Kyle Nelson received the B.Eng. (hons.) degree in Mechatronics and Robotics in 2009, and the Ph.D. degree in computer vision/image processing in 2013 from Deakin University, Australia, where he attended as a Dean’s Scholar and was a recipient of both the Alfred Deakin Medal and Vice-Chancellor’s Prize. His PhD research focused on the image enhancement technique of super-resolution and combining this concept with ideas from the field of multi-view geometry to produce high-resolution images of 3-dimensional scenes. Currently a research fellow with the Centre for Intelligent Systems Research at Deakin University, Kyle is actively involved in robot-based motion simulation, computer vision and image processing research. Kyle has worked on a number of industry-linked robotics projects including the design and development of a vision-based measurement system for Boeing Research & Technology Australia.
In the methodology of objective measurement of ride comfort, application of a Human Biomechanical... more In the methodology of objective measurement of ride comfort, application of a Human Biomechanical Model (HBM) is valuable for Whole Body Vibration (WBV) analysis. In this study, using a computational Multibody System (MBS) approach, development of a 3D passive HBM for a seated human is considered. For this purpose, the existing MBS-based HBMs of seated human are briefly reviewed first. The Equations of Motion (EoM) for the proposed model are then obtained and the simulation results are shown and compared with idealised ranges of experimental results suggested in the literature. The human-seat interaction is established using a nonlinear vibration model of foam with respect to the sectional behaviour of the seat foam. The developed system is then used for ride comfort estimation offered by a ride dynamic model. The effects of human weight, road class, and vehicle speed on the vibration of the human body segments in different directions are studied. It is shown that the there is a high correlation (more than 99.2%) between the vibration indices of the proposed HBM-foam model and the corresponding ISO 2631 WBV indices. In addition, relevant ISO 2631 indices that show a high correlation with the directional vibration of the head are identified.
Ride comfort is essential for the road vehicles due to the customer demand in the current automot... more Ride comfort is essential for the road vehicles due to the customer demand in the current automotive industry. Assessment of ride comfort is required to evaluate and improve human comfort in the car. Objective assessment of ride comfort considerably helps to reduce the cost, time, and risk compared to the subjective assessment which requires actual road tests. In this paper, design and implementation of equivalent digital filters for ISO 2631 and 5349 weighting factors are performed. Furthermore, a 3D passive MBS-based Human Biomechanical Model (HBM) of a seated human is proposed for the objective assessment of ride comfort. These two developments are then used for estimation of ride comfort for a ride dynamic model. Comparison between the result obtained from the ISO standard and the proposed HBM shows the usability of the proposed HBM for vehicular comfort studies.
– A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions ex... more – A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle as it is constrained by its physical limits. The aim of this research is to provide an optimal Motion Cueing Algorithm (MCA) that can generate the most realistic motions and high fidelity vehicle accelerations and angular velocities, within the simulator's physical limitations. The optimal washout filter based on Linear Quadratic Regulator (LQR), which takes the recent vestibular system mathematical model and simulator motion in to account, has been proposed to constrain the human perception error between the simulator and real driving, within the limits of the platform motion. This paper presents a new strategy based on optimal control theory and the Genetic Algorithm (GA) to reproduce a signal that can closely follow the reference signal and avoid false motion cues. An optimization method for adjusting the obtained optimal washout filter transfer functions, based on genetic algorithms is used. Three additional compensatory linear blocks are integrated into the LQR-based optimal washout filter, to be tuned based on GA in order to modify the performance of the filters and minimize the fitness value if increasing the order is required. This is achieved by taking a series of factors into account, including: the vestibular sensation error between real and simulated cases; the human threshold limiter in tilt coordination; the human sensation error fluctuation; and cross correlation coefficient; where the effects of these aspects have been previously ignored. The proposed optimized motion cueing algorithm based on compensators using GA is implemented in the MATLAB/Simulink software packages. The results show the superiority of the proposed method due to its better increased performance, enhanced motion fidelity, improved human sensation, and reduced workspace usage compared to previous optimal washout filters.
The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator m... more The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers' experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in under‐ standing the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limi‐ tations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator m... more The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers' experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in under‐ standing the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limi‐ tations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions expe... more A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle since it is constrained by its physical limits. The aim of this research is to provide a Cueing Motion Algorithm that can accurately produce vehicle accelerations and angular velocities in the simulator platform at high fidelity to give the most realistic motion, within the simulator's physical limitations. The higher order optimal washout filter based on Linear Quadratic (LQR) which takes the Vestibular system mathematical model, simulator constraints and capabilities in to account have been proposed to reduce the human perception error between simulator and real driving. Angular velocity and linear acceleration have been used in this design as the inputs for the washout filter. The obtained washout filters are the optimized transfer functions that link the simulator motion input to the vehicle motion input aiming to constrain the human sensation error and the platform motion. Thus, it overcomes the lack of human perception and conservative motion of previous classical washout filters and improves human perception, exploits available workspace more efficiently and respects physical constraints.
