Le travail de thèse décrit dans ce manuscrit concerne les Systèmes Avancés d’Aide à la Conduite (ADAS) qui sont devenus de nos jours un axe de recherche stratégique chez de nombreux constructeurs automobiles. Ce type de systèmes peuvent être considérés comme la première génération de dispositifs de conduite assistée ou semi-autonome et qui ouvrira la voie aux véhicules pleinement autonomes. La première partie de ce manuscrit concerne l’analyse et la commande pour les applications de contrôle de la dynamique latérale du véhicule – autoguidage par suivi de cible et aide au maintien au centre de la voie (LCA). Dans ce cadre, la sécurité joue un rôle clé, mettant en lumière la mise en oeuvre différentes techniques de commande contrainte pour des modèles linéaires à paramètres variants (LPV). La commande prédictive (MPC) et la commande par interpolation (IBC) ont été sélectionnés dans ce travail. De plus, la conception d’un système de commande robuste qui assure un comportement correct malgré la variation des paramètres du système ou la présence d’incertitudes est une caractéristique critique. Les outils de la théorie de l’invariance positive robuste (RPI) sont pris en considération pour la conception de stratégies de commande robustes LPV par rapport aux larges variations de la vitesse véhicule et aux changements de courbure de la route. Le second axe de cette thèse est la planification optimale de trajectoire pour les manouvres de dépassement et de changement de voie sur autoroute, avec réduction des risques de collision. Pour atteindre cet objectif, la description exhaustive des scénarios possible est présentée, permettant de formuler un problème d’optimisation qui maximise le confort du conducteur et assure la satisfaction des contraintes du système. / The thesis work contained in this manuscript is dedicated to the Advanced Driving Assistance Systems, which has become nowadays a strategic research line in many car companies. This kind of systems can be seen as a first generation of assisted or semi-autonomous driving, that will set the way to fully automated vehicles. The first part focuses on the analysis and control of lateral dynamics control applications - Autosteer by target tracking and the Lane Centering Assistance System (LCA). In this framework, safety plays a key role, bringing into focus the application of different constrained control techniques for linear parametervarying (LPV) models. Model Predictive Control (MPC) and Interpolation Based Control (IBC) have been the selected ones in the present work. In addition, it is a critical feature to design robust control systems that ensure a correct behavior under system's variation of parameters or in the presence of uncertainty. Robust Positive Invariance (RPI) theory tools are considered to design robust LPV control strategies with respect to large vehicle speed variations and curvature of the road changes. The second axis of this thesis is the optimization-based trajectory planning for overtaking and lane change in highways with anti-collision enhancements. To achieve this goal, an exhaustive description of the possible scenarios that may arise is presented, allowing to formulate an optimization problem which maximizes passenger comfort and ensures system constraints' satisfaction.

Comme les robots effectuent de plus en plus de tâches en interaction avec l'homme ou dans un environnement humain, ils doivent assurer la sécurité et le confort des hommes. Dans ce contexte, le robot doit adapter son comportement et agir en fonction des évolutions de l'environnement et des activités humaines. Les robots développés sur la base de l'apprentissage ou d'un planificateur de mouvement ne sont pas en mesure de réagir assez rapidement, c'est pourquoi nous proposons d'introduire un contrôleur de trajectoire intermédiaire dans l'architecture logicielle entre le contrôleur bas niveau et le planificateur de plus haut niveau. Le contrôleur de trajectoire que nous proposons est basé sur le concept de générateur de trajectoire en ligne (OTG), il permet de calculer des trajectoires en temps réel et facilite la communication entre les différents éléments, en particulier le planificateur de chemin, le générateur de trajectoire, le détecteur de collision et le contrôleur. Pour éviter de replanifier toute une trajectoire en réaction à un changement induit par un humain, notre contrôleur autorise la déformation locale de la trajectoire et la modification de la loi d'évolution pour accélérer ou décélérer le mouvement. Le contrôleur de trajectoire peut également commuter de la trajectoire initiale vers une nouvelle trajectoire. Les fonctions polynomiales cubiques que nous utilisons pour décrire les trajectoires fournissent des mouvements souples et de la flexibilité sans nécessiter de calculs complexes. De plus, les algorithmes de lissage que nous proposons permettent de produire des mouvements esthétiques ressemblants à ceux des humains. Ce travail, mené dans le cadre du projet ANR ICARO, a été intégré et validé avec les robots KUKA LWR de la plate-forme robotique du LAAS-CNRS. / In order to perform a large variety of tasks in interaction with human or in human environments, a robot needs to guarantee safety and comfort for humans. In this context, the robot shall adapt its behavior and react to the environment changes and human activities. The robots based on learning or motion planning are not able to adapt fast enough, so we propose to use a trajectory controller as an intermediate control layer in the software structure. This intermediate layer exchanges information with the low level controller and the high level planner. The proposed trajectory controller, based on the concept of Online Trajectory Generation (OTG), allows real time computation of trajectories and easy communication with the different components, including path planner, trajectory generator, collision checker and controller. To avoid the replan of an entire trajectory when reacting to a human behaviour change, the controller must allow deforming locally a trajectory or accelerate/decelerate by modifying the time function. The trajectory controller must also accept to switch from an initial trajectory to a new trajectory to follow. Cubic polynomial functions are used to describe trajectories, they provide smoothness, flexibility and computational simplicity. Moreover, to satisfy the objective of aesthetics, smoothing algorithm are proposed to produce human-like motions. This work, conducted as part of the ANR project ICARO, has been integrated and validated on the KUKA LWR robot platform of LAAS-CNRS.

