2D position system for a mobile robot in unstructured environments

Nowadays, several sensors and mechanisms are available to estimate a mobile robot trajectory and location with respect to its surroundings. Usually absolute positioning mechanisms are the most accurate, but they also are the most expensive ones, and require pre installed equipment in the environment. Therefore, a system capable of measuring its motion and location within the environment (relative positioning) has been a research goal since the beginning of autonomous vehicles. With the increasing of the computational performance, computer vision has become faster and, therefore, became possible to incorporate it in a mobile robot. In visual odometry feature based approaches, the model estimation requires absence of feature association outliers for an accurate motion. Outliers rejection is a delicate process considering there is always a trade-off between speed and reliability of the system. This dissertation proposes an indoor 2D position system using Visual Odometry. The mobile robot has a camera pointed to the ceiling, for image analysis. As requirements, the ceiling and the oor (where the robot moves) must be planes. In the literature, RANSAC is a widely used method for outlier rejection. However, it might be slow in critical circumstances. Therefore, it is proposed a new algorithm that accelerates RANSAC, maintaining its reliability. The algorithm, called FMBF, consists on comparing image texture patterns between pictures, preserving the most similar ones. There are several types of comparisons, with different computational cost and reliability. FMBF manages those comparisons in order to optimize the trade-off between speed and reliability.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

Teleoperation of a service robot using a mobile device

Teleoperation is a concept born with the rapid evolution of technology, with an intuitive meaning "operate at a distance." The first teleoperation system was created in the mid 1950s, which were handled chemicals. Remote controlled systems are present nowadays in various types of applications. This dissertation presents the development of a mobile application to perform the teleoperation of a mobile service robot. The application integrates a distributed surveillance (the result of a research project QREN) and led to the development of a communication interface between the robot (the result of another QREN project) and the vigilance system. It was necessary to specify a communication protocol between the two systems, which was implemented over a communication framework 0MQ (Zero Message Queue). For the testing, three prototype applications were developed before to perform the test on the robot.

Integration of a mobile autonomous robot in a surveillance multi-agent system

This dissertation aims to guarantee the integration of a mobile autonomous robot equipped with many sensors in a multi-agent distributed and georeferenced surveillance system. The integration of a mobile autonomous robot in this system leads to new features that will be available to clients of surveillance system may use. These features may be of two types: using the robot as an agent that will act in the environment or by using the robot as a mobile set of sensors. As an agent in the system, the robot can move to certain locations when alerts are received, in order to acknowledge the underlying events or take to action in order to assist in resolving this event. As a sensor platform in the system, it is possible to access information that is read from the sensors of the robot and access complementary measurements to the ones taken by other sensors in the multi-agent system. To integrate this mobile robot in an effective way it is necessary to extend the current multi-agent system architecture to make the connection between the two systems and to integrate the functionalities provided by the robot into the multi-agent system.

[Preface to] RoboCup 2007: Robot Soccer World Cup XI

[Excerpt] The 11th RoboCup International Symposium was held during July 9–10, 2007 at the Fox Theatre in Atlanta, GA, immediately after the 2007 Soccer, Rescue and Junior Competitions. The RoboCup community has observed an increasing interest from other communities over the past few years, e.g., the robotics community.RoboCupisseenasasignificantapproachtotheevaluationofnewlydeveloped methods to many difficult problems in robotics. Atlanta was also the location of a RoboCup@Space demonstration, which reflected the role of AI and robotics in space exploration. Prior to the symposium, space agencies had expressed an interest in cooperating with RoboCup. A first step in this direction was a successful demonstration at RoboCup 2007, which was accompanied with aninvitedtalkgivenbyaleadingscientistfromtheJapanAerospaceExploration Agency JAXA. [...]

A demonstration case on the derivation of process-level logical architectures for ambient assisted living ecosystems

When representing the requirements for an intended software solution during the development process, a logical architecture is a model that provides an organized vision of how functionalities behave regardless of the technologies to be implemented. If the logical architecture represents an ambient assisted living (AAL) ecosystem, such representation is a complex task due to the existence of interrelated multidomains, which, most of the time, results in incomplete and incoherent user requirements. In this chap- ter, we present the results obtained when applying process-level modeling techniques to the derivation of the logical architecture for a real industrial AAL project. We adopt a V-Model–based approach that expresses the AAL requirements in a process-level perspec- tive, instead of the traditional product-level view. Additionally, we ensure compliance of the derived logical architecture with the National Institute of Standards and Technology (NIST) reference architecture as nonfunctional requirements to support the implementa- tion of the AAL architecture in cloud contexts.

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.

Global vs. local nonlinear optimization techniques for human-like movement of an anthropomorphic robot

In this paper a comparison between using global and local optimization techniques for solving the problem of generating human-like arm and hand movements for an anthropomorphic dual arm robot is made. Although the objective function involved in each optimization problem is convex, there is no evidence that the admissible regions of these problems are convex sets. For the sequence of movements for which the numerical tests were done there were no significant differences between the optimal solutions obtained using the global and the local techniques. This suggests that the optimal solution obtained using the local solver is indeed a global solution.

Modelling and verifying smell-free architectures with the Archery language

Architectural (bad) smells are design decisions found in software architectures that degrade the ability of systems to evolve. This paper presents an approach to verify that a software architecture is smellfree using the Archery architectural description language. The language provides a core for modelling software architectures and an extension for specifying constraints. The approach consists in precisely specifying architectural smells as constraints, and then verifying that software architectures do not satisfy any of them. The constraint language is based on a propositional modal logic with recursion that includes: a converse operator for relations among architectural concepts, graded modalities for describing the cardinality in such relations, and nominals referencing architectural elements. Four architectural smells illustrate the approach.

Design of biped robot walking based on non-linear periodical oscillations

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2012

Design, modeling and control of an autonomous field robot for white asparagus harvesting

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2014

Robot trajectory optimization for relaxed effective tasks

Magdeburg, Univ., Fak. für Informatik, Diss., 2015

Design of biped robot walking with double-support phase

Otto-von-Guericke-Universität Magdeburg, Fakultät für Maschinenbau, Univ., Dissertation, 2015

Control remoto (web) de un robot Aibo

El robot Aibo disposa de la llibreria Aibo Remote Framework per controlar-lo remotament mitjançant un PC i una xarxa inalàmbrica, també per accedir a informació d'estat del robot, o per veure les imatges que l'Aibo capta. Combinant Remote Framework i php s'ha creat una aplicació web que permet controlar Aibos diferents remotament per Internet, així com tenir accés a les imatges subjectives de cadascun dels Aibos. A més, la tecnologia existent d'streaming permet que l'aplicació web tingui un vídeo incrustat que possibilita veure en directe els Aibos mitjançant una càmera web enfocada cap a ells.

Programación de un robot autónomo: diseño, construcción, programación e integración de un robot autónomo en el entorno Pyro

En aquest projecte, s'ha dissenyat, construït i programat un robot autònom, dotat de sistema de locomoció i sensors que li permeten navegar sense impactar en un entorn controlat. Per assolir aquests objectius s'ha dissenyat i programat una unitat de control que gestiona el hardware de baix volum de dades amb diferents modes d'operació, abstraient-lo en una única interfície. Posteriorment s'ha integrat aquest sistema en l'entorn de robòtica Pyro. Aquest entorn permet usar i adaptar, segons es necessiti, eines d'intel·ligència artificial ja desenvolupades.