Analysis of driver's steering and speed control strategies in curve negotiation.

The paper discusses some of the common speed and steering control strategies of drivers for negotiating curved roads under normal, obstacle-free driving conditions. An analysis is given of the dynamic behaviour of the driver-vehicle system for two typical strategies. A driver model is developed to take into account the application of steering and speed control before approaching a curve and during driving on curved roads, The driver's anticipatory speed-control is based on the desired lateral acceleration and estimated maximum curvature of the road ahead. The well-known two-level model is adapted for describing the steering activity of the driver. The primary control of steering is anticipatory tracking of the road-curvature previewed at a look-ahead distance. It is assumed that the driver's mental model for anticipatory steering is based on a simple, linear and non-adaptive vehicle model assuming a vehicle driving with constant forward speed. A non-linear model with variable forward speed represents the actual vehicle. When both steering input and speed control using brake/throttle are applied simultaneously the driver's anticipatory steering estimate leads to tracking errors. The driver's compensatory steering input for reducing tracking errors uses feedback of error in lateral displacement at a near-field viewpoint of the driver. The results of analysis of the driver-vehicle system demonstrate the significant influence of the driver's internal model and control strategies on the overall performance of the driver-vehicle system

Diseño y desarrollo de una aplicación para la visualización de los resultados de una captura de movimiento en un modelo 3D en Matlab.

El desarrollo completo del Proyecto se ha dividido en dos partes principales. La primera de ellas ha consistido en el diseño y la programación del software. Por otro lado, en la segunda etapa se ha completado el manual de uso del software en cuestión. Primero, se fijaron los objetivos, alcanzables y concretos, que se debían completar durante el transcurso del proyecto. El objetivo principal era desarrollar una aplicación en Matlab que funcionase conjuntamente con el software de reconstrucción de movimiento MotRec, permitiendo visualizar en un modelo 3D el movimiento capturado mediante un sistema de captura de movimiento y permitiese al usuario interactuar con ella. Así, se pretendía sustituir el software Compamm 5.1 que hasta ahora se utilizaba para cumplir dicha función en la asignatura de Biomecánica y Biorrobótica en Tecnun. Asimismo, se establecieron las características de diseño, funcionalidad e interactividad que debía tener la aplicación que se iba a programar para sustituir al software empleado hasta el momento para visualizar el movimiento en modelos 3D. El software debía resultar atractivo al usuario, fácil de manejar y con controles estándares para ejecutar la animación, como por ejemplo botones de Pause/Play, avanzar y retroceder un fotograma y una barra slider entre otros. Por otra parte, debía funcionar en sistemas operativos de Windows y Mac y ser ejecutable en la versión 2020a de Matlab. Con las bases del proyecto bien claras, se procedió al diseño de la aplicación en el entorno de programación Matlab 2020a. Debido a que se partía de cero, esta fase ha resultado ser la más costosa de todo el proyecto en lo que a complejidad y tiempo se refiere. El método de trabajo seguido ha consistido en ir añadiendo funcionalidades a la aplicación, comenzando por las más básicas, para después pulir detalles y hacer la interfaz de usuario lo más atractiva y entendible posible. Ha habido que modificar también el código del software MotRec para hacerlo compatible tanto con los sistemas operativos de Windows y Mac como con la propia aplicación. Una vez verificado el correcto funcionamiento de la app y haber asegurado que cumplía todos los objetivos y requisitos impuestos al inicio del proyecto, se dio por finalizado el proceso de diseño y programación de la aplicación. Por último, se completó el manual de uso del software MotRec ya existente. Se añadió una sección en la parte final del manual en la que se explica el funcionamiento de la aplicación y cuáles son los controles y conocimientos mínimos que debe dominar el usuario acerca de ella

A hybrid method based on a motion database and motion knowledge for the dynamic prediction of task-oriented human motion.

Digital human models are more and more frequently employed in product development processes to take human factors into account since the earliest stages of product design. To simulate the interaction of different user populations with a variety of environments, human motion prediction is a useful tool, as it aims at predicting the motion that a generic subject of a user population would reasonably perform to carry out a specific task in a given environment. The motivation of the research work presented in this thesis is the improvement of current motion prediction methods in terms of realism and representativeness. On the one hand, dynamics is included in our formulation, in order to yield physically sound predictions and in view of the fact that the forces and torques acting on and within the human body play a relevant role in discomfort perception. On the other, a hybrid approach is followed, combining the advantages of both data-based methods (which rely on actually performed motions for reference) and knowledge-based methods (which rely on the identification of the motion control laws underlying task-oriented motions). First the method is introduced, and is then applied to the prediction of clutch pedal depression motions. For this purpose, a database of clutch pedal depressions was analysed to gain insight into the subject-related and environment-related features that mostly affect the motion and into the different behavioural patterns that people exhibit carrying out the task. Both a qualitative and quantitative validation of our motion prediction method are presented. The former consists in comparing the most relevant kinematic and dynamic magnitudes in the motion against actually performed motions; the latter is based on the definition of a novel measure, which represents the realism and the representativeness of the predicted motions, and which is compared to the inherent variability of actually performed motions. The results obtained show that the proposed motion prediction method is a valid alternative to current methods, when both the physical soundness and the realism of the motion are required in the prediction

A hybrid method based on a motion database and motion knowledge for the dynamic prediction of task-oriented human motion.