Path planning that considers accurate vehicle dynamics and obstacle avoidance is an important problem in the area of autonomous driving. This paper describes a method of implementing trajectory planning for autonomous parking in conditions where the starting point and the position of fixed obstacles are uncertain. The narrow spaces and complicated manoeuvres required for parking demands a lot from the trajectory planning algorithm. It needs to have the ability to accurately model vehicle dynamics and find an efficient way around obstacles. Having obstacles in the way of the parking vehicle makes this a nonconvex problem the goal can usually not be reached by travelling in a straight line and finding a perfect trajectory around them is generally not computationally tractable. This paper reviews a two tiered approach to solving this problem. First a rough path is found using a modified Rapidly-exploring Random Tree (RRT) algorithm called Forward-Backward RRT, which runs two treebuilding processes in parallel and constructs a feasible path from where they intersect. Using optimisation this is then improved into a trajectory that is at least a local optimum. These methods will be demonstrated to produce efficient and feasible trajectories that respects the dynamic constraints of the vehicle and avoids collisions.

In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second a robot testbed is designed to test new trajectory planning algorithms. A templatebased predictive search algorithm is investigated to process the images obtained through a lowcost webcam vision system, which is used to monitor the testbed environment. Also a userfriendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, Bspline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, iv obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms.

The security of multi-robot systems is critical in various applications such as patrol, transportation, and search and rescue operations, where they face threats from adversaries attempting to gain control of the robots. These compromised robots are significant threats as they allow attackers to steer robots towards forbidden areas without being detected, potentially causing harm or compromising the mission. To address this problem, we propose a resilient planning, task assignment, and control framework. The proposed framework builds a multi-robot plan where robots are designed to get close enough to other robots according to a co-observation schedule, in order to mutually check for abnormal behaviors. For the first part of the thesis, we propose an optimal trajectory solver based on the alternating direction method of multipliers (ADMM) to generate multi-agent trajectories that satisfy spatio-temporal requirements introduced by the co-observation schedules. As part of the formulation, we provide a new reachability constraint to guarantee that, despite adversarial movement by the attacker, a compromised robot cannot reach forbidden areas between co-observations without being detected. In the second part of the thesis, to further enhance the system's performance, reliability, and robustness, we propose to deploy multiple robots on each route to form sub-teams. A new cross-trajectory co-observation scheme between sub-teams is introduced that preserves the optimal unsecured trajectories. The new planner ensures that at least one robot in each sub-team sticks to the planned trajectories, while sub-teams can constantly exchange robots during the task introducing additional co-observations that can secure originally unsecured routes. We show that the planning of cross-trajectory co-observations can be transformed into a network flow problem and solved using traditional linear program technique. In the final part of the thesis, we show that the introduction of sub-teams also improves the multi-robot system's robustness to unplanned situations, allowing servicing unplanned online events without breaking the security requirements. This is achieved by a distributed task assignment algorithm based on consensus ADMM which can handle tasks with different priorities. The assignment result and security requirements are formulated as spatio-temporal schedules and guaranteed through control barrier function (CBF) based controls.

