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To cite this version :
Baris AYKENT, Damien PAILLOT, Frédéric MERIENNE, Zhou FANG, Andras KEMENY - Study of the Influence of Different Washout Algorithms on Simulator Sickness for a Driving Simulation Task - 2011
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1 Copyright © 2011 by ASME
STUDY OF THE INFLUENCE OF DIFFERENT WASHOUT ALGORITHMS ON
SIMULATOR SICKNESS FOR A DRIVING SIMULATION TASK
Barış Aykent1, Damien Paillot, Frédéric Mérienne Arts et Métiers ParisTech, CNRS, Le2i
Institut Image, 2 rue T. Dumorey, 71100 Chalon-sur-Saône, France 1Contact Author
Zhou Fang, Andras Kemeny Technical Centre for Simulation, RENAULT
GUYANCOURT, France
KEYWORDS
Driving simulator, virtual immersion, motion sickness ABSTRACT
This paper deals with the effects of different washout algorithms used for Stewart platforms on subjective and objective ratings. Washout algorithms are used to represent vehicle dynamics in a restricted spatial place. An adaptive washout algorithm was realized to control the hexapod platform, depending on the specific force error in longitudinal, lateral and vertical directions, in order to compare user‟s experience with those in the case of classical algorithm. In this study, the simulator sickness has been evaluated for three algorithms in dynamic driving simulator situation in objective and subjective way.
INTRODUCTION
Driving simulation is a high demanded domain in terms of virtual immersion. Compared to virtual mock-up visualization in a CAVE system, driving simulation induces the motion of the user‟s body through the car driving task. In driving simulation, the motion is mainly induced by acting on visual cue (vection process) and vestibular cue (with a motion platform). To improve the virtual immersion in driving simulation task, both cues have to be well correlated. The motion platform is controlled with an algorithm which translates the car simulated motion in a possible platform motion.
The washout algorithms are used to depict the driving dynamics of a real vehicle in a constraint space for a driving experience as realistic as possible (see Figure 2). They were firstly introduced in the mid 60s in the aerospace industry
(Stewart, 1965). Afterwards, these techniques have been spread into automotive industry for the development process of the vehicles.
Different algorithms have been developed to control a 6 DOF (Degrees Of Freedom) platform (as a hexapod platform). The following table gives the list of the main common algorithms used in the literature.
Motion control algorithm
References
Classical (Stewart, 1965)
Adaptive (Parrish et al 1975, Ariel and
Sivan 1984, Nehaoua et al, 2006 PA Ioannou and J. Sun, Robust Adaptive Control. Prentice-Hall Inc., 1995)
Optimal (Sivan, et al., 1982)
Predictive model algorithm (Dagdelen et al, 2009)
Objectives of the washout algorithms are given below [3]: - Main objective for implementation of the washout
algorithm is to maximize the contribution of the motion system to the capabilities of the simulator. - Main limiting factors are the maximum speed and
stroke of the actuators.
- Reproduction of motion cues one to one is not possible. Filtering and reducing cues are needed. Proceedings of the ASME 2011 World Conference on Innovative Virtual Reality
WINVR2011 June 27-29, 2011, Milan, Italy
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5 Copyright © 2011 by ASME Motion control computer (PC MOOG in Figure 6) is the
element to control the position and orientation of the Stewart platform. Kinematics and dynamics of the platform are manipulated by PC MOOG. This component accomplishes motion algorithms and washout as well as position velocity acceleration (PVA) transformations.
Driving simulator is constituted of four elements and the driver in the loop:
1- Control system: Steering wheel, gas pedal, brake pedal (haptic cues)
2- Sound system (acoustic cues)
3- Motion system: Hexapod platform (inertial cues) 4- View system: 3 channel animator view in the driver‟s
cabin.(visual cues)
5- Driver (proprio-perceptive,vestibular system)
TESTS ON DRIVING SIMULATOR
In this study, the simulator sickness has been evaluated objectively and subjectively on driver in the dynamic driving simulator SAAM in function of three different motion platform algorithms. The SAAM simulator has been designed and developed by Arts et Métiers ParisTech and RENAULT. For the objective evaluation, we used the data acquisition from the SCANeRstudio® to collect the data related to commands (steering wheel angle, accelerator, brake pedal force, etc.), dynamics (vehicle dynamics data), engine, motion platform (position in X, Y and Z axis and the angles around X, Y and Z axis). The Table 1 gives the capabilities of the driving simulator SAAM we used.