– A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions ex... more – A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle as it is constrained by its physical limits. The aim of this research is to provide an optimal Motion Cueing Algorithm (MCA) that can generate the most realistic motions and high fidelity vehicle accelerations and angular velocities, within the simulator's physical limitations. The optimal washout filter based on Linear Quadratic Regulator (LQR), which takes the recent vestibular system mathematical model and simulator motion in to account, has been proposed to constrain the human perception error between the simulator and real driving, within the limits of the platform motion. This paper presents a new strategy based on optimal control theory and the Genetic Algorithm (GA) to reproduce a signal that can closely follow the reference signal and avoid false motion cues. An optimization method for adjusting the obtained optimal washout filter transfer functions, based on genetic algorithms is used. Three additional compensatory linear blocks are integrated into the LQR-based optimal washout filter, to be tuned based on GA in order to modify the performance of the filters and minimize the fitness value if increasing the order is required. This is achieved by taking a series of factors into account, including: the vestibular sensation error between real and simulated cases; the human threshold limiter in tilt coordination; the human sensation error fluctuation; and cross correlation coefficient; where the effects of these aspects have been previously ignored. The proposed optimized motion cueing algorithm based on compensators using GA is implemented in the MATLAB/Simulink software packages. The results show the superiority of the proposed method due to its better increased performance, enhanced motion fidelity, improved human sensation, and reduced workspace usage compared to previous optimal washout filters.
—A motion simulator is an effective tool for training a driver in a safe environment by mimicking... more —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.
The goal of a driving simulator is to produce an environment for a driver similar to the real dri... more The goal of a driving simulator is to produce an environment for a driver similar to the real driving scenario. Motion cueing algorithms are used to produce a realistic motion while respecting the workspace limitations and motion simulator boundaries. Model Predictive Control has become popular recently for motion cueing. However, in this control method, the optimization is based on a predefined constant future input trajectory while it is not a practical assumption. In this research, a method is proposed to predict the future reference based on the finite history of input. This method does not require the position trajectory to follow a specific road. Simulation results show the effectiveness of the proposed model predictive control method in terms of realistic motion sensation for a driver.
—Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour... more —Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.
In the methodology of objective measurement of ride comfort, application of a Human Biomechanical... more In the methodology of objective measurement of ride comfort, application of a Human Biomechanical Model (HBM) is valuable for Whole Body Vibration (WBV) analysis. In this study, using a computational Multibody System (MBS) approach, development of a 3D passive HBM for a seated human is considered. For this purpose, the existing MBS-based HBMs of seated human are briefly reviewed first. The Equations of Motion (EoM) for the proposed model are then obtained and the simulation results are shown and compared with idealised ranges of experimental results suggested in the literature. The human-seat interaction is established using a nonlinear vibration model of foam with respect to the sectional behaviour of the seat foam. The developed system is then used for ride comfort estimation offered by a ride dynamic model. The effects of human weight, road class, and vehicle speed on the vibration of the human body segments in different directions are studied. It is shown that the there is a high correlation (more than 99.2%) between the vibration indices of the proposed HBM-foam model and the corresponding ISO 2631 WBV indices. In addition, relevant ISO 2631 indices that show a high correlation with the directional vibration of the head are identified.
Ride comfort is essential for the road vehicles due to the customer demand in the current automot... more Ride comfort is essential for the road vehicles due to the customer demand in the current automotive industry. Assessment of ride comfort is required to evaluate and improve human comfort in the car. Objective assessment of ride comfort considerably helps to reduce the cost, time, and risk compared to the subjective assessment which requires actual road tests. In this paper, design and implementation of equivalent digital filters for ISO 2631 and 5349 weighting factors are performed. Furthermore, a 3D passive MBS-based Human Biomechanical Model (HBM) of a seated human is proposed for the objective assessment of ride comfort. These two developments are then used for estimation of ride comfort for a ride dynamic model. Comparison between the result obtained from the ISO standard and the proposed HBM shows the usability of the proposed HBM for vehicular comfort studies.
