Wave-based sensor, actuator and optimizer

Programa doutoral em Sistemas Avançados de Engenharia para a Indústria (AESI)A presente tese explora a utilização de ondas para abordar dois desafios significativos na indústria automóvel. O primeiro desafio consiste no desenvolvimento de um sistema de cancelamento ativo de ruído (ANC) que possa reduzir os ruídos não estacionários no compartimento de passageiros de um veículo. O segundo desafio é criar uma metodologia de conceção ótima para sensores de posição indutivos capazes de medir deslocamentos lineares, rotacionais e angulares. Para abordar o primeiro desafio, foi desenvolvido de um sistema ANC onde wavelets foram combinadas com um banco de filtros adaptativos. O sistema foi implementado em uma FPGA, e testes demonstraram que o sistema pode reduzir o ruído não estacionário em um ambiente acústico aberto e não controlado em 9 dB. O segundo desafio foi abordado através de uma metodologia que combina um algoritmo genético com um método numérico rápido para otimizar um sensor de posição indutivo. O método numérico foi usado para simular o campo eletromagnético associado à geometria do sensor, permitindo a maximização da corrente induzida nas bobinas recetoras e a minimização da não-linearidade no sensor. A minimização da não-linearidade foi conseguida através do desenho (layout) das bobinas que compõem o sensor. Sendo este otimizado no espaço de Fourier através da adição de harmónicos apropriados na geometria. As melhores geometrias otimizadas apresentaram uma não-linearidade inferior a 0,01% e a 0,25% da escala total para os sensores de posição angular e linear, respetivamente, sem calibração por software. O sistema ANC proposto tem o potencial de melhorar o conforto dos ocupantes do veículo, reduzindo o ruído indesejado dentro do compartimento de passageiros. Isso poderia reduzir o uso de materiais de isolamento acústico no veículo, levando a um veículo mais leve e, em última análise, a uma redução no consumo de energia. A metodologia desenvolvida para sensores de posição indutivos contribui para o estado da arte de sensores de posição eficientes e económicos, o que é crucial para os requisitos complexos da indústria automóvel. Essas contribuições têm implicações para o desenho de sistemas automotivos, com requisitos de desempenho e considerações ambientais e económicas.This thesis explores the use of waves to tackle two major engineering challenges in the automotive industry. The first challenge is the development of an Active Noise Cancelling (ANC) system that can effectively reduce non-stationary noise inside a vehicle’s passenger compartment. The second challenge is the optimization of an inductive position sensor design methodology capable of measuring linear, rotational, and angular displacements. To address the first challenge, this work designs an ANC system that employs wavelets combined with a bank of adaptive filters. The system was implemented in an FPGA, and field tests demonstrate its ability to reduce non-stationary noise in an open and uncontrolled acoustic environment by 9 dB. The second challenge was tackled by proposing a new approach that combines a genetic algorithm with a fast and lightweight numerical method to optimize the geometry of an inductive position sensor. The numerical method is used to simulate the sensor’s electromagnetic field, allowing for the maximization of induced current on the receiver coils while minimizing the sensor’s non-linearity. The non-linearity minimization was achieved through its unique sensor’s coils design optimized in the Fourier space by adding the appropriate harmonics to the coils’ geometry. The best optimized geometries exhibited a non-linearity of less than 0.01% and 0.25% of the full scale for the angular and linear position sensors, respectively. Both results were achieved without the need for signal calibration or post-processing manipulation. The proposed ANC system has the potential to enhance the comfort of vehicle occupants by reducing unwanted noise inside the passenger compartment. Moreover, it has the potential to reduce the use of acoustic insulation materials in the vehicle, leading to a lighter vehicle and ultimately reducing energy consumption. The developed methodology for inductive position sensors represents a state-of-the-art contribution to efficient and cost-effective position sensor design, which is crucial for meeting the complex requirements of the automotive industry.I would like to thank the Fundação para a Ciência e Tecnologia (FCT) and Bosch Car Multimedia for funding my PhD (grant PD/BDE/142901/2018)

Large pseudoscalar Yukawa couplings in the complex 2HDM

We start by presenting the current status of a complex flavour conserving two-Higgs doublet model. We will focus on some very interesting scenarios where unexpectedly the light Higgs couplings to leptons and to b-quarks can have a large pseudoscalar component with a vanishing scalar component. Predictions for the allowed parameter space at end of the next run with a total collected luminosity of $300 \, fb^{-1}$ and $3000 \, fb^{-1}$ are also discussed. These scenarios are not excluded by present data and most probably will survive the next LHC run. However, a measurement of the mixing angle $\phi_\tau$, between the scalar and pseudoscalar component of the 125 GeV Higgs, in the decay $h \to \tau^+ \tau^-$ will be able to probe many of these scenarios, even with low luminosity. Similarly, a measurement of $\phi_t$ in the vertex $\bar t t h$ could help to constrain the low $\tan \beta$ region in the Type I model.Comment: 21 pages, 10 figure

