Atividades com robótica educacional para as aulas de matemática do 6. ao 9. ano do ensino fundamental: utilização da metodologia LEGO® Zoom Education

Pós-graduação em Matemática - IBILCE

Um modelo cognitivo baseado na atenção para consciência em robôs inteligentes

Understanding consciousness is one of the most fascinating challenges of our time. From ancient civilizations to modern philosophers, questions have been asked on how one is conscious of his/her own existence and about the world that surrounds him/her. Although there is no precise definition for consciousness, there is an agreement that it is strongly related to human cognitive processes such as: thinking, reasoning, emotions, wishes. One of the key processes to the arising of the consciousness is the attention, a process capable of promoting a selection of a few stimuli from a huge amount of information that reaches us constantly. Machine consciousness is the field of the artificial intelligence that investigate the possibility of the production of conscious processes in artificial devices. This work presents a review about the theme of consciousness - in both natural and artificial aspects -, discussing this theme from the philosophical and computational perspectives, and investigates the feasibility of the adoption of an attentional schema as the base to the cognitive processing. A formal computational model is proposed for conscious agents that integrates: short and long term memories, reasoning, planning, emotion, decision making, learning, motivation and volition. Computer experiments in a mobile robotics domain under USARSim simulation environment, proposed by RoboCup, suggest that the agent can be able to use these elements to acquire experiences based on environment stimuli. The adoption of the cognitive architecture over the attentional model has potential to allow the emergence of behaviours usually associated to the consciousness in the simulated mobile robots. Further implementation under this model could potentially allow the agent to express sentience, selfawareness, self-consciousness, autonoetic consciousness, mineness and perspectivalness. By performing computation over an attentional space, the model also allows the ...

Algoritmo neurogenético com vistas para o planejamento de rotas de robôs móveis autônomos

Pós-graduação em Ciência da Computação - IBILCE

On Modular Design of Field Robotic Systems

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task. This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels. The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search. The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.

DESIGN AND ANALYSIS OF MULTIFUNCTIONAL ROBOT FOR NOTES

The elimination of all external incisions is an important step in reducing the invasiveness of surgical procedures. Natural Orifice Translumenal Endoscopic Surgery (NOTES) is an incision-less surgery and provides explicit benefits such as reducing patient trauma and shortening recovery time. However, technological difficulties impede the widespread utilization of the NOTES method. A novel robotic tool has been developed, which makes NOTES procedures feasible by using multiple interchangeable tool tips. The robotic tool has the capability of entering the body cavity through an orifice or a single incision using a flexible articulated positioning mechanism and once inserted is not constrained by incisions, allowing for visualization and manipulations throughout the cavity. Multiple interchangeable tool tips of the robotic device initially consist of three end effectors: a grasper, scissors, and an atraumatic Babcock clamp. The tool changer is capable of selecting and switching between the three tools depending on the surgical task using a miniature mechanism driven by micro-motors. The robotic tool is remotely controlled through a joystick and computer interface. In this thesis, the following aspects of this robotic tool will be detailed. The first-generation robot is designed as a conceptual model for implementing a novel mechanism of switching, advancing, and controlling the tool tips using two micro-motors. It is believed that this mechanism achieves a reduction in cumbersome instrument exchanges and can reduce overall procedure time and the risk of inadvertent tissue trauma during exchanges with a natural orifice approach. Also, placing actuators directly at the surgical site enables the robot to generate sufficient force to operate effectively. Mounting the multifunctional robot on the distal end of an articulating tube provides freedom from restriction on the robot kinematics and helps solve some of the difficulties otherwise faced during surgery using NOTES or related approaches. The second-generation multifunctional robot is then introduced in which the overall size is reduced and two arms provide 2 additional degrees of freedom, resulting in feasibility of insertion through the esophagus and increased dexterity. Improvements are necessary in future iterations of the multifunctional robot; however, the work presented is a proof of concept for NOTES robots capable of abdominal surgical interventions.

Tap-Off Power From the Overhead Shield Wires of an HV Transmission Line

In recent years, there was an increase of ancillary service loads, such as signaling systems, inspection robots, surveillance cameras, and other monitoring devices distributed along high-voltage transmission lines which require low-power dc voltage supplies. This paper investigates the use of the induced voltage in the shield wires of an overhead 525 kV transmission line as a primary power source. Since phase current variations throughout the day affect the induced voltage in the overhead ground wire, a step-down dc-dc converter is used after rectification of the ac voltage to provide a regulated dc output voltage. The initial encouraging results obtained indicate that this form of power supply can be a feasible and cost-effective alternative for feeding small ancillary service loads. The simulation results are validated by field measurements as well as the installation of a 200 W voltage stabilization system prototype.

