Human-Like room segmentation for domestic cleaning robots

Fleer DR. Human-Like room segmentation for domestic cleaning robots. Robotics. 2017;6(4): 35.Autonomous mobile robots have recently become a popular solution for automating cleaning tasks. In one application, the robot cleans a floor space by traversing and covering it completely. While fulfilling its task, such a robot may create a map of its surroundings. For domestic indoor environments, these maps often consist of rooms connected by passageways. Segmenting the map into these rooms has several uses, such as hierarchical planning of cleaning runs by the robot, or the definition of cleaning plans by the user. Especially in the latter application, the robot-generated room segmentation should match the human understanding of rooms. Here, we present a novel method that solves this problem for the graph of a topo-metric map: first, a classifier identifies those graph edges that cross a border between rooms. This classifier utilizes data from multiple robot sensors, such as obstacle measurements and camera images. Next, we attempt to segment the map at these room–border edges using graph clustering. By training the classifier on user-annotated data, this produces a human-like room segmentation. We optimize and test our method on numerous realistic maps generated by our cleaning-robot prototype and its simulated version. Overall, we find that our method produces more human-like room segmentations compared to mere graph clustering. However, unusual room borders that differ from the training data remain a challen

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Vision-Based Autonomous Robotic Floor Cleaning in Domestic Environments

Fleer DR. Vision-Based Autonomous Robotic Floor Cleaning in Domestic Environments. Bielefeld: Universität Bielefeld; 2018

Imagine staying in a Shanghai hotel bedroom in 2050?

Will the future hotels of Shanghai emphasise a world of contemporary design, sustainability and technological innovations in order to deal with the growing pains of pollution, competition of urban land and decreasing availability of clean water, which will impact on the quality and price of accommodation in  the city? This paper imagines what a hotel might look like in 2050 based upon nine drivers of change, whether it is new sciences such as claytronics, or programmable matter that integrate sight, sound and feel into original ideas, allowing users to interact with three-dimensional form. The applications of claytronics would be the reconfiguration of everything, so just imagine the future hotel bed that could change its degree of comfort from a hard to a soft mattress without too much effort, the possibilities are endless. Other drivers include robotics as an alternative to a human labour supply or the behaviours of  Generation Y. The heart to the future is sustainable design and this paper discusses how the hotel will feature many of these changes in a future world in order to mitigate and adapt to a paradigm of scarcity of resources.Keywords: drivers of change, future hotels, innovations, sustainability, sustainable designResearch in Hospitality Management 2012, 1(2): 85–9

Scene understanding for autonomous robots operating in indoor environments

Mención Internacional en el título de doctorThe idea of having robots among us is not new. Great efforts are continually made to replicate human intelligence, with the vision of having robots performing different activities, including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed. However, there are some tasks that humans do without much effort that are challenging for robots. Especially robots in domestic environments are far from satisfactorily fulfilling some tasks, mainly because these environments are unstructured, cluttered, and with a variety of environmental conditions to control. This thesis addresses the problem of scene understanding in the context of autonomous robots operating in everyday human environments. Furthermore, this thesis is developed under the HEROITEA research project that aims to develop a robot system to help elderly people in domestic environments as an assistant. Our main objective is to develop different methods that allow robots to acquire more information from the environment to progressively build knowledge that allows them to improve the performance on high-level robotic tasks. In this way, scene understanding is a broad research topic, and it is considered a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis, we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation of the environment. Firstly, we implement methods to recognize objects considering real indoor environments. We applied machine learning techniques incorporating uncertainties and more modern techniques based on deep learning. Besides, apart from detecting objects, it is essential to comprehend the scene where they can occur. For this reason, we propose an approach for scene recognition that considers the influence of the detected objects in the prediction process. We demonstrate that the exiting objects and their relationships can improve the inference about the scene class. We also consider that a scene recognition model can benefit from the advantages of other models. We propose a multi-classifier model for scene recognition based on weighted voting schemes. The experiments carried out in real-world indoor environments demonstrate that the adequate combination of independent classifiers allows obtaining a more robust and precise model for scene recognition. Moreover, to increase the understanding of a robot about its surroundings, we propose a new division of the environment based on regions to build a useful representation of the environment. Object and scene information is integrated into a probabilistic fashion generating a semantic map of the environment containing meaningful regions within each room. The proposed system has been assessed on simulated and real-world domestic scenarios, demonstrating its ability to generate consistent environment representations. Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is why in this thesis, we try to study how a complete knowledge of the environment influences the robot’s performance in high-level tasks. To do so, we select an essential task, which is searching for objects. This mundane task can be considered a precondition to perform many complex robotic tasks such as fetching and carrying, manipulation, user requirements, among others. The execution of these activities by service robots needs full knowledge of the environment to perform each task efficiently. In this thesis, we propose two searching strategies that consider prior information, semantic representation of the environment, and the relationships between known objects and the type of scene. All our developments are evaluated in simulated and real-world environments, integrated with other systems, and operating in real platforms, demonstrating their feasibility to implement in real scenarios, and in some cases outperforming other approaches. We also demonstrate how our representation of the environment can boost the performance of more complex robotic tasks compared to more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha demostrado que los robots son buenos en muchas tareas que resultan difíciles para nosotros, principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas que los humanos realizamos sin mucho esfuerzo y que son un desafío para los robots. Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar desordenados y cuentan con una gran variedad de condiciones ambientales que controlar. Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a los robots adquirir más información del entorno a fin de construir progresivamente un conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas. En este sentido, la comprensión de escenas es un tema de investigación amplio, y se considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis nos enfocamos específicamente en tres subtareas: detección de objetos, reconocimiento de escenas y etiquetado semántico del entorno. Por un lado, implementamos métodos para el reconocimiento de objectos considerando entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes y sus relaciones pueden mejorar el proceso de inferencia de la categoría de la escena. También consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo en entornos interiores reales demuestran que la combinación adecuada de clasificadores independientes permite obtener un modelo más robusto y preciso para el reconocimiento de escenas. Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno, proponemos una nueva división del entorno basada en regiones a fin de construir una representación útil del entorno. La información de objetos y de la escena se integra de forma probabilística generando un mapa semántico que contiene regiones significativas dentro de cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y reales, demostrando su capacidad para generar representaciones consistentes del entorno. Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse una condición previa para realizar diversas tareas robóticas complejas, como transportar objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La ejecución de estas actividades por parte de robots de servicio requiere un conocimiento profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos dos estrategias de búsqueda de objetos que consideran información previa, la representación semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando Matía Espada.- Vocal: Klaus Strob

2D Floor Plan Segmentation Based on Down-sampling

In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our technique considerably reduces the computational and implementation complexity of floor plan segmentation, making it more suitable for real-world applications. Additionally, we discuss the appropriate metric for evaluating segmentation results. Overall, our approach yields promising results for 2D floor plan segmentation in cluttered environments

A Survey on Human-aware Robot Navigation

Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202