In order to operate safely and naturally in human-centered environments, robots need to respond compliantly to force and contact interactions. While advanced robotic torsos and arms have been built that successfully achieve this, a somewhat neglected research area is the construction of compliant wheeled mobile bases. This thesis describes the mechatronics behind Trikey, a holonomic wheeled mobile base employing torque sensing at each of its three omni wheels so that it can detect and respond gracefully to force interactions. Trikey's mechanical design, kinematic and dynamic models, and control architecture are described, as well as simple experiments demonstrating compliant control. Trikey is designed to support a force-controlled humanoid upper body, and eventually, the two will be controlled together using whole-body control algorithms that utilize the external and internal dynamics of the entire system. / text

In the field of mobile robotics, many applications require feedback control laws that provide perfect path following. Previous work has shown that transverse feedback linearization is an effective approach to designing path following controllers that achieve perfect path following and path invariance. This thesis uses transverse feedback linearization and augments it with dynamic extension to present a framework for designing path following controllers for certain kinematic models of mobile robots. This approach can be used to design path following controllers for a large class of paths. While transverse feedback linearization makes the desired path attractive and invariant, dynamic extension allows the closed-loop system to achieve the desired motion along the path. In particular, dynamic extension can be used to make the mobile robot track a desired velocity or acceleration profile while moving along a path.

Construir um robô capaz de realizar tarefas sem qualquer interferência humana é um dos maiores desafios da Robótica Move!. Dispondo apenas de sensores, um robô autônomo precisa explorar ambientes desconhecidos e, simultaneamente, construir um mapa confiável a fim de se localizar e realizar a tarefa. Na presença de erros de odometria, o robô não consegue se auto-localizar corretamente em seu mapa interno e acaba por construir um mapa deformado e não condizente com a realidade. Este trabalho apresenta uma solução para o problema da auto-localização de robô moveis autônomos. Esta solução faz use de um método linear de calculo de estimativas chamado Filtro de Kalman para corrigir a posição do robô em seu mapa intern° do ambiente enquanto realiza a exploração. A proposta leva em consideração que toda entidade que se movimenta em um ambiente conta sempre com alguns pontos de referencia para se localizar. Estes pontos são implementados como objetos especiais chamados marcas de Kalman. Em simulação, o reconhecimento das marcas pode ser feito de duas maneiras: através de sua posição no mapa ou através de sua identidade. Nos experimentos realizados em simulação, o método é testado para diferentes erros no angulo de orientação do robô. Os resultados são comparados levando em consideração as deformações no mapa gerado, com e sem marcas de Kalman, e o erro médio da posição do robô durante todo o processo exploratório. / Build a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.

Faculty of Engineering in collaboration with the National Agricultural and Livestock Investigation Institute is developing a low cost platform capable of dealing with challenges imposed by agricultural activities, specifically, fruit transportation inside fields. In this context, the consequent dissertation focuses on the development of a dynamic model of an all terrain articulated autonomous vehicle to be applied in the aforementioned platform. The study includes the kinematic and dynamic analysis of the vehicle. Once those models are deducted, they are put together against the ones obtained for a biarticulated robot arm without ground fixation. To sum up, results will be adapted for computational simulation which was done via Gazebo, an ambience of the robot operating system ROS. Through this simulations it was possible to determine the model validity for the autonomous operating robotic platform. / Facultad de Ingeniería en conjunto con el Instituto Nacional de Investigación Agropecuaria desarrolla una plataforma móvil de bajo costo capaz de enfrentar los retos impuestos por la actividad agrícola, más específicamente, asistencia en el transporte de fruta. Es en este contexto que el presente trabajo se enfoca en desarrollar un modelo dinámico de un vehículo autónomo articulado todoterreno para ser aplicado en la mencionada plataforma. El estudio comprende el análisis de la cinemática y dinámica del vehículo. Una vez obtenidos los modelos cinemáticos y dinámicos, se compara los mismos con los obtenidos para un brazo biarticulado sin vínculo a tierra. Finalmente se adaptan los resultados para simulación computacional la cual fue realizada utilizando el ambiente Gazebo del sistema operativo robótico ROS. A través de esta simulación se pudo comprobar la validez del modelo desarrollado para la plataforma robótica autónoma en operación.