LGMD based neural network for automatic collision detection

Real-time collision detection in dynamic scenarios is a hard task if the algorithms used are based on conventional techniques of computer vision, since these are computationally complex and, consequently, time-consuming. On the other hand, bio-inspired visual sensors are suitable candidates for mobile robot navigation in unknown environments, due to their computational simplicity. The Lobula Giant Movement Detector (LGMD) neuron, located in the locust optic lobe, responds selectively to approaching objects. This neuron has been used to develop bio-inspired neural networks for collision avoidance. In this work, we propose a new LGMD model based on two previous models, in order to improve over them by incorporating other algorithms. To assess the real-time properties of the proposed model, it was applied to a real robot. Results shown that the LGMD neuron model can robustly support collision avoidance in complex visual scenarios.(undefined

Timed trajectory generation combined with an Extended Kalman Filter for a vision-based autonomous mobile robot

Series : Advances in intelligent systems and computing, vol. 193, ISSN 2194-5357Planning collision-free trajectories requires the combination of generation and modulation techniques. This is especially important if temporal stabilization of the generated trajectories is considered. Temporal stabilization means to conform to the planned movement time, in spite of environmental conditions or perturbations. This timing problem has not been addressed in most current robotic systems, and it is critical in several robotic tasks such as sequentially structured actions or human-robot interaction. This work focuses on generating trajectories for a mobile robot, whose goal is to reach a target within a constant time, independently of the world complexity. Trajectories are generated by nonlinear dynamical systems. Herein, we extend our previous work by including an Extended Kalman Filter (EKF) to estimate the target location relative to the robot. A simulated hospital environment and a Pioneer 3-AT robot are used to demonstrate the robustness and reliability of the proposed approach in cluttered, dynamic and uncontrolled scenarios. Multiple experiments confirm that the inclusion of the EKF preserves the timing properties of the overall architecture.Work supported by the Portuguese Science Foundation (grant PTDC/EEA-CRO/100655/2008), and by project FCT PEst-OE/EEI/LA0009/2011. Jorge B. Silva is supported by PhD Grant SFRH/BD/68805/2010, granted by the Portuguese Science Foundation

Developing a timed navigation architecture for hospital delivery robots

In hospitals, typical tasks of delivering goods between different locations are usually done by auxiliary staff. With the development of robotic technologies, such tasks can be performed by mobile robots releasing the staff effort to other tasks. In order to successfully complete the tasks of delivering goods inside hospitals, mobile robots should be able to generate trajectories free of collisions. In addition, including timing constraints to the generated trajectories has not been addressed in most current robotic systems, and it is critical in robotic tasks as human-robot interaction. Including timing constraints means to obey to the planned movement time, despite diversified environmental conditions or perturbations. In this paper we aim to develop a navigation architecture with timing constraints based on a mesh of nonlinear dynamical systems and feedthrough maps for wheeled mobile robots. A simulated hospital environment and a wheeled robot pioneer 3-DX are used to demonstrate the robustness and reliability of the proposed architecture in cluttered, dynamic and uncontrolled hospital scenarios

Generating trajectories with temporal constraints for an autonomous robot

Trajectory modulation and generation are two fundamental issues in the path planning problem in autonomous robotics, specially considering temporal stabilization of the generated movements. This is a very critical issue in several robotic tasks including: catching, hitting, and human-robot scenarios. In this work, we address these problems and focus on generating movement for a mobile robot, whose goal is to reach a target within a constant time. We use an Hopf oscillator whose solution controls velocity, adapted according to temporal feedback. We have also proposed an adaptive mechanism for frequency modulation of the velocity profile that enables setting different times for acceleration and deceleration. This approach is demonstrated on a DRK8000 mobile robot in order to confirm the system’s reliability with low-level sensors.(undefined

Timed trajectory generation for a toy-like wheeled robot

In this work, we address temporal stabilization of generated movements in autonomous robotics. We focus on generating movement for a mobile robot, that must reach a target location within a constant time. Target location is online calculated by using the robot visual system, such that action is steered by the sensory information. This is a very critical issue in several robotic tasks including: catching, hitting, and humanrobot scenarios. Robot velocity is controlled through an Hopf oscillator, adapted according to temporal feedback. Timing of the velocity profile is modulated according to an adaptive mechanism that enables setting different times for acceleration and deceleration. Results on a DRK8000 mobile robot confirm the system’s reliability with low-level sensors

Evaluation of Pozzolanic Reactivity of Artificial Pozzolans

Materials Science Forum Vols. 730-732 (2013) pp 433-438Pozzolanicity is a very interesting issue regarding building materials, as a way to enhance mortars and concrete durability. This property results from the reaction between calcium hydroxide and silica and alumina based materials. Different types of natural and artificial pozzolans show pozzolanic activities that differ depending on the materials characteristics. Therefore, the study of this property, namely its reactivity with calcium hydroxide, reveals itself to be important in the selection of the type and content of these materials. This paper presents the results of several pozzolanic reactivity methods, applied to different pozzolanic materials. The selected pozzolanic methods include Chapelle method, Fratinni method and Strength Activity Index. Those tests have been applied to evaluate the reactivity of various kinds of artificial pozzolans. The correlation between the test methods are presented and discussed