Analyses of strain localisation in hdpe butterfly specimen during biaxial tests using digital image correlation

This work presents a study of high density polyethylene (hdpe) strain under biaxial loading. An Arcan apparatus is achieved in order to load a newly-designed flat specimen called “butterfly specimen” to various combinations of tensile and shear loading.These specimens have a central region with a minimal thickness (1mm); witch constitutes a small area where the strain and the stress should be uniform before necking. All tests are conducted on an Instrone tensile machine at constant speed of the upper cross-head (v = 0,5 mm/min) at the ambient temperature. Displacement fields are measured in the central area of the specimens, during the tests, by coupling digital image correlation (DIC) with imaging using high-speed CCD cameras placed in front of the specimen. The experimental results show a strain localisation in the specimen gauge section

Novel mobile robot path planning algorithm

In this present work we propose a novel mobile robot path planning algorithm. Autonomous robots which work without human operators are required in robotic fields. In order to achieve tasks, autonomous robots have to be intelligent and should decide their own action. When the autonomous robot decides its action, it is necessary to plan optimally depending on their tasks. More, when a robot moves from a point to a target point in its given environment, it is necessary to plan an optimal or feasible path avoiding obstacles in its way and answer to some criterion of autonomy requirements such as : thermal, energy, time, and safety for example. First, we assume that the goal position is unknown. Secondly, only obstacles in the “relevant” area (according to the logical position ) are consider, i.e. the obstacles that are far, or in the direction opposite to the movement of the robot are not relevant. In this context, a full range of “main sub_position concepts” for vehicle control have been investigated by the execution of the asked mission. These feasible sub_position works demonstrate that obstacle detection and collision avoidance are improved with good results. While this model has been successful for the path planning problem, it is problematic for robots to react, act, decide, and to take a suitable action ”high level reasoning”. Much of the challenge of the mobile robots requires intelligence at subconscious level. In this context, the proposed path planning algorithm provides the robot the possibility to move from the initial position to the final position (target). The results are satisfactory to see the great number of environments treate

Route map generation

In this paper we present a route map generation of an autonomous mobile robot. The work in path planning has led into issues of map representation for a real world. Therefore, this problem is considered as one challenge in the field of mobile robots because of its direct effect for having a simple computationally efficient path planning strategy. For the real application in a real environment, it is necessary for the mobile robot to have a real time section while executing the planned path connected the start point and the goal point. The robot must then be able to understand the structure of the environment to find a way towards its target without collisions. To perform well this task several requirements must be satisfied and intelligent components become a necessity. More, world understanding and data interpreting is very solicited in any way of navigation When the target position is detected, the path planner will generate the proper path between the start and the goal position. This is called path planning step. The next step is to generate the geometric information of the generated path by searching the ways around the robot along the paths. This is called route map generation. When a route map generation is done, the next work is to control the robot itself to execute the route map, in order to achieve the goal planned by path planner and it is named as route runner. This is will be more clarified by the proposed work while answering to some interesting questions. The software implementation is very interesting to see the main factors are realize

Épistémologies socio-sémiotiques et communication organisante : la coproduction de sens comme moteur de l’organisation

Cet article présente une synthèse des théories sémiotiques compatibles avec une approche communicationnelle des organisations. Les organisations sont définies comme la résultante d’un système d’interactions complexe orienté vers l’accomplissement de projets d’action. Les acteurs, par leur communication, structurent et résolvent trois niveaux de problèmes communicationnels correspondant à trois dimensions sémiotiques, les procédures de production de symboles, les schèmes interprétatifs des symboles et les actions motivées et orientées vers la provocation d’un effet organisant l’activité collective. Les dimensions sémiotiques de la communication reflètent la matrice sociale-cognitive qui les supporte. Les socio-sémiotiques ont revalorisé les approches phénoménologiques de l’organisation insistant sur l’importance des points de vue intersubjectifs et la matérialité des phénomènes de communication organisante. Les concepts et les méthodes socio‑sémiotiques outillent le chercheur en communication des organisations axé sur l’analyse du développement des organisations et de l’identité collective.This paper aims to summarize semiotic theories which are compatible with a communicational approach to organizations. Organizations are defined as the result of a complex system of interactions directed towards the accomplishment of projects. Actors, through their communication, structure and solve communication problems of three types that correspond to three semiotic dimensions, procedures for symbols’ production, interpretive schemes of symbols, and motivated actions oriented towards the provocation of an organizing effect. These semiotic dimensions reflect the social-cognitive matrix that supports them. Socio-semiotics approaches have placed emphasis on the phenomenological properties of organizations. They stress the importance of intersubjective views and the materiality of organizing communications. The concepts and methods proposed by socio-semiotics empower the researcher in organizational communication when he is focused on the analysis of organizational development and collective identit

