MOGA 4WD: multi-objective genetic algorithm for four-wheel drive electrical vehicle torque distribution in challenging conditions: MOGA 4WD: algoritmo genético multi-objetivo para distribuição de torque em veículos elétricos com tração em quatro rodas e em condições desafiadoras

This article aims to present a strategy for multi-objective optimization based on torque distribution for electrical 4WD (four-wheel drive) vehicles. By considering applications on uneven terrain, common to the navigation of tractors, off-road vehicles, or even mobile robots, an algorithm is developed having as input the vehicle attitude and output the controlled torque on each actuated wheel. The main criterion adopted is to guarantee the execution of a stable trajectory. And, to avoid wheel slippage, which occurs when low torques are applied, as well as vehicle rollover, which can occur in the presence of high torques, it is necessary to use two objective functions. To find the Pareto optimal solutions, the simplified dynamic model of a vehicle is adopted, considering a quasi-static motion. For each vehicle, its electrical, mechanical, and geometric characteristics can be used as formulation constraints. From an optimization performed offline, and adopting a polynomial approximation-based approach for real-time application, simulations and experiments show an interesting behavior: solutions that go beyond allowing the ascent of simple ramps or the overcoming of smooth obstacles are found - it is possible, for example, to climb ramps with high slopes, taking the vehicle to the limit between stability and instability

Is notch sensitivity a stress analysis problem?

Semi–empirical notch sensitivity factors q have been widely used to properly account for notch effects in fatigue design for a long time. However, the intrinsically empirical nature of this old concept can be avoided by modeling it using sound mechanical concepts that properly consider the influence of notch tip stress gradients on the growth behavior of mechanically short cracks. Moreover, this model requires only well establishedmechanical properties, as it has no need for data-fitting or similar ill-defined empirical parameters.In this way, the q value can now be calculated considering the characteristics of the notch geometry and of theloading, as well as the basic mechanical properties of the material, such as its fatigue limit and crack propagation threshold, if the problem is fatigue, or its equivalent resistances to crack initiation and to crack propagation under corrosion conditions, if the problem is environmentally assisted or stress corrosion cracking. Predictions based on this purely mechanical model have been validated by proper tests both in the fatigue and in the SCC cases, indicating that notch sensitivity can indeed be treated as a stress analysis problem

On the Dominant Role of Crack Closure on Fatigue Crack Growth Modeling

Abstract Crack closure is the most used mechanism to model thickness and load interaction effects on fatigue crack propagation. But assuming it is the only mechanism is equivalent to suppose that the rate of fatigue crack growth da/dN is primarily dependent on ⌬K eff = K max ϪK op , not on ⌬K. But this assumption would imply that the normal practice of using da/dN×⌬K curves measured under plane-stress conditions (without considering crack closure) to predict the fatigue life of components working under planestrain could lead to highly non-conservative errors, because the expected fatigue life of "thin" (plane-stress dominated) structures could be much higher than the life of "thick" (plane-strain dominated) ones, when both work under the same stress intensity range and load ratio. However, crack closure cannot be used to explain the overload-induced retardation effects found in this work under plane-strain, where both crack arrest and delays were associated to an increase in ⌬K eff . These results indicate that the dominant role of crack closure in the modeling of fatigue crack growth should be reviewed

Application of the Moment Of Inertia method to the Critical-Plane Approach

The Moment-Of-Inertia (MOI) method has been proposed by the authors to solve some of the shortcomings of convex-enclosure methods, when they are used to calculate path-equivalent ranges and mean components of complex non-proportional (NP) multiaxial load histories. In the proposed 2D version for use with critical-plane models, the MOI method considers the non-proportionality of the projected shear-shear history on each candidate plane through the shape of the load path, providing good results even for challenging non-convex paths. The MOI-calculated path-equivalent shear stress (or strain) ranges from each counted load event can then be used in any shear-based critical-plane multiaxial fatigue damage model, such as Findley’s or Fatemi-Socie’s. An efficient computer code with the shear-shear version of the MOI algorithm is also provided in this work. KEYWORDS. Multiaxial fatigue; Non-proportional loadings; Equivalent ranges; Critical-Plane Approach

A two-step multiaxial racetrack filter algorithm for non-proportional load histories

The recently proposed multiaxial racetrack filter (MRF) is able to deal with general non-proportional multiaxial load histories. While only requiring a single user-defined scalar filter amplitude, the MRF is able to synchronously eliminate non-damaging events from any noisy multiaxial load history without changing the overall shape of its original path, a necessary condition to avoid introducing errors in fatigue damage assessments. The MRF procedures are optimized here by the introduction of a pre-processing “partitioning” step on the load history data, which selects candidates for the reversal points in a robust partitioning process, highly increasing the filter efficiency and decreasing its computational time. The improved MRF is evaluated through the fatigue analyses of over-sampled tension-torsion data measured in 316L stainless steel tubular specimens under non-proportional load paths

A multiaxial incremental fatigue damage formulation using nested damage surfaces

Multiaxial fatigue damage calculations under non-proportional variable amplitude loadings still remains a quite challenging task in practical applications, in part because most fatigue models require cycle identification and counting to single out individual load events before quantifying the damage induced by them. Moreover, to account for the non-proportionality of the load path of each event, semi-empirical methods are required to calculate path-equivalent ranges, e.g. using a convex enclosure or the MOI (Moment Of Inertia) method. In this work, a novel Incremental Fatigue Damage methodology is introduced to continuously account for the accumulation of multiaxial fatigue damage under service loads, without requiring rainflow counters or path-equivalent range estimators. The proposed approach is not based on questionable Continuum Damage Mechanics concepts or on the integration of elastoplastic work. Instead, fatigue damage itself is continuously integrated, based on damage parameters adopted by traditional fatigue models well tested in engineering practice. A framework of nested damage surfaces is introduced, allowing the calculation of fatigue damage even for general 6D multiaxial load histories. The proposed approach is validated by non-proportional tensiontorsion experiments on tubular 316L stainless steel specimens

