Probabilistic mapping with mobile robots in structured environments

La mayor parte de los entornos diseñados por el hombre presentan características geométricas específicas. En ellos es frecuente encontrar formas poligonales, rectangulares, circulares . . . con una serie de relaciones típicas entre distintos elementos del entorno. Introducir este tipo de conocimiento en el proceso de construcción de mapas de un robot móvil puede mejorar notablemente la calidad y la precisión de los mapas resultantes. También puede hacerlos más útiles de cara a un razonamiento de más alto nivel. Cuando la construcción de mapas se formula en un marco probabilístico Bayesiano, una especificación completa del problema requiere considerar cierta información a priori sobre el tipo de entorno. El conocimiento previo puede aplicarse de varias maneras, en esta tesis se presentan dos marcos diferentes: uno basado en el uso de primitivas geométricas y otro que emplea un método de representación cercano al espacio de las medidas brutas. Un enfoque basado en características geométricas supone implícitamente imponer un cierto modelo a priori para el entorno. En este sentido, el desarrollo de una solución al problema SLAM mediante la optimización de un grafo de características geométricas constituye un primer paso hacia nuevos métodos de construcción de mapas en entornos estructurados. En el primero de los dos marcos propuestos, el sistema deduce la información a priori a aplicar en cada caso en base a una extensa colección de posibles modelos geométricos genéricos, siguiendo un método de Maximización de la Esperanza para hallar la estructura y el mapa más probables. La representación de la estructura del entorno se basa en un enfoque jerárquico, con diferentes niveles de abstracción para los distintos elementos geométricos que puedan describirlo. Se llevaron a cabo diversos experimentos para mostrar la versatilidad y el buen funcionamiento del método propuesto. En el segundo marco, el usuario puede definir diferentes modelos de estructura para el entorno mediante grupos de restricciones y energías locales entre puntos vecinos de un conjunto de datos del mismo. El grupo de restricciones que se aplica a cada grupo de puntos depende de la topología, que es inferida por el propio sistema. De este modo, se pueden incorporar nuevos modelos genéricos de estructura para el entorno con gran flexibilidad y facilidad. Se realizaron distintos experimentos para demostrar la flexibilidad y los buenos resultados del enfoque propuesto. Abstract Most human designed environments present specific geometrical characteristics. In them, it is easy to find polygonal, rectangular and circular shapes, with a series of typical relations between different elements of the environment. Introducing this kind of knowledge in the mapping process of mobile robots can notably improve the quality and accuracy of the resulting maps. It can also make them more suitable for higher level reasoning applications. When mapping is formulated in a Bayesian probabilistic framework, a complete specification of the problem requires considering a prior for the environment. The prior over the structure of the environment can be applied in several ways; this dissertation presents two different frameworks, one using a feature based approach and another one employing a dense representation close to the measurements space. A feature based approach implicitly imposes a prior for the environment. In this sense, feature based graph SLAM was a first step towards a new mapping solution for structured scenarios. In the first framework, the prior is inferred by the system from a wide collection of feature based priors, following an Expectation-Maximization approach to obtain the most probable structure and the most probable map. The representation of the structure of the environment is based on a hierarchical model with different levels of abstraction for the geometrical elements describing it. Various experiments were conducted to show the versatility and the good performance of the proposed method. In the second framework, different priors can be defined by the user as sets of local constraints and energies for consecutive points in a range scan from a given environment. The set of constraints applied to each group of points depends on the topology, which is inferred by the system. This way, flexible and generic priors can be incorporated very easily. Several tests were carried out to demonstrate the flexibility and the good results of the proposed approach.

Applying concept mapping strategies to improve students’ lifelong learning in physical chemistry

Este artículo ofrece una reflexión sobre el papel de los mapas conceptuales en el actual escenario de la educación In the present paper, we carry out the application of concept mapping strategies to learning Physical Chemistry, in particular, of all aspect of Corrosion. This strategy is an alternative method to supplement examinations: it can show the teacher how much the students knew and how much they didn´t know; and the students can evaluate their own learning. Before giving tile matter on Corrosion, the teachers evaluated the previous knowledge of the students in the field and explained to the students how create the conceptual maps with Cmap tools. When the subject is finished, teachers are assessed the conceptual maps developed by students and therefore also the level of the students learning. Teachers verified that the concept mapping is quite suitable for complicated theorics as Corrosion and it is an appropriate tool for the consolidation of educational experiences and for improvement affective lifelong learning. By using this method we demonstrated that the set of concepts accumulated in the cognitive structure of every student in unique and every student has therefore arranged the concepts from top to bottom in the mapping field in different ways with different linking" phrases, although these are involved in the same learning task.

Mapping energy efficiency barriers

In the E2KW Conference we present the research we are following in collaboration with ANESE (Asociación de Empresas de Servicios Energéticos) to check the interaction among barriers that previous studies have identified. We focus our research question in the information problems that include a number of specific problems such as lack of information, asymmetric information and the well-documented principle-agent problem. Asymmetric information problems occur when one party involved in a transaction has more information that the other, which may lead to suboptimal energy decisions. The fact that energy efficiency cannot be observed (ie. it is ?invisible?) further intensifies this asymmetric information barrier.

From Analysis Model to Software Architecture: a PIM2PIM Mapping.

To our knowledge, no current software development methodology explicitly describes how to transit from the analysis model to the software architecture of the application. This paper presents a method to derive the software architecture of a system from its analysis model. To do this, we are going to use MDA. Both the analysis model and the architectural model are PIMs described with UML 2. The model type mapping designed consists of several rules (expressed using OCL and natural language) that, when applied to the analysis artifacts, generate the software architecture of the application. Specifically the rules act on elements of the UML 2 metamodel (metamodel mapping). We have developed a tool (using Smalltalk) that permits the automatic application of these rules to an analysis model defined in RoseTM to generate the application architecture expressed in the architectural style C2.

Towards Mapping Experience Design for the Internet of Things.

We are witnessing a fundamental transformation in how Internet of Things (IoT) is having an impact on the experience users have with data-driven devices, smart appliances, and connected products. The experience of any place is commonly defined as the result of a series of user engagements with a surrounding place in order to carry out daily activities (Golledge, 2002). Knowing about users? experiences becomes vital to the process of designing a map. In the near future, a user will be able to interact directly with any IoT device placed in his surrounding place and very little is known on what kinds of interactions and experiences a map might offer (Roth, 2015). The main challenge is to develop an experience design process to devise maps capable of supporting different user experience dimensions such as cognitive, sensory-physical, affective, and social (Tussyadiah and Zach, 2012). For example, in a smart city of the future, the IoT devices allowing a multimodal interaction with a map could help tourists in the assimilation of their knowledge about points of interest (cognitive experience), their association of sounds and smells to these places (sensory-physical experience), their emotional connection to them (affective experience) and their relationships with other nearby tourists (social experience). This paper aims to describe a conceptual framework for developing a Mapping Experience Design (MXD) process for building maps for smart connected places of the future. Our MXD process is focussed on the cognitive dimension of an experience in which a person perceives a place as a "living entity" that uses and feeds through his experiences. We want to help people to undergo a meaningful experience of a place through mapping what is being communicated during their interactions with the IoT devices situated in this place. Our purpose is to understand how maps can support a person?s experience in making better decisions in real-time.