Web 2.0 Technologies for Education

Page 1. Web 2.0 Technologies for Education G. Santhosh Kumar Dept. Of Computer Science Cochin University Page 2. What is Internet? CUSAT is linked to this Web through 10 Mbps leased line connectivity Page 3. Size of the Web? GYWA = Sorted on Google, Yahoo!, Windows Live Search (Msn Search) and Ask YGWA = Sorted on Yahoo!, Google, Windows Live Search (Msn Search) and Ask www.worldwidewebsize.com Page 4. The Machine is Us/ing Us ■ http://in.youtube.com/watch?v=NLlGopyXT_g&feature=channel Page 5. ..

Heterogeneous Multi-Robot Cooperation

This report addresses the problem of achieving cooperation within small- to medium- sized teams of heterogeneous mobile robots. I describe a software architecture I have developed, called ALLIANCE, that facilitates robust, fault tolerant, reliable, and adaptive cooperative control. In addition, an extended version of ALLIANCE, called L-ALLIANCE, is described, which incorporates a dynamic parameter update mechanism that allows teams of mobile robots to improve the efficiency of their mission performance through learning. A number of experimental results of implementing these architectures on both physical and simulated mobile robot teams are described. In addition, this report presents the results of studies of a number of issues in mobile robot cooperation, including fault tolerant cooperative control, adaptive action selection, distributed control, robot awareness of team member actions, improving efficiency through learning, inter-robot communication, action recognition, and local versus global control.

Disseny i construcció d’ un braç de robot de 5 graus de llibertat governat des de PC via USB.

El braç robot es va crear com a resposta a una necessitat de fabricació d’elements mitjançant la producció en cadena i en tasques que necessiten precisió. Hi ha, però, altres tipus de tasques les quals no són repetitives, ni poden ésser programades, que necessiten però ser controlades en tot moment per un ésser humà. Són activitats que han d’estar realitzades per un ésser humà, però que requereixen molta precisió, és per això que es creu necessari el disseny d’un prototipus de control d’un braç robot estàndard, que permeti a una persona el control total sobre aquest en temps real per a la realització d’una tasca no repetitiva i no programable prèviament. Pretenem, en el present projecte, dissenyar i construir un braç robot de 5 graus de llibertat, controlat des d’un PC mitjançant un microcontrolador PIC amb comunicació a través d’un bus USB. El robot serà governat des d’un PC a través d’un software de control específic

Millora experimental de les lleis de control predictiu aplicades sobre la plataforma PRIM I, per el seguiment de trajectòries

Aquest projecte s’aplica sobre el robot PRIM (Plataforma Robotitzada d’Informació Multimèdia), un robot autònom no humanoide creat el 2004 per Ateneu Informàtic (AI) que permet realitzar trajectòries 2D gràcies a un sistema de tracció format per dues rodes motrius propulsades independentment. La plataforma PRIM és controlada a partir del control predictiu, aquest control es va implementar en un projecte anterior, creat per l’Alexandre Blasco Gutierrez i titulat “Implementació de tècniques MPC (Model Predictiu Control) sobre la plataforma PRIM I”. El que es pretén en aquest projecte és millorar els resultats obtinguts en el passat projecte reformulant la llei de control i analitzar les discrepàncies obtingudes en les metodologies que s’utilitzen per minimitzar la funció de costos a partir de simulacions de trajectòries

Odometria i planificació de trajectòries amb el robot e-puck

En el laboratori docent de robòtica s'utilitzen robots mòbils autònoms per treballar aspectes relacionats amb el posicionament, el control de trajectòries, la construcció de mapes... Es disposa de cinc robots comercials anomenats “e-puck”, que es caracteritzen per les seves dimensions reduïdes, dos motors i un conjunt complet de sensors. Aquests robots es programen en C++ utilitzant el simulador Webots, que disposa d'un conjunt de llibreries per programar el robot. També es disposa d'un entorn de proves on els robots es poden moure i evitar obstacles. Donat el poc temps que disposen els estudiants que realitzen pràctiques en aquest laboratori, és d'interès desenvolupar un software que contingui ja el posicionament del robot mitjançant odometria i també varis algoritmes de control de trajectòries. Per últim, en el laboratori es disposa de càmeres i targes d'adquisició de dades. Així doncs els objectius que s'han proposat per el projecte són: 1. Estudi de la documentació i software proporcinats pels fabricants del robot i de l'entorn Webots; 2. Programació del software de l'odometria i realització de proves per comprovar-ne la precisió; 3. Disseny, programació i verificació del software dels algoritmes de planificació de trajectòries. Realització d'experiments per a comprovar-ne el funcionament i 4. Disseny, programació i verificació d'un sistema de visió artificial que permeti conèixer la posició absoluta del robot en l'entorn

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Mission control system for dam inspection with an AUV

This paper presents a complete control architecture that has been designed to fulfill predefined missions with an autonomous underwater vehicle (AUV). The control architecture has three levels of control: mission level, task level and vehicle level. The novelty of the work resides in the mission level, which is built with a Petri network that defines the sequence of tasks that are executed depending on the unpredictable situations that may occur. The task control system is composed of a set of active behaviours and a coordinator that selects the most appropriate vehicle action at each moment. The paper focuses on the design of the mission controller and its interaction with the task controller. Simulations, inspired on an industrial underwater inspection of a dam grate, show the effectiveness of the control architecture

Using petri nets to specify and execute missions for autonomous underwater vehicles

This paper presents the design and implementation of a mission control system (MCS) for an autonomous underwater vehicle (AUV) based on Petri nets. In the proposed approach the Petri nets are used to specify as well as to execute the desired autonomous vehicle mission. The mission is easily described using an imperative programming language called mission control language (MCL) that formally describes the mission execution thread. A mission control language compiler (MCL-C) able to automatically translate the MCL into a Petri net is described and a real-time Petri net player that allows to execute the resulting Petri net onboard an AUV are also presented

Identification and Semiactive Control of Smart Structures Equipped with Magnetorheological Actuators

This paper deals with the problem of identification and semiactive control of smart structures subject to unknown external disturbances such as earthquake, wind, etc. The experimental setup used is a 6-story test structure equipped with shear-mode semiactive magnetorheological actuators being installed in WUSCEEL. The experimental results obtained have verified the effectiveness of the proposed control algorithms

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced.

Rhythmic behavior of social insects from single to multibody

Revealing the evolution of well-organized social behavior requires understanding a mechanism by which collective behavior is produced. A well-organized group may be produced by two possible mechanisms, namely, a central control and a distributed control. In the second case, local interactions between interchangeable components function at the bottom of the collective behavior. We focused on a simple behavior of an individual ant and analyzed the interactions between a pair of ants. In an experimental set-up, we placed the workers in a hemisphere without a nest, food, and a queen, and recorded their trajectories. The temporal pattern of velocity of each ant was obtained. From this bottom-up approach, we found the characteristic behavior of a single worker and a pair of workers as follows: (1) Activity of each individual has a rhythmic component. (2) Interactions between a pair of individuals result in two types of coupling, namely the anti-phase and the in-phase coupling. The direct physical contacts between the pair of workers might cause a phase shift of the rhythmic components in individual ants. We also build up a simple model based on the coupled oscillators toward the understanding of the whole colony behavior.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.