Construir um robô capaz de realizar tarefas sem qualquer interferência humana é um dos maiores desafios da Robótica Move!. Dispondo apenas de sensores, um robô autônomo precisa explorar ambientes desconhecidos e, simultaneamente, construir um mapa confiável a fim de se localizar e realizar a tarefa. Na presença de erros de odometria, o robô não consegue se auto-localizar corretamente em seu mapa interno e acaba por construir um mapa deformado e não condizente com a realidade. Este trabalho apresenta uma solução para o problema da auto-localização de robô moveis autônomos. Esta solução faz use de um método linear de calculo de estimativas chamado Filtro de Kalman para corrigir a posição do robô em seu mapa intern° do ambiente enquanto realiza a exploração. A proposta leva em consideração que toda entidade que se movimenta em um ambiente conta sempre com alguns pontos de referencia para se localizar. Estes pontos são implementados como objetos especiais chamados marcas de Kalman. Em simulação, o reconhecimento das marcas pode ser feito de duas maneiras: através de sua posição no mapa ou através de sua identidade. Nos experimentos realizados em simulação, o método é testado para diferentes erros no angulo de orientação do robô. Os resultados são comparados levando em consideração as deformações no mapa gerado, com e sem marcas de Kalman, e o erro médio da posição do robô durante todo o processo exploratório. / Build a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.

Made available in DSpace on 2014-12-17T14:56:01Z (GMT). No. of bitstreams: 1 ClauberGB.pdf: 726956 bytes, checksum: d3fb1b2d7c6ad784a1b7d40c1a54f8f8 (MD5) Previous issue date: 2004-03-08 / Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark / Diversos m?todos de navega??o de rob?s m?veis requerem a medi??o da posi??o e orienta??o do rob? no seu espa?o de trabalho. No caso de rob?s m?veis com rodas, t?cnicas baseadas em odometria permitem determinar a localiza??o do rob? atrav?s da integra??o de medi??es dos deslocamentos incrementais de suas rodas. No entanto, essa t?cnica est? sujeita a erros que se acumulam com a dist?ncia percorrida pelo rob?, o que inviabiliza o seu uso exclusivo. Outros m?todos se baseiam na detec??o de marcos naturais ou artificiais, cuja localiza??o ? conhecida, presentes no ambiente. Apesar desta t?cnica n?o gerar erros cumulativos, ela pode requisitar um tempo de processamento bem maior do que o uso de odometria. Assim, muitos m?todos fazem uso de ambas as t?cnicas, de modo a corrigir periodicamente os erros de odometria, atrav?s de medi??es obtidas a partir dos marcos. De acordo com esta abordagem, propomos neste trabalho um sistema h?brido de localiza??o para rob?s m?veis com rodas em ambientes internos, baseado em odometria e marcos naturais, onde os marcos adotados s?o linhas retas definidas pelas jun??es existentes no piso do ambiente, formando uma grade bi-dimensional no ch?o. Para a detec??o deste tipo de marco, a partir de imagens digitais, ? utilizada a transformada de Hough, associada a heur?sticas que permitem a sua aplica??o em tempo real. Em particular, para reduzir o tempo de busca dos marcos, propomos mapear erros de odometria em uma regi?o da imagem capturada que possua grande probabilidade de conter o marco procurado

Faculty of Engineering in collaboration with the National Agricultural and Livestock Investigation Institute is developing a low cost platform capable of dealing with challenges imposed by agricultural activities, specifically, fruit transportation inside fields. In this context, the consequent dissertation focuses on the development of a dynamic model of an all terrain articulated autonomous vehicle to be applied in the aforementioned platform. The study includes the kinematic and dynamic analysis of the vehicle. Once those models are deducted, they are put together against the ones obtained for a biarticulated robot arm without ground fixation. To sum up, results will be adapted for computational simulation which was done via Gazebo, an ambience of the robot operating system ROS. Through this simulations it was possible to determine the model validity for the autonomous operating robotic platform. / Facultad de Ingeniería en conjunto con el Instituto Nacional de Investigación Agropecuaria desarrolla una plataforma móvil de bajo costo capaz de enfrentar los retos impuestos por la actividad agrícola, más específicamente, asistencia en el transporte de fruta. Es en este contexto que el presente trabajo se enfoca en desarrollar un modelo dinámico de un vehículo autónomo articulado todoterreno para ser aplicado en la mencionada plataforma. El estudio comprende el análisis de la cinemática y dinámica del vehículo. Una vez obtenidos los modelos cinemáticos y dinámicos, se compara los mismos con los obtenidos para un brazo biarticulado sin vínculo a tierra. Finalmente se adaptan los resultados para simulación computacional la cual fue realizada utilizando el ambiente Gazebo del sistema operativo robótico ROS. A través de esta simulación se pudo comprobar la validez del modelo desarrollado para la plataforma robótica autónoma en operación.