Digital human models are more and more frequently employed in product development processes to take human factors into account since the earliest stages of product design. To simulate the interaction of different user populations with a variety of environments, human motion prediction is a useful tool, as it aims at predicting the motion that a generic subject of a user population would reasonably perform to carry out a specific task in a given environment. The motivation of the research work presented in this thesis is the improvement of current motion prediction methods in terms of realism and representativeness. On the one hand, dynamics is included in our formulation, in order to yield physically sound predictions and in view of the fact that the forces and torques acting on and within the human body play a relevant role in discomfort perception. On the other, a hybrid approach is followed, combining the advantages of both data-based methods (which rely on actually performed motions for reference) and knowledge-based methods (which rely on the identification of the motion control laws underlying task-oriented motions). First the method is introduced, and is then applied to the prediction of clutch pedal depression motions. For this purpose, a database of clutch pedal depressions was analysed to gain insight into the subject-related and environment-related features that mostly affect the motion and into the different behavioural patterns that people exhibit carrying out the task. Both a qualitative and quantitative validation of our motion prediction method are presented. The former consists in comparing the most relevant kinematic and dynamic magnitudes in the motion against actually performed motions; the latter is based on the definition of a novel measure, which represents the realism and the representativeness of the predicted motions, and which is compared to the inherent variability of actually performed motions. The results obtained show that the proposed motion prediction method is a valid alternative to current methods, when both the physical soundness and the realism of the motion are required in the prediction

An optimization method for overdetermined kinematic problems formulated with natural coordinates.

In this paper, we present an optimization method for solving the nonlinear constrained optimization problem arising from a motion reconstruction problem formulated with natural coordinates. A motion reconstruction problem consists in a kinematic analysis of a rigid multibody system whose motion is usually overdetermined by an excess of data. The method has been applied to the analysis of human motion which is a typical case of an overdetermined kinematic problem as a large number of markers are usually placed on a subject to capture its movement. The efficiency of the method has been tested both with computer-simulated and real experimental data using models that include open and closed kinematic loops

A New Robust Motion Reconstruction Method based on Optimization with Redundant Constraints and Natural Coordinates.

The three-dimensional analysis of human movement is of interest in many different fields of life sciences, computer animation and engineering. The elements involved in the analysis of human movement are usually measurement equipments for estimating kinematic, kinetic and myoelectric variables, mathematical models of the human musculoskeletal system, and mathematical methods for calculating the variables which cannot be directly measured. The aim of this thesis is to advance in the knowledge of four aspects of the three-dimensional analysis of the human movement: 1) the motion reconstruction of human movements using large and medium-size skeletal models with open- and closed-loops, 2) two problems inherent to optoelectronic motion capture systems: the missing marker problem and the impossibility of measuring completely the motion of some bones which move under the skin, 3) the estimation of subject-specific parameters using only a motion capture system, and 4) the development of several human skeletal models suitable for analysing different vehicle-related motions. The motion reconstruction problem using human skeletal models defined with natural coordinates is formulated as a nonlinear constrained optimisation problem with equality constraint equations. The main contribution of this thesis is a new optimisation method for solving the motion reconstruction problem. The new optimisation method can reconstruct the motion of large-size human skeletal models with open- and closed-loops defined with natural coordinates and it can also handle redundant constraints. Four new strategies have been proposed for solving the two problems inherent to optoelectronic motion capture systems addressed in this thesis. The four strategies have been evaluated using experimental motion data with satisfactory results. These strategies enable a more robust reconstruction of the human movement. The subject-specific parameters are estimated using methods based on the measurement of anatomical landmarks. Furthermore, a new measurement protocol for measuring the anatomical landmarks and a new methodology for estimating all subject-specific parameters from the measured anatomical landmarks are proposed. Three human skeletal models have been developed for studying driving manoeuvres: one upper body model with a detailed model of the shoulder complex and another upper body model with a simplified model of the shoulder complex for studying steering manoeuvres and one right lower limb model for studying braking manoeuvres. Additionally, a human skeletal model of the whole body, based on the RAMSIS model, has been used to study generic arm reaching motions and three types of vehicle-related motions

A New Robust Motion Reconstruction Method based on Optimization with Redundant Constraints and Natural Coordinates.

