Lateral dynamics vehicle model: an analysis on different approaches with increasing level of model complexity

This thesis presents an analysis on different approaches with increasing level of model complexity with respect to lateral vehicle dynamics. In recent years, there has been an increasing interest in developing accurate and reliable models for lateral vehicle dynamics, in order to improve vehicle stability and control. The analysis begins with a review of the existing literature on lateral vehicle dynamics, and the differ- ent mathematical and physical modeling approaches that have been proposed. These include linear and nonlinear models, as well as more complex models that take into account factors such as tire dynamics and suspension dynamics, among others, that are not detailed in this study. Next, the thesis presents a comparative analysis of the different approaches based on machine learn- ing, using both simulation and experimental data from scientific literature. The analysis focuses on the accuracy and predictive power of the different deep learning models, as well as their computa- tional efficiency and ease of implementation. The results of the analysis indicate that while more complex models can provide more accurate pre- dictions of lateral vehicle dynamics, they also require significantly more computational resources and can be more difficult to implement. Therefore, the results of several publications indicate that deep learning models can provide highly accurate predictions for lateral vehicle dynamics, and can be trained with relatively small datasets. Overall, this study provides a comprehensive analysis on the use of machine learning techniques to develop competitive and high performance controllers for lateral vehicle dynamics and state estima- tion, highlighting the potential contribution in autonomous driving and vehicle stability and control that these methods will allow to achieve in the next decade

Microanalysis of nonverbal communication: Development of a nonverbal research method using high-performance 3D character animation

This work provides a novel research tool for the field of nonverbal communication, with the goal being to transform 3D motion data into metric measurements that allow for the application of standard statistical methods such as analysis of variance, factor analysis, or multiple regression analysis. 3D motion data are automatically captured by motion capture systems or manually coded by humans using 3D character animation software. They precisely describe human movements, but without any furter data processing, they cannot meaningfully be interpreted and statistically analyzed. To make this possible, three nonverbal coding systems describing static body postures, dynamic body movements, and proper body part motions such as head nods have been developed. A geometrical model describing postures and movements as flexion angles of body parts on three clearly understandable and nonverbal relevant dimensions—the sagittal, the rotational, and the lateral—has been developed and provides the basis for math formulas which allow the transformation of motion capture data or 3D animation data into metric measures. Furthermore, math formulas were developed to compute around 30 nonverbal cues described in the literature on kinesics that can be understood as geometrical features of body parts such as openness, symmetry, and expansiveness of body postures, head position and head nods, gaze direction and body orientation, pointing behavior and relational gestures, interactional synchrony, proxemics, and touch, including dynamic features of movements such as rate, velocity, and acceleration. To obtain accurate measurements, the software APEx (Automatic Parameter Extraction) has been developed with a number of convenient features extracting more than 150 nonverbal parameters consisting 380 metric variables out of available motion data

Study of Control Strategies for Robot Ball Catching

La tesi riguarda lo studio di un possibile scenario per la cattura di una palla con un braccio robotico usando tecnologie disponibili e considerando due problemi principali: studiare differenti strategie di controllo per il braccio robotico al fine di catturare la palla (controllo predittivo e prospettivo); implementare un simulatore in ROS che simula il robot reale, includendo un sistema di visione per riconoscere e tracciare la palla usando il sensore Microsoft Kinect, con diverse simulazion

Efficient Learning and Inference for High-dimensional Lagrangian Systems

Learning the nature of a physical system is a problem that presents many challenges and opportunities owing to the unique structure associated with such systems. Many physical systems of practical interest in engineering are high-dimensional, which prohibits the application of standard learning methods to such problems. This first part of this work proposes therefore to solve learning problems associated with physical systems by identifying their low-dimensional Lagrangian structure. Algorithms are given to learn this structure in the case that it is obscured by a change of coordinates. The associated inference problem corresponds to solving a high-dimensional minimum-cost path problem, which can be solved by exploiting the symmetry of the problem. These techniques are demonstrated via an application to learning from high-dimensional human motion capture data. The second part of this work is concerned with the application of these methods to high-dimensional motion planning. Algorithms are given to learn and exploit the struc- ture of holonomic motion planning problems effectively via spectral analysis and iterative dynamic programming, admitting solutions to problems of unprecedented dimension com- pared to known methods for optimal motion planning. The quality of solutions found is also demonstrated to be much superior in practice to those obtained via sampling-based planning and smoothing, in both simulated problems and experiments with a robot arm. This work therefore provides strong validation of the idea that learning low-dimensional structure is the key to future advances in this field