Construir um robô capaz de realizar tarefas sem qualquer interferência humana é um dos maiores desafios da Robótica Move!. Dispondo apenas de sensores, um robô autônomo precisa explorar ambientes desconhecidos e, simultaneamente, construir um mapa confiável a fim de se localizar e realizar a tarefa. Na presença de erros de odometria, o robô não consegue se auto-localizar corretamente em seu mapa interno e acaba por construir um mapa deformado e não condizente com a realidade. Este trabalho apresenta uma solução para o problema da auto-localização de robô moveis autônomos. Esta solução faz use de um método linear de calculo de estimativas chamado Filtro de Kalman para corrigir a posição do robô em seu mapa intern° do ambiente enquanto realiza a exploração. A proposta leva em consideração que toda entidade que se movimenta em um ambiente conta sempre com alguns pontos de referencia para se localizar. Estes pontos são implementados como objetos especiais chamados marcas de Kalman. Em simulação, o reconhecimento das marcas pode ser feito de duas maneiras: através de sua posição no mapa ou através de sua identidade. Nos experimentos realizados em simulação, o método é testado para diferentes erros no angulo de orientação do robô. Os resultados são comparados levando em consideração as deformações no mapa gerado, com e sem marcas de Kalman, e o erro médio da posição do robô durante todo o processo exploratório. / Build a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.

