Evaluation of Modern Laser Based Indoor SLAM Algorithms

One of the key issues that prevents creation of a truly autonomous mobile robot is the simultaneous localization and mapping (SLAM) problem. A solution is supposed to estimate a robot pose and to build a map of an unknown environment simultaneously. Despite existence of different algorithms that try to solve the problem, the universal one has not been proposed yet [1]. A laser rangefinder is a widespread sensor for mobile platforms and it was decided to evaluate actual 2D laser scan based SLAM algorithms on real world indoor environments. The following algorithms were considered: Google Cartographer [2], GMapping [3], tinySLAM [4]. According to their evaluation, Cartographer and GMapping are more accurate than tinySLAM and Cartographer is the most robust of the algorithms

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments

Integración de un robot móvil en el entorno ROS (Robot Operating System). Análisis de la implementación en diferentes versiones

Trabajo Fin de Máster. Máster Universitario en sistemas inteligentes. Curso académico 2022-2023.[ES]En los últimos años, el campo de la robótica ha experimentado una evolución exponencial, y el uso de robots móviles se ha vuelto cada vez más común en diversos sectores de la sociedad, como la industria, la medicina, la agricultura y la logística. Por ello, cada vez más los desarrolladores optan por el uso de herramientas o recursos que les permitan un desarrollo de software ágil. Para poder desarrollar software en la robótica, se requiere de herramientas y plataformas de desarrollo software especializadas. Una de las más destacadas y ampliamente utilizadas es el Robot Operating System (ROS). En esta memoria, se realizará un análisis detallado de las diferentes versiones de ROS, desde las primeras versiones hasta las más recientes. Se examinarán las características y mejoras introducidas en cada una de ellas, así como las posibles limitaciones y desafíos asociados durante la prueba de las mismas. Además, en este proyecto se abordará el proceso de integración de un robot móvil en las versiones de ROS 1 y ROS 2. Indicando el proceso seguido desde la selección de componentes principales, como la percepción, el control, los recursos software y la planificación de movimiento. En este proceso se explorarán diferentes paquetes, repositorios y herramientas disponibles en ROS que facilitan esta integración. El objetivo principal de esta memoria es proporcionar a los lectores una visión global de la integración de un robot móvil en el entorno de ROS, abarcando las diferentes versiones de esta plataforma y su evolución a lo largo del tiempo. En conclusión, se espera que esta investigación fomente el uso y la investigación en el campo de la robótica, impulsando el avance de la tecnología y su aplicación en diversas áreas de la sociedad.[EN]In recent years, the field of robotics has undergone an exponential evolution, and the use of mobile robots has become increasingly common in various sectors of society, such as industry, medicine, agriculture and logistics. As a result, more and more developers are opting for the use of tools or resources that enable agile software development. In order to develop software in robotics, specialised software development tools and platforms are required. One of the most prominent and widely used is the Robot Operating System (ROS). In this report, a detailed analysis of the different versions of ROS, from the ear liest versions to the most recent ones, will be carried out. It will examine the features and improvements introduced in each of them, as well as the possible limitations and challenges associated with testing them. In addition, this project will address the process of integrating a mobile robot into ROS 1 and ROS 2 versions, indicating the process followed from the selection of main components, such as perception, control, software resources and motion planning. In this process, different packages, repositories and tools available in ROS that facilitate this integration will be explored. The main objective of this report is to provide readers with an overview of the integration of a mobile robot in the ROS environment, covering the different versions of this platform and its evolution over time. In conclusion, it is hoped that this research will encourage the use of and research in the field of robotics, driving the advancement of the technology and its application in various areas of society