Modeling and grasping of thin deformable objects

Deformable modeling of thin shell-like and other objects have potential application in robot grasping, medical robotics, home robots, and so on. The ability to manipulate electrical and optical cables, rubber toys, plastic bottles, ropes, biological tissues, and organs is an important feature of robot intelligence. However, grasping of deformable objects has remained an underdeveloped research area. When a robot hand applies force to grasp a soft object, deformation will result in the enlarging of the finger contact regions and the rotation of the contact normals, which in turn will result in a changing wrench space. The varying geometry can be determined by either solving a high order differential equation or minimizing potential energy. Efficient and accurate modeling of deformations is crucial for grasp analysis. It helps us predict whether a grasp will be successful from its finger placement and exerted force, and subsequently helps us design a grasping strategy. The first part of this thesis extends the linear and nonlinear shell theories to describe extensional, shearing, and bending strains in terms of geometric invariants including the principal curvatures and vectors, and the related directional and covariant derivatives. To our knowledge, this is the first non-parametric formulation of thin shell strains. A computational procedure for the strain energy is then offered for general parametric shells. In practice, a shell deformation is conveniently represented by a subdivision surface. We compare the results via potential energy minimization over a couple of benchmark problems with their analytical solutions and the results generated by two commercial softwares ABAQUS and ANSYS. Our method achieves a convergence rate an order of magnitude higher. Experimental validation involves regular and freeform shell-like objects (of various materials) grasped by a robot hand, with the results compared against scanned 3-D data (accuracy 0.127mm). Grasped objects often undergo sizable shape changes, for which a much higher modeling accuracy can be achieved using the nonlinear elasticity theory than its linear counterpart. The second part numerically studies two-finger grasping of deformable curve-like objects under frictional contacts. The action is like squeezing. Deformation is modeled by a degenerate version of the thin shell theory. Several differences from rigid body grasping are shown. First, under a squeeze, the friction cone at each finger contact rotates in a direction that depends on the deformable object\u27s global geometry, which implies that modeling is necessary for grasp prediction. Second, the magnitude of the grasping force has to be above certain threshold to achieve equilibrium. Third, the set of feasible finger placements may increase significantly compared to that for a rigid object of the same shape. Finally, the ability to resist disturbance is bounded in the sense that increasing the magnitude of an external force may result in the breaking of the grasp

Prohibited Volume Avoidance for Aircraft

This thesis describes the development of a pilot override control system that prevents aircraft entering critical regions of space, known as prohibited volumes. The aim is to prevent another 9/11 style terrorist attack, as well as act as a general safety system for transport aircraft. The thesis presents the design and implementation of three core modules in the system; the trajectory generation algorithm, the trigger mechanism for the pilot override and the trajectory following element. The trajectory generation algorithm uses a direct multiple shooting strategy to provide trajectories through online computation that avoid pre-defi ned prohibited volume exclusion regions, whilst accounting for the manoeuvring capabilities of the aircraft. The trigger mechanism incorporates the logic that decides the time at which it is suitable for the override to be activated, an important consideration for ensuring that the system is not overly restrictive for a pilot. A number of methods are introduced, and for safety purposes a composite trigger that incorporates di fferent strategies is recommended. Trajectory following is best achieved via a nonlinear guidance law. The guidance logic sends commands in pitch, roll and yaw to the control surfaces of the aircraft, in order to closely follow the generated avoidance trajectory. Testing and validation is performed using a full motion simulator, with volunteers flying a representative aircraft model and attempting to penetrate prohibited volumes. The proof-of-concept system is shown to work well, provided that extreme aircraft manoeuvres are prevented near the exclusion regions. These hard manoeuvring envelope constraints allow the trajectory following controllers to follow avoidance trajectories accurately from an initial state within the bounding set. In order to move the project closer to a commercial product, operator and regulator input is necessary, particularly due to the radical nature of the pilot override system

Multibody Systems with Flexible Elements

Multibody systems with flexible elements represent mechanical systems composed of many elastic (and rigid) interconnected bodies meeting a functional, technical, or biological assembly. The displacement of each or some of the elements of the system is generally large and cannot be neglected in mechanical modeling. The study of these multibody systems covers many industrial fields, but also has applications in medicine, sports, and art. The systematic treatment of the dynamic behavior of interconnected bodies has led to an important number of formalisms for multibody systems within mechanics. At present, this formalism is used in large engineering fields, especially robotics and vehicle dynamics. The formalism of multibody systems offers a means of algorithmic analysis, assisted by computers, and a means of simulating and optimizing an arbitrary movement of a possibly high number of elastic bodies in the connection. The domain where researchers apply these methods are robotics, simulations of the dynamics of vehicles, biomechanics, aerospace engineering (helicopters and the behavior of cars in a gravitational field), internal combustion engines, gearboxes, transmissions, mechanisms, the cellulose industry, simulation of particle behavior (granulated particles and molecules), dynamic simulation, military applications, computer games, medicine, and rehabilitation