The development of autonomous vehicles is progressing rapidly through extensive efforts by the automotive industry and researchers. One of the key factors for the adoption of autonomous driving technology is motion comfort and the ability to engage in non-driving tasks such as reading, socialising, and relaxing without experiencing motion sickness while travelling. Therefore, for the full success of autonomous vehicles, it is necessary to learn how to design and control the vehicles to mitigate motion sickness for the passengers.  This thesis aims to investigate methods for prediction of motion sickness in autonomous vehicles and how to mitigate it using vehicle dynamics based solutions, with an emphasis on trajectory planning. As a first step, a review and evaluation of existing motion sickness prediction methods were performed. The review highlighted the importance of accurate motion sickness assessment in the early phases of autonomous vehicle design. Two chosen methods (ISO 2631-based and sensory conflict theory-based) were evaluated to estimate individual motion sickness feelings using measured data and subjective assessment ratings from field tests. It can be concluded that the methods can be adjusted to predict individual motion sickness feelings, as shown by the comparison with the experimental data. To continue the work, a review of vehicle dynamics based motion sickness mitigation methods for autonomous vehicles was performed. Several chassis control strategies in literature like active suspension, rear-wheel steering and torque distribution have demonstrated the potential help to reduce motion sickness. Another effective approach to mitigate motion sickness in autonomous vehicles is to regulate vehicle speed and path using trajectory planning which was chosen to be further investigated. The trajectory planning was constructed as an optimisation problem where there is a trade-off between motion sickness and manoeuvre time. The impact of the trajectory planning algorithm to reduce motion sickness was analysed by simulating two different vehicle models in specific test manoeuvres. The results indicate that driving style has a significant influence on motion sickness and trajectory planning algorithms should be carefully designed to find a good balance between journey time and motion sickness. The research presented in this thesis contributes to the development of methodologies for predicting and mitigating motion sickness in autonomous vehicles, helping to achieve the goal of ensuring their overall success. / Utvecklingen av autonoma fordon går snabbt framåt tack vare omfattande insatser från fordonsindustrin och forskare. En av de viktigaste faktorerna för införandet av teknik för autonom körning är åkkomfort och möjligheten att ägna sig åt andra saker än körning, som att läsa, umgås och koppla av, utan att drabbas av åksjuka under resan. För att autonoma fordon ska lyckas fullt ut är det därför nödvändigt att förstå hur man utformar och styr fordonen för att minska risken för att passagerarna drabbas av åksjuka.  Denna licentiatuppsats syftar till att undersöka hur åksjuka kan förutsägas i vägfordon och hur den kan reduceras med hjälp av fordonsdynamikbaserade lösningar, med tonvikt på trajektorieplanering. Som ett första steg genomfördes en granskning och utvärdering av befintliga metoder för åksjukeprediktion. Granskningen belyste vikten av en korrekt bedömning av åksjuka i de tidiga faserna av autonom fordonsdesign. Två valda metoder (ISO 2631-baserad och sensorisk konfliktbaserad) utvärderades för att uppskatta individuell åksjuka med hjälp av uppmätta data och subjektiva bedömningar från fälttester. Slutsatsen är att metoderna kan justeras för att förutsäga individuell åksjuka, vilket framgår av jämförelsen med experimentella data. För att fortsätta arbetet gjordes en genomgång av fordonsdynamikbaserade metoder för att minska åksjuka i autonoma fordon. Flera chassireglerstrategier i litteraturen, såsom aktiv fjädring, bakhjulsstyrning och drivmomentfördelning, har visat sig kunna bidra till att minska åksjuka. En annan effektiv metod för att minska åksjuka i autonoma fordon är att reglera fordonets hastighet och bana med hjälp av trajektorieplanering, vilket valdes att undersökas ytterligare. Trajektorieplaneringen konstruerades som ett optimeringsproblem där det finns en avvägning mellan åksjuka och manövertid. Effekten av trajektorieplaneringsalgoritmen för att minska åksjuka analyserades genom att simulera två olika fordonsmodeller i specifika testmanövrar. Resultaten indikerar att körstil har en betydande inverkan på åksjuka och att algoritmer för trajektorieplanering bör utformas noggrant för att hitta en bra balans mellan restid och åksjuka. Forskningen som presenteras i denna uppsats bidrar till utvecklingen av metoder för att förutsäga och mildra åksjuka i autonoma fordon, vilket hjälper till att uppnå målet att säkerställa deras framgång.

