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[Resumen] En este trabajo se presenta un algoritmo de detección humana basado en el uso de único sensor RGB-D para demostrar movimientos teniendo en cuenta los puntos clave del brazo humano (hombro-codo-muñeca) para controlar un brazo no antropomórfico. El método desarrollado se basa en la detección de dichos puntos significativos del brazo humano y su mapeo al robot, un proceso de filtrado Gaussiano que permite suavizar los movimientos y el ruido del sensor y un algoritmo de optimización que obtiene la configuración más cercana al brazo humano sin generar colisiones con el entorno ni consigo mismo. La novedad de este método se basa en la utilización de los puntos clave del brazo humano, teniendo en cuenta el efector final y el codo, para obtener la configuración más similar para un brazo no antropomórfico. Se han generado pruebas con diferentes movimientos realizados a distintas velocidades para validar nuestro método, comprobando su eficacia en el efector final del robot., [Abstract] This paper presents a human detection algorithm based on the use of a single RGB-D sensor to demonstrate movements taking into account the key points of the human arm (shoulder-elbow-wrist) to control a non-anthropomorphic arm. The developed method is based on the detection of significant points of the human arm and their mapping to the robot, a Gaussian filtering process that allows smoothing movements and sensor noise, and an optimisation algorithm that obtains the closest configuration to the human arm without generating collisions with the environment or itself. The novelty of this method is based on the use of the key points of the human arm, and taking into account the end-effector and the elbow, to obtain the most similar configuration for a nonanthropomorphic arm. Tests have been generated with different movements performed at different speeds to validate our method, checking its effectiveness on the robot’s end effector.

The increasing isolation of the elderly both in their own homes and in care homes has made the problem of caring for elderly people who live alone an urgent priority. This article presents a proposed design for a heterogeneous multirobot system consisting of (i) a small mobile robot to monitor the well-being of elderly people who live alone and suggest activities to keep them positive and active and (ii) a domestic mobile manipulating robot that helps to perform household tasks. The entire system is integrated in an automated home environment (AAL), which also includes a set of low-cost automation sensors, a medical monitoring bracelet and an Android application to propose emotional coaching activities to the person who lives alone. The heterogeneous system uses ROS, IoT technologies, such as Node-RED, and the Home Assistant Platform. Both platforms with the home automation system have been tested over a long period of time and integrated in a real test environment, with good results. The semantic segmentation of the navigation and planning environment in the mobile manipulator for navigation and movement in the manipulation area facilitated the tasks of the later planners. Results about the interactions of users with the applications are presented and the use of artificial intelligence to predict mood is discussed. The experiments support the conclusion that the assistance robot correctly proposes activities, such as calling a relative, exercising, etc., during the day, according to the user's detected emotional state, making this is an innovative proposal aimed at empowering the elderly so that they can be autonomous in their homes and have a good quality of life., Funding agency:Fundacion Seneca 19895/GERM/15

[Resumen] Los manipuladores robóticos son sistemas altamente no lineales, y es difícil obtener un modelo matemático preciso con técnicas convencionales. Aplicando técnicas de control clásico se puede resolver este tipo de problemas, pero tiene el inconveniente de que se tarda una gran cantidad de tiempo en obtener un resultado satisfactorio para manipuladores donde se tenga una gran cantidad de grados de libertad (GDL). Es por esto, por lo que se requiere una técnica eficiente para tratar con este tipo de sistemas complejos y dinámicos. El algoritmo de Differential Evolution (DE) es una técnica de optimización global muy potente la cual en la actualidad se ha hecho popular debido a su posible aplicación en una gran cantidad de campos dentro de la robótica. En el presente trabajo se muestra un aplicación directa de este método de optimización en un modelo simulado de un brazo de 6 GDL y una posterior aplicación sobre un modelo de bajo coste., [Abstract] Robotic manipulators are highly non-linear systems, and it is difficult to obtain an accurate mathematical model with conventional techniques. Applying classical control techniques can solve this type of problem, but it has the disadvantage that it takes a large amount of time to obtain a satisfactory result for manipulators with a large number of degrees of freedom (GDL). Therefore, an efficient technique is required to deal with such complex and dynamic systems. The Differential Evolution (DE) algorithm is a very powerful global optimisation technique which has nowadays become popular due to its possible application in a large number of fields within robotics. This paper shows a direct application of this optimisation method on a simulated model of a 6 GDL arm and a subsequent application on a low-cost model.