World understanding and planning missions

In this paper, we present an intelligent control of an autonomous mobile robot in unknown environments. When an autonomous robot moves from an initial point to a target point in its given environment, it is necessary to plan an optimal or feasible path avoiding obstacles in its way and answer to some criterion of autonomy requirements such as : thermal, energy, time, and safety for example. Therefore, the major main work for path planning for autonomous mobile robot is to search a collision free path. . A key prerequisite for a truly autonomous robot is that it can navigate safely within its environment and executing the task without doubt. The problem of achieving this mobility is one of the most active areas in mobile robotics research. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In order to get an intelligent component, the proposed approach based on intelligent computing offers to the autonomous mobile system the ability to realize these factors: recognition, learning, decision-making, and action (the principle obstacle avoidance problems) which are the main factors to be considered in any design of navigation approach. The acquisition of these faculties constitutes the key of a certain kind of intelligence. Building this kind of intelligence is, up to now, a human ambition in the design and development of intelligent vehicles. However, the mobile robot is an appropriate tool for investing optional artificial intelligence problems relating to world understanding and taking a suitable action, such as, planning missions, avoiding obstacles, and fusing data from many sources. In this context we discuss this ability by proposing this approach. The results are promising for next development

Intelligent autonomous path planning systems

The theory and practice of Intelligent Autonomous Robot IAR are currently among the most intensively studied and promising areas in computer science and engineering which will certainly play a primary goal role in future. These theories and applications provide a source linking all fields in which intelligent control plays a dominant role. Cognition, perception, action, and learning are essential components of such-systems and their use is tending extensively towards challenging applications (service robots, micro-robots, bio-robots, guard robots, warehousing robots). The present paper studies the problem of motion of a mobile robot that moves inside an unknown environment with stationary unknown obstacles. This paper deals with the main principles of Intelligent Autonomous Systems IAS Path Planning and illustrates some criteria to be taken into account in any intelligent navigation control of IAS. For any starting point within the environment representing the initial position of the mobile robo

Multi-object tracking and classification : contributions with belief functions theory

Cette thèse aborde le problèeme du suivi et de la classification de plusieurs objets simultanément.Il est montré dans la thèese que les fonctions de croyance permettent d'améliorer les résultatsfournis par des méthodes classiques à base d'approches Bayésiennes. En particulier, une précédenteapproche développée dans le cas d'un seul objet est étendue au cas de plusieurs objets. Il est montréque dans toutes les approches multi-objets, la phase d'association entre observations et objetsconnus est fondamentale. Cette thèse propose également de nouvelles méthodes d'associationcrédales qui apparaissent plus robustes que celles trouvées dans la littérature. Enfin, est abordée laquestion de la classification multi-capteurs qui nécessite une seconde phase d'association. Dans cedernier cas, deux architectures de fusion des données capteurs sont proposées, une dite centraliséeet une autre dite distribuée. De nombreuses comparaisons illustrent l'intérêt de ces travaux, queles classes des objets soient constantes ou variantes dans le temps.This thesis deals with multi-objet tracking and classification problem. It was shown that belieffunctions allow the results of classical Bayesian methods to be improved. In particular, a recentapproach dedicated to a single object classification which is extended to multi-object framework. Itwas shown that detected observations to known objects assignment is a fundamental issue in multiobjecttracking and classification solutions. New assignment solutions based on belief functionsare proposed in this thesis, they are shown to be more robust than the other credal solutions fromrecent literature. Finally, the issue of multi-sensor classification that requires a second phase ofassignment is addressed. In the latter case, two different multi-sensor architectures are proposed, aso-called centralized one and another said distributed. Many comparisons illustrate the importanceof this work, in both situations of constant and changing objects classes

Neural path planning for mobile robots

Navigation is a major challenge for autonomous, mobile robots. The problem can basically be divided into positioning and path planning. The proposed path finding strategy is designed in a known static environments. The proposed method starts from an initial point to a target point establishing a control nodes neural networks for which connections are made to determine the form of the path. This algorithm provides the robot the possibility to move from the initial position to the final position (target). The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. The proposed algorithm can deal with any shape obstacles even if it is the case of circular obstacles. This case is the hardest one in any navigation problem. The problem is solved by proposing neural networks navigation systems. Indeed, NNs are well adapted in appropriate form when knowledge based systems are involved. Since the network is able to take into account and respond to new constraints and data related to the external environments, the adaptation here is largely related to the learning capacity. Besides, Networks of neurons can achieve complex classification based on the elementary capability of each neuron to distinguish classes its activation function. Some useful solutions are proposed for each situation. For any proposed environment, the robot succeeds to reach its target without collisions. The results are satisfactory to see the great number of environments treated The simulation results display the ability of the neural networks based approach providing autonomous mobile robots with capability to intelligently navigate in several environment