Context: Effective vision processing is an important study area for mobile robots which use vision to detect objects. The problem of detecting small sized coloured objects (e.g. Lego bricks) with no texture information can be solved using either colour or contours of the objects. The shape of such objects doesn‟t help much in detecting the objects due to the poor quality of the picture and small size of the object in the image. In such cases it is seen that the use of hybrid techniques can benefit the overall detection of objects, especially, combining keypoint based methods with the colour based techniques. Robotic motion also plays a vital role in the completion of autonomous tasks. Mobile robots have different configurations for locomotion. The most important system is differential steering because of its application in sensitive areas like military tanks and security robot platforms. The kinematic design of a robotic platform is usually based on the number of wheels and their movement. There can be several configurations of wheels designs, for example differential drives, car-like designs, omni-directional, and synchro drives. Differential drive systems use speed on individual channels to determine the combined speed and trajectory of the robot. Accurate movement of the robot is very important for correct completion of its activities. Objectives: A vision solution is developed that is capable of detecting small sized colour objects in the environment. This has also been compared with other shape detection techniques for performance evaluation. The effect of distance on detection is also investigated for the participating techniques. The precise motion of a four-wheel differential drive system is investigated. The target robot platform uses a differential drive steering system and the main focus of this study is accurate position and orientation control based upon sensor data. Methods: For object detection, a novel hybrid method „HistSURF‟ is proposed and is compared with other vision processing techniques. This method combines the results of colour histogram comparison and detection by the SURF algorithm. A solution for differential steering using a Gyro for the rotational speed measurement is compared with a solution using a speed model and control outputs without feedback (i.e. dead reckoning). Results: The results from the vision experiment rank the new proposed method highest among the other participating techniques. The distance experiment indicates that there is a direct and inverse relation between the distance and detected SURF features. It is also indicated by the results that distance affects the detection rate of the new proposed technique. In case of robot control, the differential drive solution using a speed model has less error rate than the one that uses a Gyro for angle measurement. It is also clear from the results that the greater the difference of speeds among the channels the less smooth is the angular movement. Conclusions: The results indicate that by combining a key-point based technique with colour segmentation, the false positive rate can be reduced and hence object recognition performance improves . It has also become clear that the improved accuracy of the proposed technique is limited to small distances and its performance decreases rapidly with increase in the distance to target objects. For robot control, the results indicate that a Gyro alone cannot improve the movement accuracy of the robotic system due to a variable drift exhibited by the Gyro while in rotation. However, a Gyro can be effective if used in combination with a magnetometer and some form of estimation mechanism like a Kalman filter. A Kalman filter can be used to correct the error in the Gyro by using the output from the magnetometer, resulting in a good estimate. / Bakgrund: Effektiv vision behandling är ett viktigt studieområde för mobila robotar som använder vision att upptäcka föremål. Problemet upptäcka små och medelstora färgade föremål (t.ex. Lego tegelstenar) utan konsistens information kan lösas med färg eller konturer av föremålen. Formen på sådana föremål spelar ingen hjälpa mycket att upptäcka föremål på grund av den dåliga kvaliteten på bild och ringa storlek på objektet i bilden. I sådana fall är det sett att användningen av hybrid-teknik kan gynna den totala upptäckt av föremål, särskilt genom att kombinera keypoint metoder med färgen tekniker. Robotic motion spelar också en viktig roll i genomförandet av självständiga uppgifter. Mobila robotar har olika konfigurationer för transport. Det viktigaste är differentierad styrning på grund av dess tillämpning i känsliga områden som stridsvagnar och säkerhet plattformar robot. Den kinematiska utformningen av en robot plattform är vanligtvis baserad på antalet hjul och deras rörelser. Det kan finnas flera konfigurationer av hjul mönster, till exempel olika enheter, bil-liknande mönster, rundstrålande, och driver synkroniserad. Differential drivsystem använder fart om olika kanaler för att bestämma den kombinerade snabbhet och banan för roboten. Exakt förflyttning av roboten är mycket viktigt för korrekt ifyllande av sin verksamhet. Mål: En vision lösning har utvecklats som kan upptäcka små och medelstora färg objekt i miljön. Detta har också jämfört med andra tekniker form upptäcka för utvärdering av prestanda. Effekten av avstånd vid upptäckt är också undersökas för de deltagande tekniker. Den exakta rörelse av en fyrhjulsdriven olika drivsystem undersöks. Målet robot plattform använder en differentierad system driva styrning och i centrum för denna studie är korrekt läge och riktning kontroll baserat på sensordata. Metoder: För att upptäcka, en ny hybrid metod "HistSURF" föreslås och jämförs med andra tekniker vision bearbetning. Denna metod kombinerar resultaten av färg histogram jämförelse och upptäckt av SURF algoritm. En lösning för differentierad styrning med hjälp av en Gyro för varvtal mätningen jämförs med en lösning med en hastighet modell och utgångar kontroll utan återkoppling (dvs död räkning). Resultat: Resultaten från den vision experiment inom den nya föreslagna metoden högsta bland de andra deltagande tekniker. Avståndet experiment indikerar att det finns ett direkt och omvänd korrelation mellan avstånd och upptäckt SURF funktioner. Det är också framgå av resultatet från det avståndet påverkar upptäckten hastighet av den nya föreslagna tekniken. Vid robot kontroll har skillnaden köra lösningen med en hastighet modell mindre felfrekvens än den som använder en Gyro för vinkelmätning. Det framgår även av resultaten att ju större skillnaden i hastigheter mellan de kanaler de mindre smidiga är vinkelrörelse. Slutsatser: Resultaten visar att genom att kombinera en central-punkt baserad teknik med färg segmentering, den falska positiva kan sänkas och därmed objektigenkänning prestanda ökar. Det har också blivit uppenbart att förbättrad noggrannhet av den föreslagna tekniken är begränsad till små avstånd och dess prestanda minskar snabbt med ökat avstånd till målet objekt. För robot kontroll, tyder resultaten på att en Gyro inte ensam kan förbättra rörligheten noggrannhet robotsystem på grund av en variabel glida ut av Gyro medan rotation. Men en Gyro kan vara effektiva om de används i kombination med en magnetometer och någon form av uppskattning mekanism som ett Kalman filter. En Kalman filter kan användas för att rätta till felet i Gyro med hjälp av utdata från magnetometer, vilket resulterar i en god uppskattning.