The three-dimensional analysis of human movement is of interest in many different fields of life sciences, computer animation and engineering. The elements involved in the analysis of human movement are usually measurement equipments for estimating kinematic, kinetic and myoelectric variables, mathematical models of the human musculoskeletal system, and mathematical methods for calculating the variables which cannot be directly measured. The aim of this thesis is to advance in the knowledge of four aspects of the three-dimensional analysis of the human movement: 1) the motion reconstruction of human movements using large and medium-size skeletal models with open- and closed-loops, 2) two problems inherent to optoelectronic motion capture systems: the missing marker problem and the impossibility of measuring completely the motion of some bones which move under the skin, 3) the estimation of subject-specific parameters using only a motion capture system, and 4) the development of several human skeletal models suitable for analysing different vehicle-related motions. The motion reconstruction problem using human skeletal models defined with natural coordinates is formulated as a nonlinear constrained optimisation problem with equality constraint equations. The main contribution of this thesis is a new optimisation method for solving the motion reconstruction problem. The new optimisation method can reconstruct the motion of large-size human skeletal models with open- and closed-loops defined with natural coordinates and it can also handle redundant constraints. Four new strategies have been proposed for solving the two problems inherent to optoelectronic motion capture systems addressed in this thesis. The four strategies have been evaluated using experimental motion data with satisfactory results. These strategies enable a more robust reconstruction of the human movement. The subject-specific parameters are estimated using methods based on the measurement of anatomical landmarks. Furthermore, a new measurement protocol for measuring the anatomical landmarks and a new methodology for estimating all subject-specific parameters from the measured anatomical landmarks are proposed. Three human skeletal models have been developed for studying driving manoeuvres: one upper body model with a detailed model of the shoulder complex and another upper body model with a simplified model of the shoulder complex for studying steering manoeuvres and one right lower limb model for studying braking manoeuvres. Additionally, a human skeletal model of the whole body, based on the RAMSIS model, has been used to study generic arm reaching motions and three types of vehicle-related motions

Pilot study describing the design process of an oil sump for a competition vehicle by combining additive manufacturing and carbon fibre layers

Formula Student is an international competition governed by the Society of Automotive Engineers (SAE) which challenges university students to design and build a racing car that will subsequently be compared against other cars from universities around the world on homologated racing circuits by non-professional drivers. This study focuses on the design, analysis and manufacturing process of a new oil sump for a Formula Student car - which involves combining a main ABS-plastic core created by an additive manufacturing (AM) printing process and a manual lay-up process with carbon fibre - in order to reduce the sloshing effect due to the movement of the oil during racing. The new oil sump and the original sump were modelled with computer-aided design (CAD) software and five computational fluid dynamics (CFD) simulations were performed to compare the sloshing effect in both designs in three driving scenarios: acceleration, braking and changing direction. The simulations showed that acceleration is not a critical situation since the new internal design of the sump was capable of delaying the immersion time of the oil pick-up pipe from 0.75 seconds to 2 seconds during braking and from 0.4 seconds to 0.8 seconds during lateral acceleration. The new design was physically manufactured and subsequently integrated into an internal combustion engine for testing for 45 minutes. During this test, the engine was started and put at 9600 RPM, so the oil worked under realistic temperature conditions (80 degrees C). It did not present any oil leak. After testing, it was disassembled and visually inspected. No failure in the inner surfaces of the oil sump was observed due to temperature. According to these results, the present research argues that the combination of AM technology (i.e., fused deposition modelling) and layers of carbon fibre is a real alternative to conventional manufacturing processes in order to create geometrically complex oil sumps that minimise the sloshing effect in competition automobiles

A comparison between optimization-based human motion prediction methods: data-based, knowledge-based and hybrid approaches

In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method is hybrid as the prediction relies both on actually performed motions for reference (following a data-based approach) and on the definition of appropriate performance measures (following a knowledge-based approach). The prediction is carried out through the definition of a constrained non-linear optimization problem, in which the objective function is composed of a weighted combination of data-based and knowledge-based contributions. The weights of each contribution are varied in order to generate a battery of hybrid predictions, which range from purely data-based to purely knowledge-based. The results of the predictions are analyzed and compared against actually performed motions both qualitatively and quantitatively, using a measure of realism defined as the distance of the predicted motions from the mean of the actually performed motions. The method is applied to clutch pedal depression motions and the comparison between the different approaches favors the hybrid solution, which seems to combine the strengths of both data- and knowledge-based approaches, enhancing the realism of the predicted motion