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An Analytic Policy Gradient-Based Deep Reinforcement Learning motion cueing algorithm for Driving Simulators

IEEE ACCESS 2025年 13卷 81507-81523页

作者： Huang, Xiaowei Shi, Xuhua Wang, Peiyao Xu, Hongzan Tang, Xiaojun Zhang, Gaoran Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zeekr Automobile Ningbo Hangzhou Bay New Zone Co L Ningbo 315800 Peoples R China

The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant enhancements. First, transform the complex simulator environment into a differentiable simulator environment that provides gradient information at each time step and use this gradient information to directly update the control policy. Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator's motion reference control signal sequences for the same duration. Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. As the prediction horizon increases, the algorithm demonstrates superior computational efficiency, which helps reduce the incidence of motion sickness during the use of the driving simulator. Third, a loss function specifically designed for the motion simulator is proposed. This function incorporates constraints derived from the MPC framework to address workspace limitations and applies them to workspace management. These constraints restrict the platform's acceleration and speed near the workspace boundaries, allowing for better utilization of the available space. The algorithm is validated using Carla's autonomous driving simulation software as the dataset generator. During the training process, the proposed algorithm in this paper achieves an order-of-magnitude improvement in convergence speed compared to conventional training methods of PPO and DDPG. Simulations with a 10-step prediction horizon indicate that the Root Mean Square Error (RMSE) produced by this algorithm is comparable to that of the MCA based on MPC (MPC-MC

关键词： Prediction algorithms Software algorithms Cost function Training Trajectory Predictive models motion sickness Mathematical models Filters Deep reinforcement learning motion cueing algorithm driving simulator analytic policy gradient (APG) differentiable simulator deep reinforcement learning

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motion cueing algorithm for high performance driving simulator using yaw table

Motion cueing algorithm for high performance driving simulat...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Fang, Z. Tsushima, M. Kitahara, E. Machida, N. Wautier, D. Kemeny, A. Nissan Motor Co Ltd CAE & Testing Div 1 NTC 560-2 Atsugi Kanagawa 2430192 Japan Renault Virtual Real & Immers Simulat Ctr TCR AVA 0 131 Ave Golf F-78288 Guyancourt France

In the last decade, MPC based motion cueing algorithm (MCA) has been successfully applied in driving simulator. Generally, in the conventional MPC based MCA, the multi-axes motion cueing problem is dealt with separately using 2DOF (linear + tilt motions) optimization technique. In the current study, a multi-axes coupled 4DOF optimization algorithm is proposed which completes the lack of efficient motion cueing algorithm for X-Y based driving simulator using yaw table. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： motion control motion cueing algorithm driving simulator real-time control

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Model Predictive Control based motion cueing algorithm for Driving Simulator

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Journal of Systems Science and Systems Engineering 2024年第5期33卷 607-626页

作者： Ayesha Hameed Ali Soltani Sharif Abadi Andrzej Ordys Institute of Automatic Control and Robotics Warsaw University of TechnologyWarsawPoland Institute of Electronic Systems Warsaw University of TechnologyWarsawPoland

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver’s behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. motion cueing algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver’s perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC).

关键词： Driving simulator motion cueing algorithm control theory model predictive control PID

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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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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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An Optimal Nonlinear Model Predictive Control-Based motion cueing algorithm Using Cascade Optimization and Human Interaction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9191-9202页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Chen, Yutao Abdar, Moloud Karkoub, Mansour Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fuzhou Univ Coll Elect Engn & Automat Qishan Campus Fuzhou 350108 Peoples R China Lamar Univ Mech Engn Dept Beaumont TX 77705 USA Swinburne Univ Technol Hawthorn Vic 3122 Australia Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

关键词： Solid modeling Tuning Optimization Actuators Mathematical models Kinematics Predictive models Cascade optimization human interaction motion cueing algorithm nonlinear model predictive control weight tuning

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A Time-Varying Weight MPC-Based motion cueing algorithm for motion Simulation Platform

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11767-11778页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP's users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP's physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP's workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP's workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

关键词： motion cueing algorithm time-varying weight model predictive control fuzzy logic multi-objective genetic algorithm hexapod platform

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A Neural Network-Based motion cueing algorithm Using the Classical Washout Filter for Comprehensive Driving Scenarios

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 5112-5121页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Zakarya, Muhammad Lim, Chee Peng Liew, Alan Wee-Chung Karkoub, Mansour Nahavandi, Saeid Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4222 Australia Lamar Univ Mech Engn Dept Beaumont TX 77705 USA Swinburne Univ Technol Def Innovat Hawthorn Vic 3122 Australia Harvard Univ Harvard Paulson Sch Engn & Appl Sci Boston MA 02134 USA