Les travaux de cette thèse portent sur la navigation des véhicules autonomes, notamment la planification de trajectoires et le contrôle du véhicule. En premier lieu, un modèle véhicule plan est développé en utilisant une technique de modélisation qui assimile le véhicule à un robot constitué de plusieurs corps articulés. La description géométrique du véhicule est basée sur la convention de Denavit-Hartenberg modifiée. Le modèle dynamique du véhicule est ensuite calculé en utilisant la méthode récursive de Newton-Euler, qui est souvent utilisée dans le domaine de robotique. La validation du modèle a été conduite sur le simulateur Scaner-Studio développé par Oktal pour les applications automobiles. Le modèle du véhicule développé est ensuite utilisé pour la synthèse de lois de commande couplées pour les dynamiques longitudinale et latérale du véhicule. Deux correcteurs sont proposés dans ce travail : le premier est basé sur les techniques de commande par Lyapunov, le second utilise une approche ”Immersion et Invariance”. Ces deux contrôleurs ont pour objectifs de suivre une trajectoire de référence donnée avec un profil de vitesse désirée, tout en tenant compte du couplage existant entre les dynamiques longitudinale et latérale du véhicule. En effet, le contrôle couplé est nécessaire pour garantir la sécurité du véhicule autonome surtout lors de l’exécution des manœuvres couplées comme les manœuvres de changement de voie, les manœuvres d’évitement d’obstacles et les manœuvres exécutées dans les situations de conduite critiques. Les contrôleurs développés ont été validés en simulation sous Matlab/Simulink en utilisant des données expérimentales. Par la suite, ces contrôleurs ont été validés expérimentalement en utilisant le véhicule démonstrateur robotisé (Renault-Zoé) du laboratoire Heudiasyc financé par l’Equipex Robotex. En ce qui concerne la planification de trajectoires, une méthode de planification basée sur la méthode des tentacules sous forme de clothoides a été développée. En outre, une méthode de planification de manœuvres qui s’intéresse essentiellement à la manœuvre de dépassement a été mise en place, afin d’améliorer et de compléter la méthode locale des tentacules. Le planificateur local et le planificateur de manœuvres ont été ensuite combinés pour établir une stratégie de navigation complète. Cette stratégie a été validée par la suite sous Matlab/Simulink en utilisant le modèle de véhicule développé et le contrôleur basé sur Lyapunov. / In this thesis, the trajectory planning and the control of autonomous vehicles are addressed. As a first step, a multi-body modeling technique is used to develop a four wheeled vehicle planar model. This technique considers the vehicle as a robot consisting of articulated bodies. The geometric description of the vehicle system is derived using the modified Denavit Hartenberg parameterization and then the dynamic model of the vehicle is computed by applying a recursive method used in robotics, namely Newton-Euler based Algorithm. The validation of the developed vehicle model was then conducted using an automotive simulator developed by Oktal, the Scaner-Studio simulator. The developed vehicle model is then used to derive coupled control laws for the lateral and the longitudinal vehicle dynamics. Two coupled controllers are proposed in this thesis: In the first controller, the control is designed using Lyapunov control techniques while in the second one an Immersion and Invariance approach is used. Both of the controllers aim to ensure a robust tracking of the reference trajectory and the desired speed while taking into account the strong coupling between the lateral and the longitudinal vehicle dynamics. In fact, the coupled controller is a key step for the vehicle safety handling, especially in coupled maneuvers such as lane-change maneuvers, obstacle avoidance maneuvers and combined maneuvers in critical driving situations. The developed controllers were validated in simulation under Matlab/Simulink using experimental data. Subsequently, an experimental validation of the proposed controllers was conducted using a robotized vehicle (Renault-ZOE) present in the Heudiasyc laboratory within the Equipex Robotex project. Concerning the trajectory planning, a local planning method based on the clothoid tentacles method is developed. Moreover, a maneuver planning strategy focusing on the overtaking maneuver is developed to improve and complete the local planning approach. The local and the maneuver planners are then combined in order to establish a complete navigation strategy. This strategy is then validated using the developed robotics vehicle model and the Lyapunov based controller under Matlab/Simulink.