Incorporation of Mean/Maximum Stress Effects in the Multiaxial Racetrack Filter

This work extends the Multiaxial Racetrack Filter (MRF) to incorporate mean or maximum stress effects, adopting a filter amplitude that depends on the current stress level along the stress or strain path. In this way, a small stress or strain amplitude event can be filtered out if associated with a non-damaging low mean or peak stress level, while another event with the very same amplitude can be preserved if happening under a more damaging high mean or peak stress level. The variable value of the filter amplitude must be calculated in real time, thus it cannot depend on the peak or mean stresses along a load event, because it would require cycle identification and as so information about future events. Instead, mean/maximum stress effects are modeled in the filter as a function of the current (instantaneous) hydrostatic or normal stress along the multiaxial load path, respectively for invariantbased and critical-plane models. The MRF efficiency is evaluated from tension-torsion experiments in 316L stainless steel tubular specimens under non-proportional (NP) load paths, showing it can robustly filter out nondamaging events even under multiaxial NP variable amplitude loading histories. KEYWORDS. Multiaxial racetrack filter; Mean/peak stress effects; Nondamaging events; Multiaxial loads

A NEW TECHNIQUE IN MOBILE ROBOT SIMULTANEOUS LOCALIZATION AND MAPPING

ABSTRACT In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite. KEYWORDS: Service robots, visual mapping, selflocalization, information theory, Mellin transform. RESUMO Usualmente não é possível fornecer a priori a robôs móveis autônomos um mapa detalhado de seu ambiente de trabalho. Nestes casos, o robô precisa criar um modelo geométrico preciso movendo-se pelo ambiente e utilizando seus sensores. Neste trabalho, um algoritmo iterativo é proposto para planejar a estratégia de exploração de robôs móveis autôno-mos, permitindo-os construir de forma eficiente um modelo do ambiente em forma de grafo sem a necessidade de sensores de alto custo. Neste algoritmo, o conteúdo de informação presente em sub-regiões de uma imagem panorâmica 2-D do ambiente é determinada a partir da posição atual do robô usando uma única câmera fixada em sua estrutura. Usando uma métrica baseada na teoria da informação de Shannon, o algoritmo determina, a partir da imagem 2-D, localizações potenciais para novos nós do grafo, a partir dos quais serão tomadas novas imagens panorâmicas para prosseguir com a exploração. Uma transformada de Mellin é usada para guiar o robô de volta a um nó previamente explorado. Este processo continua até que todo o ambiente tenha sido explorado em um nível de detalhes pré-especificado. A eficácia do algoritmo é verificada experimentalmente através da exploração de um ambiente interno por um agente robótico móvel dispondo apenas de um conjunto limitado de sensores. PALAVRAS-CHAVE: Robôs móveis, mapeamento visual, auto-localização, teoria da informação, transformada de Mellin

A model to quantify fatigue crack growth by cyclic damage accumulation calculated by strip-yield procedures

Elber's hypothesis that DeltaKeff can be assumed as the driving force for fatigue crack growth (FCG) is the basis for strip-yield models widely used to predict fatigue lives under variable amplitude loads, although it does not explain all load sequence effects observed in practice. To verify if these models are indeed intrinsically better, the mechanics of a typical strip-yield model is used to predict FCG rates based both on Elber's ideas and on the alternative view that FCG is instead due to damage accumulation induced by the cyclic strain history ahead of the crack tip, which does not need or use DeltaKeff ideas. The main purpose here is to predict FCG using the cyclic strains induced by the plastic displacements calculated by strip-yield procedures, assuming there are strain limits associated both the with the FCG threshold and with the material toughness. Despite based on conflicting principles, both models can reproduce quite well FCG data, a somewhat surprising result that deserves to be carefully analyzed

Visual Localization and Mapping in Dynamic and Changing Environments

The real-world deployment of fully autonomous mobile robots depends on a robust SLAM (Simultaneous Localization and Mapping) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing environments, where objects are moved or replaced after the robot has already mapped the scene. This paper presents Changing-SLAM, a method for robust Visual SLAM in both dynamic and changing environments. This is achieved by using a Bayesian filter combined with a long-term data association algorithm. Also, it employs an efficient algorithm for dynamic keypoints filtering based on object detection that correctly identify features inside the bounding box that are not dynamic, preventing a depletion of features that could cause lost tracks. Furthermore, a new dataset was developed with RGB-D data especially designed for the evaluation of changing environments on an object level, called PUC-USP dataset. Six sequences were created using a mobile robot, an RGB-D camera and a motion capture system. The sequences were designed to capture different scenarios that could lead to a tracking failure or a map corruption. To the best of our knowledge, Changing-SLAM is the first Visual SLAM system that is robust to both dynamic and changing environments, not assuming a given camera pose or a known map, being also able to operate in real time. The proposed method was evaluated using benchmark datasets and compared with other state-of-the-art methods, proving to be highly accurate.Comment: 14 pages, 13 figure