The motion cueing algorithm (MCA) enables lifelike motion in simulators resembling real driving. Regenerated motions must adhere to workspace constraints. Vehicle motion signals (linear acceleration, angular velocity) are generated in a simulated vehicle environment utilised in MCA for motion cues. These signals are categorised into levels (slow, medium, fast) based on frequency and amplitude. The commonly used MCA, the classical washout filter, is typically fine-tuned using worst-case (fast-driving) scenarios to meet the simulator's requirements across various situations. However, this approach reduces the MCA's effectiveness in handling slower driving scenarios, resulting in conservatism in platform workspace usage for slow and medium driving. Consequently, a noticeable motion sensation error arises between real vehicle drivers and motion simulator users. To rectify this issue, a novel neural network-based MCA is developed in this study. Three distinct classical washout filters are meticulously tuned to cater to slow, medium, and fast driving scenarios. These filters generate precise motion cues for simulator users at corresponding levels of driving scenarios. The neural network-based MCA is constructed using the synthesised signals from these classical washout filters. This proposed method is thoroughly validated through the utilisation of MATLAB software. In direct comparison with the standard classical washout filter, the proposed MCA significantly reduces the motion sensation error, enriches motion fidelity, and optimises the utilisation of the simulator's workspace.

关键词： motion cueing algorithm classical washout filter neural network multilayer perceptron optimisation methods parallel processing

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A Novel Decoupled Model Predictive Control-Based motion cueing algorithm for Driving Simulators

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第6期72卷 7024-7034页

作者： Asadi, Houshyar Bellmann, Tobias Qazani, Mohammadreza Chalak Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia German Aerosp Ctr Inst Syst Dynam & Control Deutsch Zentrum Luft & Raumfahrt Space Syst Dynam D-82234 Oberpfaffenhofen Germany

The motion cueing algorithm (MCA) is used to reproduce the driving/flying motion sensation of the real land or air vehicles for the users of motion simulator. Highly accurate MCAs are required for the motion simulators to create realistic sensation for the simulator users, otherwise, they might cause motion sickness and user discomfort. Model predictive control (MPC) is a popular technique in development of the high-fidelity MCAs as it is able to consider the motion sensation as well as the workspace constraints of the simulator platform. Tilt coordination in MCA is in charge of creating sustained linear acceleration feeling through tilting the platform cabin and exploiting gravitational acceleration under human motion threshold, to not allow the user to perceive this as a rotational motion. However, the existing MPC-based MCAs have not considered the rate limit to constrain the tilt motion under human threshold which causes violations of the rotational sensation threshold during generating sustained acceleration feeling. Instead, some researchers increase the penalty weights of rotational motions in order to slow down rotational motions. Using this strategy slows down all the simulator rotational motions not only the ones coming from tilt coordination, to generate sustained acceleration feeling, but also from rotational channel (due to real vehicle rotation) which causes motion sensation error and consequently motion sickness. Therefore, as the existing MPC-based MCAs cannot differentiate the rotations of tilt coordination channel (due to sustained acceleration feeling generation), from rotational channel (due to vehicle rotational motions), they are not able to produce accurate rotational motion sensations because of insufficient rotational motions. In this research, a novel decoupled MPC-based MCA is developed to systematically address the issues related to the existing MPC-based MCAs, in terms of inaccurate motion generation, by redesigning the MPC-based MCA usi

关键词： motion simulator platform motion cueing algorithm human vestibular model model predictive control

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A Safety-Oriented motion cueing algorithm for a Serial Robotic Flight Simulator Using a Predictive Neural Network Reference Governor

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15718-15731页

作者： Matheus, Aline da C. de Oliveira, Wesley R. Villani, Emilia Embraer BR-12227901 Sao Jose Dos Campos Brazil Aeronaut Inst Technol ITA Competence Ctr Mfg CCM Div Mech Engn IEM BR-12228900 Sao Jose Dos Campos Brazil

High fidelity flight simulators use motion platforms to reproduce the feeling of motion from a real flight. While most of the published works for both aircraft and vehicle simulators are related to parallel motion platforms, this work approaches the problem of designing the motion cueing algorithm of a flight simulator based on a serial manipulator. The simulator presents a large cockpit with an embedded visual system and dimensions that resemble those of an aircraft flight deck. motion cueing in this context should be able to minimize false cues while ensuring safe operation, coping not only with the dynamic and kinematic constraints of the robot but also avoiding crash events that might happen between the cockpit and the serial arm. While there have been several contributions regarding classical filtering, tuning optimization, and model-based predictive control approaches to cope with constraints of parallel platforms, they result in the inefficient utilization of the robot workspace or even the inability to handle collisions of the cockpit with the robot. This work presents a novel motion cueing algorithm for a serial robotic flight simulator, which focuses on ensuring safety regarding the physical boundaries of the cockpit while enhancing motion fidelity. The approach is based on a hybrid model-based predictor that uses a neural network to infer workspace collisions in real-time (including crash events of the cockpit with the robotic arm), releasing a non-linear deterministic control action that acts as a feedforward reference governor. Simulation and experimental results evince improved workspace usage while ensuring safe operation.

关键词： Robots Collision avoidance Prediction algorithms Manipulators Robot sensing systems Filtering algorithms Arms motion cueing algorithm serial robotic flight simulator neural networks collision-avoidance

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