motion cueing algorithm nonlinear model predictive control neural network motion simulator platform

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

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SCIENCE PROGRESS 2021年第3期104卷 368504211036857-368504211036857页

作者： Pham, Duc-An Nguyen, Duc-Toan Hanoi Univ Sci & Technol Sch Mech Engn 1A Dai Co Viet Hanoi 100000 Vietnam

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.

关键词： motion cueing algorithm false cues objective assessment driving simulator

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