Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Wearable monitoring system for assistance dogs

Despite the recent progresses in robotics, autonomous robots still have too many limitations to reliably help people with disabilities. On the other hand, animals, and especially dogs, have already demonstrated great skills in assisting people in many daily situations. However, dogs also have their own set of limitations. For example, they need to rest periodically, to be healthy (physically and psychologically), and it is difficult to control them remotely. This project aims to “augment” the Assistance dog, by developing a system that compensates some of the dog weaknesses through a robotic device mounted on the dog harness. This specific study, involved in the COCHISE project, focuses on the development of a system for the monitoring of dogs activity and physiological parameters.

The potential field method and the nonlinear attractor dynamics approach: what are the differences?

One of the most popular approaches to path planning and control is the potential field method. This method is particularly attractive because it is suitable for on-line feedback control. In this approach the gradient of a potential field is used to generate the robot's trajectory. Thus, the path is generated by the transient solutions of a dynamical system. On the other hand, in the nonlinear attractor dynamic approach the path is generated by a sequence of attractor solutions. This way the transient solutions of the potential field method are replaced by a sequence of attractor solutions (i.e., asymptotically stable states) of a dynamical system. We discuss at a theoretical level some of the main differences of these two approaches.

Vision-based hand Wheel-chair control

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

RoboCup Soccer Leagues

RoboCup was created in 1996 by a group of Japanese, American, and European Artificial Intelligence and Robotics researchers with a formidable, visionary long-term challenge: “By 2050 a team of robot soccer players will beat the human World Cup champion team.” At that time, in the mid 90s, when there were very few effective mobile robots and the Honda P2 humanoid robot was presented to a stunning public for the first time also in 1996, the RoboCup challenge, set as an adversarial game between teams of autonomous robots, was fascinating and exciting. RoboCup enthusiastically and concretely introduced three robot soccer leagues, namely “Simulation,” “Small-Size,” and “Middle-Size,” as we explain below, and organized its first competitions at IJCAI’97 in Nagoya with a surprising number of 100 participants [RC97]. It was the beginning of what became a continously growing research community. RoboCup established itself as a structured organization (the RoboCup Federation www.RoboCup.org). RoboCup fosters annual competition events, where the scientific challenges faced by the researchers are addressed in a setting that is attractive also to the general public. and the RoboCup events are the ones most popular and attended in the research fields of AI and Robotics.RoboCup further includes a technical symposium with contributions relevant to the RoboCup competitions and beyond to the general AI and robotics.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generating timed trajectories foran autonomous robot

Tese de Doutoramento Programa Doutoral em Engenharia Electrónica e Computadores

Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Review of robotic technology for keyhole transcranial stereotactic neurosurgery

The research of stereotactic apparatus to guide surgical devices began in 1908, yet a major part of today's stereotactic neurosurgeries still rely on stereotactic frames developed almost half a century ago. Robots excel at handling spatial information, and are, thus, obvious candidates in the guidance of instrumentation along precisely planned trajectories. In this review, we introduce the concept of stereotaxy and describe a standard stereotactic neurosurgery. Neurosurgeons' expectations and demands regarding the role of robots as assistive tools are also addressed. We list the most successful robotic systems developed specifically for or capable of executing stereotactic neurosurgery. A critical review is presented for each robotic system, emphasizing the differences between them and detailing positive features and drawbacks. An analysis of the listed robotic system features is also undertaken, in the context of robotic application in stereotactic neurosurgery. Finally, we discuss the current perspective, and future directions of a robotic technology in this field. All robotic systems follow a very similar and structured workflow despite the technical differences that set them apart. No system unequivocally stands out as an absolute best. The trend of technological progress is pointing toward the development of miniaturized cost-effective solutions with more intuitive interfaces.

Assistive locomotion strategies for active lower limb devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Definition of multibody system

This chapter presents a general view of multibody system concept and definition by describing the main features associated with spatial systems. The mechanical components, which can be modeled as rigid or flexible, are constrained by kinematic pair of different types. Additionally, the bodies can be actuated upon by force elements and external forces due to interaction with environment. This chapter also presents some examples of application of multibody systems that can include automotive vehicles, mechanisms, robots and biomechanical systems.

Manipulador Robótico

El uso de robots industriales junto con los sistemas de diseño asistidos por computadora (CAD), y los sistemas de fabricación asistidos por computadora (CAM), son la última tendencia en automatización de los procesos de fabricación. Aunque el crecimiento del mercado de la industria Robótica ha sido lento en comparación con los primeros años de la década de los 80´s, de acuerdo a algunas predicciones, la industria de la robótica está en su infancia. Ya sea que éstas predicciones se realicen completamente, o no, es claro que la industria robótica, en una forma o en otra, permanecerá. En la actualidad el uso de los robots industriales está concentrado en operaciones muy simples, como tareas repetitivas que no requieren tanta precisión. En los 80´s las tareas relativamente simples como las máquinas de inspección, transferencia de materiales, pintado automotriz, y soldadura son económicamente viables para ser robotizadas. Los análisis de mercado en cuanto a fabricación predicen que en ésta década y en las posteriores los robots industriales incrementarán su campo de aplicación, ésto debido a los avances tecnológicos en sensórica, los cuales permitirán tareas mas sofisticadas. El futuro es muy incierto y seguramente los ROBOTS tendrán mucho que ver en nuestra vida diaria en los próximos años y tenemos que lograr que la Argentina no solo este presente, en desarrollos que ameriten una publicación Internacional, si no también que puedan ser, en conjunción con la Universidad, la industria y aportes de capitales privados, diseñados, construidos, aplicados y comercializados según las necesidades de nuestro medio, desde el reemplazo de miembros humanos a personas con discapacidades, el alivio al hombre en tareas inhumanas y también en aplicaciones industriales aun no explotadas. Actualmente la investigación en el área de robótica en la Argentina, esta enfocada a problemas de electrónica y control. La mecánica no forma parte de dicho ambiente científico como puede verse en las 4 últimas Jornadas Argentinas de Robótica. En todos los casos se trabaja en la investigación de algoritmos o métodos de control montados en pequeños robots comerciales. El proyecto ROBOT-01 propone la construcción de un manipulador de 7 grados de libertad, aplicando las mas modernas técnicas de Simulación, diseño, CAD-3D, materiales compuestos, construcción de micro-mecánica, electrónica y software. El brazo manipulador estará diseñado para ser continuado o asociado con una mano robótica y con una base móvil autónoma, las que serán encaradas en proyectos futuros, o con interacción con otros grupos de investigación similares de otras Universidades