In the industrial area, robots are an important part of the technological resources available to perform manipulation tasks in manufacturing, assembly, the transportation of dangerous waste, and a variety of applications. Specialized systems of computer vision have entered the market to solve problems that other technologies have been unable to address. This document analyzes a stereo vision system that is used to provide the center of mass of an object in three dimensions. This kind of application is mounted using two or more cameras that are aligned along the same axis and give the possibility to measure the depth of a point in the space. The stereoscopic system described, measures the position of an object using a combination between the 2D recognition, which implies the calculus of the coordinates of the center of mass and using moments, and the disparity that is found comparing two images: one of the right and one of the left. This converts the system into a 3D reality viewfinder, emulating the human eyes, which are capable of distinguishing depth with good precision.The proposed stereo vision system is integrated into a 5 degree of freedom pneumatic robot, which can be programmed using the GRAFCET method by means of commercial software. The cameras are mounted in the lateral plane of the robot to ensure that all the pieces in the robot's work area can be observed.For the implementation, an algorithm is developed for recognition and position measurement using open sources in C++. This ensures that the system can remain as open as possible once it is integrated with the robot. The validation of the work is accomplished by taking samples of the objects to be manipulated and generating robot's trajectories to see if the object can be manipulated by its end effector or not. The results show that is possible to manipulate pieces in a visually crowded space with acceptable precision. However, the precision reached does not allow the robot to perform tasks that require higher accuracy as the one is needed in manufacturing assembly process of little pieces or in welding applications. / En el área industrial los robots forman parte importante del recurso tecnológico disponible para tareas de manipulación en manufactura, ensamble, manejo de residuos peligrosos y aplicaciones varias. Los sistemas de visión computacional se han ingresado al mercado como soluciones a problemas que otros tipos de sensores y métodos no han podido solucionar. El presente trabajo analiza un sistema de visión estereoscópico aplicado a un robot. Este arreglo permite la medición de coordenadas del centro de un objeto en las tres dimensiones, de modo que, le da al robot la posibilidad de trabajar en el espacio y no solo en un plano. El sistema estereoscópico consiste en el uso de dos o más cámaras alineadas en alguno de sus ejes, mediante las cuales, es posible calcular la profundidad a la que se encuentran los objetos. En el presente, se mide la posición de un objeto haciendo una combinación entre el reconocimiento 2D y la medición de las coordenadas y de su centro calculadas usando momentos. En el sistema estereoscópico, se añade la medición de la última coordenada mediante el cálculo de la disparidad encontrada entre las imágenes de las cámaras inalámbricas izquierda y derecha, que convierte al sistema en un visor 3D de la realidad, emulando los ojos humanos capaces de distinguir profundidades con cierta precisión. El sistema de visión computacional propuesto es integrado a un robot neumático de 5 grados de libertad el cual puede ser programado desde la metodología GRAFCET mediante software de uso comercial. Las cámaras del sistema de visión están montadas en el plano lateral del robot de modo tal, que es posible visualizar las piezas que quedan dentro de su volumen de trabajo. En la implementación, se desarrolla un algoritmo de reconocimiento y medición de posición, haciendo uso de software libre en lenguaje C++. De modo que, en la integración con el robot, el sistema pueda ser lo más abierto posible. La validación del trabajo se logra tomando muestras de los objetos a ser manipulados y generando trayectorias para el robot, a fin de visualizar si la pieza pudo ser captada por su garra neumática o no. Los resultados muestran que es posible lograr la manipulación de piezas en un ambiente visualmente cargado y con una precisión aceptable. Sin embargo, se observa que la precisión no permite que el sistema pueda ser usado en aplicaciones donde se requiere precisión al nivel de los procesos de ensamblado de piezas pequeñas o de soldadura.

Nature and its variety of motion forms have inspired new robot designs with inherentunderactuated dynamics. The fundamental characteristic of these controlled mechanicalsystems, called underactuated, is to have the number of actuators less than the number ofdegrees of freedom. The absence of full actuation brings challenges to planning feasibletrajectories and designing controllers. This is in contrast to classical fully-actuated robots.A particular problem that arises upon study of such systems is that of generating periodicmotions, which can be seen in various natural actions such as walking, running,hopping, dribbling a ball, etc. It is assumed that dynamics can be modeled by a classicalset of second-order nonlinear differential equations with impulse effects describing possibleinstantaneous impacts, such as the collision of the foot with the ground at heel strikein a walking gait. Hence, we arrive at creating periodic solutions in underactuated Euler-Lagrange systems with or without impulse effects. However, in the qualitative theory ofnonlinear dynamical systems, the problem of verifying existence of periodic trajectoriesis a rather nontrivial subject.The aim of this work is to propose systematic procedures to plan such motions and ananalytical technique to design orbitally stabilizing feedback controllers. We analyze andexemplify both cases, when the robotmodel is described just by continuous dynamics, andwhen continuous dynamics is interrupted from time to time by state-dependent updates.For trajectory planning, systems with one or two passive links are considered, forwhich conditions are derived to achieve periodicmotions by encoding synchronizedmovementsof all the degrees of freedom. For controller design we use an explicit form tolinearize dynamics transverse to the motion. This computation is valid for an arbitrarydegree of under-actuation. The linear system obtained, called transverse linearization, isused to analyze local properties in a vicinity of the motion, and also to design feedbackcontrollers. The theoretical background of these methods is presented, and developedin detail for some particular examples. They include the generation of oscillations forinverted pendulums, the analysis of human movements by captured motion data, and asystematic gait synthesis approach for a three-link biped walker with one actuator.

