To appear, AAAI-07, Integrated Intelligence Track 1 An Integrated Robotic System for Spatial Understanding and Situated Interaction in Indoor Environments

A major challenge in robotics and artificial intelligence lies in creating robots that are to cooperate with people in human-populated environments, e.g. for domestic assistance or elderly care. Such robots need skills that allow them to interact with the world and the humans living and working therein. In this paper we investigate the question of spatial understanding of human-made environments. The functionalities of our system comprise perception of the world, natural language, learning, and reasoning. For this purpose we integrate state-of-the-art components from different disciplines in AI, robotics and cognitive systems into a mobile robot system. The work focuses on the description of the principles we used for the integration, including cross-modal integration, ontology-based mediation, and multiple levels of abstraction of perception. Finally, we present experiments with the integrated “CoSy Explorer ” 1 system and list some of the major lessons that were learned from its design, implementation, and evaluation

Semantic information for robot navigation: a survey

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situationsThe research leading to these results has received funding from HEROITEA: Heterogeneous 480 Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish 481 Ministerio de Economía y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón física, cognitiva y afectiva de mayores"; funded by the Spanish State Research Agency under grant 2019/00428/001. It is also funded by WASP-AI Sweden; and by Spanish project Robotic-Based Well-Being Monitoring and Coaching for Elderly People during Daily Life Activities (RTI2018-095599-A-C22)

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Crea un robot para la biblioteca de la UPCT

Para localizar un libro en la biblioteca ya no hará falta que el bibliotecario deje su puesto. Un pequeño robot se encargará de dirigir al usuario hacia la estantería donde se encuentra el volumen que necesita. La idea es de Antonio Pérez, un alumno del Máster en Ingeniería Industrial, que ha desarrollado este invento en su Trabajo Fin de Máster, partiendo de un robot que ya existía en el mercado y al que se le han incorporado unos sensores para mejorar su utilidad en la biblioteca. El trabajo, dirigido por Óscar Martínez y Francisco Ortiz, se ha enfocado para que el robot funcione exclusivamente en una biblioteca, aunque si bien es cierto que, como apunta su creador, se podría usar en otros ámbitos, como en una cafetería. 'En principio se quería hacer para la cafetería, pero es un entorno con muchos obstáculos, había que depurar mucho el sistema y para evitar problemas preferimos usarlo para la biblioteca', afirma. El robot es de bajo coste comercial, que ha supuesto un coste de unos 800 euros. Se ha modificado la estructura del robot para añadirle un sensor extra que aporta un campo de visión más amplio y preciso que el que traía pr defecto y así pueda evitar tanto objetos fijos como móviles. El precio global ha sido de unos 4.000 euros sufragado por la propia universidad, que será la que emplee el robot. Por su parte, uno de los coodirectores del TFM, Óscar Martínez, ha explicado que el joven estaba muy interesado en hacer un proyecto de robótica móvil de servicio, tras pensar en una aplicación cercana a la sociedad se dieron cuenta de que hasta ahora no existía nada similar en las bibliotecas. Hasta ahora, si alguien preguntaba por un libro y no lo encontraba, el bibliotecario debía abandonar su puesto en recepción para ir a la estantería donde se encontraba el libro. Ahora, es el propio bibliotecario el que da las indicaciones al robot para que se dirija a la estantería donde está el volumen que se busca. Aunque Antonio finaliza con este proyecto el máster y ahora piensa en aprovechar para hacer un Erasmus +, su director de trabajo manifiesta que lo ideal sería que otro estudiante continuará la línea de investigación de Antonio para mejorar este robot o darle nuevos usos

Local Descriptors for Visual SLAM

We present a comparison of several local image descriptors in the context of visual Simultaneous Localization and Mapping (SLAM). In visual SLAM a set of points in the environment are extracted from images and used as landmarks. The points are represented by local descriptors used to resolve the association between landmarks. In this paper, we study the class separability of several descriptors under changes in viewpoint and scale. Several experiments were carried out using sequences of images in 2D and 3D scenes.