Table 1: Software limits for each degree of freedom (DOF) of the
dynamic simulator SAAM [MOOG FCS User Manual]
DOF Displacement Velocity Acceleration Pitch ±22 deg ±30 deg/s ±500 deg/s2
Roll ±21 deg ±30 deg/s ±500 deg/s2
Yaw ±22 deg ±40 deg/s ±400 deg/s2
Heave ±0.18 m ±0.30 m/s ±0.5 G
Surge ±0.25 m ±0.5 m/s ±0.6 G
Sway ±0.25 m ±0.5 m/s ±0.6 G
The subjects were asked to drive the three different washout algorithms on the dynamic driving simulator SAAM (Figure 1) at the same scenario of Country Road of SCANeRstudio® OKTAL (Figure 8 and 9) with a constant driving velocity of v= 70 km/h. 7 subjects participated to the experiment. In addition, they had a familiarization drive before each session to avoid misevaluation and to help them assess as objective as possible.
During the testing phase, they drove three times for each motion algorithm. After each attempt they were asked to fill in the regarding questionnaire for the subjective rating of the “simulator sickness” and to evaluate the “simulator fidelity” respectively. Besides, at each essay, the data recorded with the default value of SCANeRstudio® software for sampling period of 0.1 s. [OKTAL SCANeRstudio User Manual]
Figure 8. Country scenario OKTAL SCANeRstudio [OKTAL SCANeRstudio User Manual]
Figure 9. Conducted route [OKTAL SCANeRstudio User Manual]
To rate the simulator sickness, the “simulator sickness questionnaire (SSQ)” (Kennedy 1993), “motion sickness questionnaire (MSQ)” (Kennedy 1992), “biofeedback methods (BFM)” are commonly applied with some other approaches like “motion sickness dose value (MSDV)” (ISO 2631-1, 1997) (Griffin 1990).
OBJECTIVE EVALUATION
Objective evaluation refers to an assessment method for the driving simulator applications of which the measured data such as the effective roll, pitch, yaw angles of the hexapod
6 Copyright © 2011 by ASME motion platform; positions of the motion platform ; the
dynamics data of the vehicle model, etc. are considered. Table 2 indicates the experimental conditions of the moving base simulator experiments.
Table 2: Conditions of the moving base simulator experiments
Figure 10. Effective roll angle of the motion platform at different motion drive algorithms
Figure 11. Effective pitch angle of the motion platform at different motion drive algorithms
Figure 12. Effective yaw angle of the motion platform at different motion drive algorithms
The results from the motion platform (Figure 10, 11 and 12) point out that by increasing the travel scale for longitudinal, lateral and vertical axis of the hexapod; the oscillations of the associated rotation are being more smoothed. It means that the greater the travel scale factor on the adaptive washout algorithms commanded platform dynamics is, the less the undulations will occur.
EFFECT OF THE DRIVER AS A CLOSED LOOP CONTROL
As we discussed above, the subject participating in the experimentation is also a secondary closed loop controller in addition to washout algorithm. In fact, the subject can also be considered as a primary control (we have investigated the primary control as open loop „classical washout‟ and as closed loop „adaptive washout‟ in this paper).
The lateral position difference of those which have been obtained from the driving simulator („driver + washout algorithm‟ as controller, see blue curve below) and the simulation („washout algorithm‟ as controller, see red curve below) results figure out that, even the same scenario with the same washout has been realized, the results have differed to each other which emphasizes the factor of the „driver behavior‟ that is probably the most difficult part to model, estimate and/or simulate to capture a better fidelity for driving simulator research and development phases.
0 20 40 60 80 100 120 140 160 180 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Time (s) La te ra l p os iti on (m )
Effect of the Driver as a "Closed Loop Control (Man in the Loop - MIL)" at Classical Washout Algorithm
Lateral position during the Simulator Tests Lateral position during the Simulation
Figure 13. Driver’s influence as a second closed loop controller Classical Washout Algorithm Adaptive Washout Algorithm (adaptive1, scale factor: 0.2) Adaptive Washout Algorithm (adaptive2, scale factor: 1.0) Velocity Travel Scale factor Velocity Travel Scale factor Velocity 70 km/h 0.2 70 km/h 1.0 70 km/h
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![Table 1: Software limits for each degree of freedom (DOF) of the dynamic simulator SAAM [MOOG FCS User Manual]](https://thumb-eu.123doks.com/thumbv2/123doknet/7358352.213634/6.918.503.840.264.502/table-software-limits-degree-freedom-dynamic-simulator-manual.webp)