In the industrial area, robots are an important part of the technological resources available to perform manipulation tasks in manufacturing, assembly, the transportation of dangerous waste, and a variety of applications. Specialized systems of computer vision have entered the market to solve problems that other technologies have been unable to address. This document analyzes a stereo vision system that is used to provide the center of mass of an object in three dimensions. This kind of application is mounted using two or more cameras that are aligned along the same axis and give the possibility to measure the depth of a point in the space. The stereoscopic system described, measures the position of an object using a combination between the 2D recognition, which implies the calculus of the coordinates of the center of mass and using moments, and the disparity that is found comparing two images: one of the right and one of the left. This converts the system into a 3D reality viewfinder, emulating the human eyes, which are capable of distinguishing depth with good precision.The proposed stereo vision system is integrated into a 5 degree of freedom pneumatic robot, which can be programmed using the GRAFCET method by means of commercial software. The cameras are mounted in the lateral plane of the robot to ensure that all the pieces in the robot's work area can be observed.For the implementation, an algorithm is developed for recognition and position measurement using open sources in C++. This ensures that the system can remain as open as possible once it is integrated with the robot. The validation of the work is accomplished by taking samples of the objects to be manipulated and generating robot's trajectories to see if the object can be manipulated by its end effector or not. The results show that is possible to manipulate pieces in a visually crowded space with acceptable precision. However, the precision reached does not allow the robot to perform tasks that require higher accuracy as the one is needed in manufacturing assembly process of little pieces or in welding applications. / En el área industrial los robots forman parte importante del recurso tecnológico disponible para tareas de manipulación en manufactura, ensamble, manejo de residuos peligrosos y aplicaciones varias. Los sistemas de visión computacional se han ingresado al mercado como soluciones a problemas que otros tipos de sensores y métodos no han podido solucionar. El presente trabajo analiza un sistema de visión estereoscópico aplicado a un robot. Este arreglo permite la medición de coordenadas del centro de un objeto en las tres dimensiones, de modo que, le da al robot la posibilidad de trabajar en el espacio y no solo en un plano. El sistema estereoscópico consiste en el uso de dos o más cámaras alineadas en alguno de sus ejes, mediante las cuales, es posible calcular la profundidad a la que se encuentran los objetos. En el presente, se mide la posición de un objeto haciendo una combinación entre el reconocimiento 2D y la medición de las coordenadas y de su centro calculadas usando momentos. En el sistema estereoscópico, se añade la medición de la última coordenada mediante el cálculo de la disparidad encontrada entre las imágenes de las cámaras inalámbricas izquierda y derecha, que convierte al sistema en un visor 3D de la realidad, emulando los ojos humanos capaces de distinguir profundidades con cierta precisión. El sistema de visión computacional propuesto es integrado a un robot neumático de 5 grados de libertad el cual puede ser programado desde la metodología GRAFCET mediante software de uso comercial. Las cámaras del sistema de visión están montadas en el plano lateral del robot de modo tal, que es posible visualizar las piezas que quedan dentro de su volumen de trabajo. En la implementación, se desarrolla un algoritmo de reconocimiento y medición de posición, haciendo uso de software libre en lenguaje C++. De modo que, en la integración con el robot, el sistema pueda ser lo más abierto posible. La validación del trabajo se logra tomando muestras de los objetos a ser manipulados y generando trayectorias para el robot, a fin de visualizar si la pieza pudo ser captada por su garra neumática o no. Los resultados muestran que es posible lograr la manipulación de piezas en un ambiente visualmente cargado y con una precisión aceptable. Sin embargo, se observa que la precisión no permite que el sistema pueda ser usado en aplicaciones donde se requiere precisión al nivel de los procesos de ensamblado de piezas pequeñas o de soldadura.