– A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions ex... more – A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle as it is constrained by its physical limits. The aim of this research is to provide an optimal Motion Cueing Algorithm (MCA) that can generate the most realistic motions and high fidelity vehicle accelerations and angular velocities, within the simulator's physical limitations. The optimal washout filter based on Linear Quadratic Regulator (LQR), which takes the recent vestibular system mathematical model and simulator motion in to account, has been proposed to constrain the human perception error between the simulator and real driving, within the limits of the platform motion. This paper presents a new strategy based on optimal control theory and the Genetic Algorithm (GA) to reproduce a signal that can closely follow the reference signal and avoid false motion cues. An optimization method for adjusting the obtained optimal washout filter transfer functions, based on genetic algorithms is used. Three additional compensatory linear blocks are integrated into the LQR-based optimal washout filter, to be tuned based on GA in order to modify the performance of the filters and minimize the fitness value if increasing the order is required. This is achieved by taking a series of factors into account, including: the vestibular sensation error between real and simulated cases; the human threshold limiter in tilt coordination; the human sensation error fluctuation; and cross correlation coefficient; where the effects of these aspects have been previously ignored. The proposed optimized motion cueing algorithm based on compensators using GA is implemented in the MATLAB/Simulink software packages. The results show the superiority of the proposed method due to its better increased performance, enhanced motion fidelity, improved human sensation, and reduced workspace usage compared to previous optimal washout filters.
The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator m... more The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers' experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in under‐ standing the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limi‐ tations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator m... more The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers' experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in under‐ standing the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limi‐ tations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions expe... more A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle since it is constrained by its physical limits. The aim of this research is to provide a Cueing Motion Algorithm that can accurately produce vehicle accelerations and angular velocities in the simulator platform at high fidelity to give the most realistic motion, within the simulator's physical limitations. The higher order optimal washout filter based on Linear Quadratic (LQR) which takes the Vestibular system mathematical model, simulator constraints and capabilities in to account have been proposed to reduce the human perception error between simulator and real driving. Angular velocity and linear acceleration have been used in this design as the inputs for the washout filter. The obtained washout filters are the optimized transfer functions that link the simulator motion input to the vehicle motion input aiming to constrain the human sensation error and the platform motion. Thus, it overcomes the lack of human perception and conservative motion of previous classical washout filters and improves human perception, exploits available workspace more efficiently and respects physical constraints.
– A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions ex... more – A simulator motion platform cannot exactly reproduce the longitudinal and rotational motions experienced in a real vehicle as it is constrained by its physical limits. The aim of this research is to provide an optimal Motion Cueing Algorithm (MCA) that can generate the most realistic motions and high fidelity vehicle accelerations and angular velocities, within the simulator's physical limitations. The optimal washout filter based on Linear Quadratic Regulator (LQR), which takes the recent vestibular system mathematical model and simulator motion in to account, has been proposed to constrain the human perception error between the simulator and real driving, within the limits of the platform motion. This paper presents a new strategy based on optimal control theory and the Genetic Algorithm (GA) to reproduce a signal that can closely follow the reference signal and avoid false motion cues. An optimization method for adjusting the obtained optimal washout filter transfer functions, based on genetic algorithms is used. Three additional compensatory linear blocks are integrated into the LQR-based optimal washout filter, to be tuned based on GA in order to modify the performance of the filters and minimize the fitness value if increasing the order is required. This is achieved by taking a series of factors into account, including: the vestibular sensation error between real and simulated cases; the human threshold limiter in tilt coordination; the human sensation error fluctuation; and cross correlation coefficient; where the effects of these aspects have been previously ignored. The proposed optimized motion cueing algorithm based on compensators using GA is implemented in the MATLAB/Simulink software packages. The results show the superiority of the proposed method due to its better increased performance, enhanced motion fidelity, improved human sensation, and reduced workspace usage compared to previous optimal washout filters.
—A motion simulator is an effective tool for training a driver in a safe environment by mimicking... more —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.
The goal of a driving simulator is to produce an environment for a driver similar to the real dri... more The goal of a driving simulator is to produce an environment for a driver similar to the real driving scenario. Motion cueing algorithms are used to produce a realistic motion while respecting the workspace limitations and motion simulator boundaries. Model Predictive Control has become popular recently for motion cueing. However, in this control method, the optimization is based on a predefined constant future input trajectory while it is not a practical assumption. In this research, a method is proposed to predict the future reference based on the finite history of input. This method does not require the position trajectory to follow a specific road. Simulation results show the effectiveness of the proposed model predictive control method in terms of realistic motion sensation for a driver.
—Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour... more —Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.
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Papers by Kyle Nelson