In-Flight Collision Avoidance Controller Based Only on OS4 Embedded Sensors

The major goal of this research was the development and implementation of a control system able to avoid collisions during the flight for a mini-quadrotor helicopter, based only on its embedded sensors without changing the environment. However, it is important to highlight that the design aspects must be seriously considered in order to overcome hardware limitations and achieve control simplification. The controllers of a UAV (Unmanned Aerial Vehicle) robot deal with highly unstable dynamics and strong axes coupling. Furthermore, any additional embedded sensor increases the robot total weight and therefore, decreases its operating time. The best balance between embedded electronics and robot operating time is desired. This paper focuses not only on the development and implementation of a collision avoidance controller for a mini-robotic helicopter using only its embedded sensors, but also on the mathematical model that was essential for the controller developing phases. Based on this model we carried out the development of a simulation tool based on MatLab/Simulink that was fundamental for setting the controllers' parameters. This tool allowed us to simulate and improve the OS4 controllers in different modeled environments and test different approaches. After that, the controllers were embedded in the real robot and the results proved to be very robust and feasible. In addition to this, the controller has the advantage of being compatible with future path planners that we are developing.

Detection of gait perturbations based on proprioceptive information. Application to Limit Cycle Walkers

Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans': Neither trajectory controlled robots, nor dynamic walking machines (Limit CycleWalkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

Reasoning about shadows in a mobile robot environment

This paper describes a logic-based formalism for qualitative spatial reasoning with cast shadows (Perceptual Qualitative Relations on Shadows, or PQRS) and presents results of a mobile robot qualitative self-localisation experiment using this formalism. Shadow detection was accomplished by mapping the images from the robot’s monocular colour camera into a HSV colour space and then thresholding on the V dimension. We present results of selflocalisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other, the threshold is set according to a prediction about the robot’s location, based upon a qualitative spatial reasoning theory about shadows. This theory-driven threshold search and the qualitative self-localisation procedure are the main contributions of the present research. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform robot self-localisation and to calibrate a robot’s interpretation of its perceptual input.

Taller del proyecto AVORA, robot submarino de la ULPGC-PLOCAN dentro de Ciberlandia 2013

Celebrado el 24 de mayo en el Edificio de Informática y Matemáticas de la ULPGC

A-TIRMA: A small autonomous sailboat

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

On the use of a low-cost thermal sensor to improve kinect people detection in a mobile robot

[EN]Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform.

Automatic initialization for facial analysis in interactive robotics

[EN]This paper focuses on four different initialization methods for determining the initial shape for the AAM algorithm and their particular performance in two different classification tasks with respect to either the facial expression DaFEx database and to the real world data obtained from a robot’s point of view.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Coordinated Control of Robotic Swarms in Unknown Environments

This thesis gathers the work carried out by the author in the last three years of research and it concerns the study and implementation of algorithms to coordinate and control a swarm of mobile robots moving in unknown environments. In particular, the author's attention is focused on two different approaches in order to solve two different problems. The first algorithm considered in this work deals with the possibility of decomposing a main complex task in many simple subtasks by exploiting the decentralized implementation of the so called \emph{Null Space Behavioral} paradigm. This approach to the problem of merging different subtasks with assigned priority is slightly modified in order to handle critical situations that can be detected when robots are moving through an unknown environment. In fact, issues can occur when one or more robots got stuck in local minima: a smart strategy to avoid deadlock situations is provided by the author and the algorithm is validated by simulative analysis. The second problem deals with the use of concepts borrowed from \emph{graph theory} to control a group differential wheel robots by exploiting the Laplacian solution of the consensus problem. Constraints on the swarm communication topology have been introduced by the use of a range and bearing platform developed at the Distributed Intelligent Systems and Algorithms Laboratory (DISAL), EPFL (Lausanne, CH) where part of author's work has been carried out. The control algorithm is validated by demonstration and simulation analysis and, later, is performed by a team of four robots engaged in a formation mission. To conclude, the capabilities of the algorithm based on the local solution of the consensus problem for differential wheel robots are demonstrated with an application scenario, where nine robots are engaged in a hunting task.