[Resumen] La presencia de los robots móviles en entornos de interior es cada vez mayor para tareas como la asistencia de personas mayores. Dado que estas plataformas deben adaptarse a las dimensiones y formas de los entornos donde se encuentran, su tamaño resulta considerable. Es por ello que se necesita una estrategia de navegación que permita el paso de zonas estrechas de una manera segura. Este trabajo propone la coordinación de niveles geométrico y topológico para la navegación de entornos de interior teniendo en cuenta el paso de puertas. Se propone el mapeado geométrico y topológico del entorno, así como una coordinación de dos estrategias de navegación, una para zonas de mayor tamaño y otra, a más bajo nivel, para el paso de puertas. El método es probado en un entorno real, mostrando su eficacia., [Abstract] The presence of mobile robots on indoor environments for tasks such as assisting elderly people is constantly growing. Given that these platforms need to perform tasks such as cooking in scenarios thought for people, their dimension and shape must be adapted to them. This fact derives into considerably large mobile platforms which need navigation strategies for trespassing narrow zones in a safe manner. This work proposes the coordination of geometric and topological levels for navigating indoor environments taking into account door locations. The environment is initially mapped using geometric and topological information, which is afterwards used for coordinating two different navigation strategies, one for wide areas and another one for narrow passages. Experiments are carried out in a real scenario, proving the correct functioning of the proposed method.

The success of mobile robots, specially those operating in human environments, relies on the ability to understand human structures. The aim of this work is to develop a localization framework that considers different scenes and a hierarchical model of the environment. A probabilistic model for recognizing scenes including the objects in the environment is implemented. An efficient hierarchical model formed by different topological representations is used to localize the robot. The localization result is improved by semantic scene information. The experiments developed give preliminary results of our framework working in real environments. They uphold the usefulness of integrating the understanding of the environment in a localization process., Funding Agency:RoboCity2030-DIH-CM project (RoboCity2030 - Madrid Robotics Digital Innovation Hub), Grant Number: S2018/NMT-4331

The objective of the chapter is to show current trends in robot navigation systems related to indoor environments. Navigation systems depend on the level of abstraction of the environment representation. The three main techniques for representing the environment will be described: geometric, topological, and semantic. The geometric representation of the environment is closer to the sensor and actuator world and it is the best one to perform local navigation. Topological representation of the environment uses graphs to model the environment and it is used in large navigation tasks. The semantic representation is the most abstract representation model and adds concepts such as utilities or meanings of the environment elements in the map representation. In addition, regardless of the representation used for navigation, perception plays a significant role in terms of understanding and moving through the environment.

Semantic navigation is the navigation paradigm in which environmental semantic concepts and their relationships are taken into account to plan the route of a mobile robot. This paradigm facilitates the interaction with humans and the understanding of human environments in terms of navigation goals and tasks. At the high level, a semantic navigation system requires two main components: a semantic representation of the environment, and a reasoning system. This paper is focused on develop a model of the environment using semantic concepts. This paper presents two solutions for the semantic navigation paradigm. Both systems implement an ontological model. Whilst the first one uses a relational database, the second one is based on KnowRob. Both systems have been integrated in a semantic navigator. We compare both systems at the qualitative and quantitative levels, and present an implementation on a mobile robot as a proof of concept.

Giving a robot autonomy and independence in a human environment implies not only having to move safely but also the ability to understand the environment where it is located. Scene understanding is one of the most challenging tasks in robotics because the design, the objects and the arrangement of them in the scene varies considerably. In this paper we present a Probabilistic Place Recognition Model applied to mobile robots and able to work in indoor human environments. A model of uncertainties is proposed based on the information about the objects in the scene and the relationships between them. This information can influence the final decision about the probability of the presence of a robot in a place. The experimental results obtained of common indoor human environments demonstrate the ability of the model to predict place categories considering the information of the objects and the relations between them. Using more information in the prediction process makes the model more descriptive, scalable and better adapted for human-robot and robot-environment interaction., Funding Agency:RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III) - Programas de Actividades I+D en la Comunidad de Madri, Grant Number: S2013/MIT-2748European Union (EU)NAVEGASE-AUTOCOGNAV project - Ministerio de Economia y competitividad of SPAIN, Grant Number: DPI2014-53525-C3-3-R

The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks., Funding Agency:RoboCity-III-CM project, Grant Number: S2013/MIT-2748Programas de Actividades I+D en la Comunidad de Madrid European Union (EU)NAVEGASE-AUTOCOGNAV, Grant Number: DPI2014-53525-C3-3-RMinisterio de Economia y Competitividad of SPAIN