L’autonomie d’un agent mobile se définit par sa capacité à naviguer dans un environnement sans intervention humaine. Cette tâche s’avère très demandée pour les robots d’assistance à la personne. C’est pour cela que notre contribution s’est portée en particulier sur l’instrumentation et l’augmentation de l’autonomie d’un fauteuil roulant pour les personnes à mobilité réduite. L’objectif de ce travail est de concevoir des lois de commande qui permettent à un robot de naviguer en temps réel et en toute autonomie dans un environnement inconnu. Un cadre de perception virtuelle unifié est introduit et permet de projeter l’espace navigable obtenu par des observations éventuellement multiples. Une approche de navigation autonome et sûre a été conçue pour se déplacer dans un environnement peu encombré dont la structure peut être assimilée à un couloir (lignes au sol, murs, délimitation herbes, routes...). La problématique a été résolue en utilisant le formalisme de l’asservissement visuel. Les caractéristiques visuelles utilisées dans la loi de commande ont été construites à partir de la représentation virtuelle (à savoir la position du point de fuite et l’orientation de la ligne médiane du couloir). Pour assurer une navigation sûre et lisse, même lorsque ces paramètres ne peuvent pas être extraits, nous avons conçu un observateur d’état pour estimer les caractéristiques visuelles dans le but de maintenir la commande fonctionnelle du robot. Cette approche permet de faire naviguer un robot mobile dans un couloir même en cas de défaillance sensorielle (données non fiables) et/ou de perte de mesure. La première contribution de cette thèse a été étendue en traitant tout type d’environnement encombré statique ou dynamique. Cela a été réalisé en utilisant le diagramme de Voronoï. Le diagramme de Voronoï généralisé, également appelé squelette, est une représentation puissante de l’environnement. Il définit un ensemble de chemins à la distance maximale des obstacles. Dans ce travail, une approche d’asservissement visuel basée sur le squelette extrait en temps réel était proposée pour une navigation autonome et sûre des robots mobiles. La commande est basée sur une approximation du DVG local en utilisant le Delta Medial axis, un algorithme de squelettisation rapide et robuste. Ce dernier produit un squelette filtré de l’espace libre entourant le robot en utilisant un paramètre qui prend en compte la taille du robot. Cette approche peut faire face aux bruits de mesure au niveau de la perception et au niveau de la commande à cause des glissement des roues. C’est pour cela que nous avons conçu une approche d’asservissement visuel sur une prédiction d’une linéarisation du DVG. Une analyse complète a été réalisée pour montrer la stabilité des lois de commandes proposées. Des simulations et des tests expérimentaux valident l’approche proposée. / The autonomy of a mobile agent is defined by its ability to navigate in an environment without human intervention. This task is very required for personal assistance robots. That’s why our contribution has been particularly focused on instrumentation and increasing the autonomy of a wheelchair for reduced mobility peaple. The objective of this work is to design control laws that allow a robot to navigate in real time and independently in an unknown environment. A unified virtual perception framework is introduced and allows to project the navigable space obtained by possibly multiple observations. First we designed an autonomous and safe navigation approach in environment whose structure can be assimilated to a corridor (lines on the ground, walls, delimitation of grasses, roads ...). We have solved this problem by using the formalism of visual servoing. The visual characteristics used in the control law were constructed from the virtual representation (ie the position of the vanishing point and the orientation of the center line of the corridor). To ensure safe and smooth navigation, even when these parameters can not be extracted, we have designed a finite-time state observer to estimate the visual characteristics in order to maintain the robot’s control efficient. This approach let a mobile robot navigate in a corridor even in in the case of sensory failure (unreliable data) and/or loss of measurement. We have extended the first contribution of this work with dealing with any type of static or dynamic environment. This was done using the Voronoi diagram. The Generalized Voronoi Diagram (GVD), also named skeleton, is a powerful environment representation, since, among other reasons, it defines a set of paths at maximal distance from the obstacles. In this work, a real time skeleton based visual servoing approach is proposed for a safe autonomous navigation of mobile robots. The control is based on an approximation of the local GVD using the Delta Medial Axis, a fast and robust skeletonization algorithm. The latter produces a filtered skeleton of the free space surrounding the robot using a pruning parameter that takes into account the robot size. This approach can cope with measurement noises at the perception and control with the wheel slip. This is why we have designed a visual servoing approach on a prediction of a GVD linearization. A complete analysis was performed to show the stability of the proposed control laws. Simulations and experimental tests validate the proposed approach.