Efficient Solutions in Path Planning for Autonomous Mobile Robots

Fecha de lectura de Tesis: 25 julio 2018.Los robots, máquinas que desempeñan un abanico de tareas de lo más variopinto. Desde realizar tareas muy específicas en cadenas de montaje hasta desempeñar la mayoría de labores cotidianas que los seres humanos tenemos que afrontar cada día. Como se puede intuir, para esto se necesitan no solo máquinas, sino máquinas dotadas de cierta inteligencia, que surge de la necesidad de que las máquinas abandonen su estatismo y monotonía para comenzar a enfrentarse a un mundo dinámico y ambiguo: nuestro mundo. El principal desencadenante que ha llevado al ser humano a dotar de inteligencia y movilidad a las máquinas es su afán de dominar y, al mismo tiempo, liberarse de un entorno cada vez más estresante. Hay dos aspectos irrefutables que marcan la versatilidad de una máquina: su inteligencia y su movilidad. Hablando de robótica y movilidad surge el problema de cómo y por dónde debe moverse un robot para alcanzar un determinado objetivo sin comprometer su integridad física. Como el significado de moverse puede ser muy amplio, aquí hablaremos de desplazamiento, en el sentido literal de viajar. Y cuando viajamos a algún lugar siempre nos preguntamos lo siguiente: ¿Por dónde vamos? y ¿Cuál es el la mejor alternativa?. Esta problemática, en robótica, se conoce como el problema del Path Planning. En esta tesis doctoral se aborda, de manera innovadora y altamente paralela, el problema del Path Planing sobre mapas reales extensos en un contexto de tiempo real. Este grado de paralelismo se consigue gracias al uso intensivo de las populares GPU (Unidad Gráfica de Procesamiento) y de los bien conocidos chips multi-core. Pero aquí no solo se aborda el problema del Path Planning desde un punto de vista altamente paralelo sino que, de manera transversal, también se aborda desde un punto de vista inteligente aplicando metaheurísticas

On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehousePartial funding for open access charge: Universidad de Málaga. This work has been partially developed within SA3IR (an experiment funded by EU H2020 ESMERA Project under Grant Agreement 780265), the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the B1-2021_26 project, funded by the University of Málaga

Integration of the Alexa assistant as a voice interface for robotics platforms

Virtual assistants such as Cortana or Google Assistant are becoming familiar devices in everyday environments, where they are used to control real devices through natural language. This paper extends this application scenario, and it describes the use of the Alexa assistant from Amazon through an Echo dot device to drive the behaviour of a robotic platform. The paper focuses on the description of the technologies employed to set such ecosystem. Significantly, the proposed architecture is based, from the remote server to the on-board controllers, in LowEnergy (LE) hardware and a scalable software platform. This approach will ease programmers integrating different platforms, e.g. mobile-based applications to control robots or home-made devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Evolution of Socially-Aware Robot Navigation

In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans

Measuring Smoothness as a Factor for Efficient and Socially Accepted Robot Motion

Social robots, designed to interact and assist people in social daily life scenarios, require adequate path planning algorithms to navigate autonomously through these environments. These algorithms have not only to find feasible paths but also to consider other requirements, such as optimizing energy consumption or making the robot behave in a socially accepted way. Path planning can be tuned according to a set of factors, being the most common path length, safety, and smoothness. This last factor may have a strong relation with energy consumption and social acceptability of produced motion, but this possible relation has never been deeply studied. The current paper focuses on performing a double analysis through two experiments. One of them analyzes energy consumption in a real robot for trajectories that use different smoothness factors. The other analyzes social acceptance for different smoothness factors by presenting different simulated situations to different people and collecting their impressions. The results of these experiments show that, in general terms, smoother paths decrease energy consumption and increase acceptability, as far as other key factors, such as distance to people, are fulfilled

Development of a virtual preclinical practical curriculum for acquisition of skills in the use of rotary instruments for dental students

La pandemia SARS-CoV-2 obliga a buscar soluciones educativas no presenciales; sin embargo, la adquisición de habilidades necesarias en Odontología son difícilmente virtualizables. Este proyecto ofrece una alternativa viable de prácticas preclínicas a distancia.The SARS-CoV-2 pandemic has forced dental schools to find virtual educational solutions for students to gain competency in different areas. Specially important is the acquisition of skills in Dentistry; however, preclinical training is difficult to virtualize. This project offers a viable alternative for virtual preclinical practices.Depto. de Odontología Conservadora y PrótesisFac. de OdontologíaFALSEVicerrectorado de Calidad UCMsubmitte

#Own your future: Promocionando la empleabilidad y el emprendimiento entre los estudiantes de Odontología

Los miembros de este equipo pretendimos facilitar la incorporación al mundo laboral de nuestros estudiantes y despertar su espíritu emprendedor abriendo nuevos horizontes de trabajo con este proyecto y creando nuevas perspectivas de desarrollo profesional basados en la excelencia de trabajo, esfuerzo y principios deontológicos.Depto. de Odontología Conservadora y PrótesisFac. de OdontologíaFALSEsubmitte