Usage of Automatic Guided Vehicle Systems and Multi-agent Technology in higher education

Today, smart manufacturing is differentiated from many other initiatives by its emphasis on human ingenuity. Human capabilities must be enhanced by intelligently designing a customized solution for a specific domain. For example, Industry 4.0 is based on collaborative robots that digitize and simplify manufacturing processes. In fact, Automatic Guided Vehicles (AGVs) are widely used in intelligent industries due to their productivity, flexibility, and versatility. They are widely considered as one of the most important tools for flexible logistics in workshops. They can move materials and products without a predefined route. Many commercially available AGVs provide a self-guided navigation system to find their way to target workstations. However, many developers and producers of industrial robots face several challenges in designing AGV systems, such as the difficulty of defining a decentralized system decision as well as the discontinuity and complexity of the design process. One of the relevant research areas related to our AGV solution is the establishment of the human-machine industrial relationship and the creation of safe operation side by side

Intelligent Robotics: Navigation, Planning, and Human-Robot Interaction

The development of robotic systems that are able to independently navigate their environments, effectively plan their activities, and communicate naturally with people has given rise to the field of research known as intelligent robotics. The objective of this abstract is to give a summary of the developments in intelligent robotics with regard to planning, navigation, and human-robot interaction. As a result, the fields of navigation, planning, and human-robot interaction have seen notable breakthroughs in intelligent robots. Robots are now capable of navigating across complicated areas with efficiency because to the development of reliable navigation algorithms. Robots may now use planning strategies to make wise judgments and carry out activities on their own. Additionally, research on human-robot interaction has concentrated on creating user-friendly interfaces that allow for seamless collaboration between humans and robots. These developments open the way for intelligent robots to become fundamental elements of our society, improving output, security, and quality of life across a range of fields. But more study is still needed to address issues like long-term autonomy, environment adaptation, and the moral ramifications of widespread use of intelligent robots

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Extended Kalman Filter SLAM Implementation for a Differential Robot with LiDAR

SLAM is an approach deployed in robotics to develop autonomous mobile robots. These robots have been deployed in numerous fields such as manufacturing, aerospace navigation, and other areas deemed dangerous to humans. SLAM techniques have made these robots to operate without necessarily having the prior maps, a shortcoming of the current robots which require prior maps. Several SLAM approaches exist, but EKF has been seen to possess all the useful features of convergence and consistency. Via SLAM, concurrency between localisation and mapping has been made possible. An EKF SLAM algorithm has been presented in this thesis, which was implemented in a two-wheeled mobile robot. The robot autonomously navigated in a structured indoor environment, while simultaneously building a map and localising itself within that map. A 360 degrees LiDAR was used to measure the range and bearing of the surroundings and an ultrasound sensor was used to avoid the obstacles. Furthermore, the algorithm was implemented using Python 3

ФЕДЕРАТИВНОЕ ОБУЧЕНИЕ ДЛЯ ВИЗУАЛЬНОГО ОБХОДА ПРЕПЯТСТВИЙ В МОБИЛЬНЫХ РОБОТАХ

Federated learning (FL) is a machine learning approach that allows multiple devices or systems to train a model collaboratively, without exchanging their data. This is particularly useful for autonomous mobile robots, as it allows them to train models customized to their specific environment and tasks, while keeping the data they collect private. Research Objective to train a model to recognize and classify different types of objects, or to navigate around obstacles in its environment. Materials and me¬thods we used FL to train models for a variety of tasks, such as object recognition, obstacle avoidance, localization, and path planning by an autonomous mobile robot operating in a warehouse FL. We equipped the robot with sensors and a processor to collect data and perform machine learning tasks. The robot must communicate with a central server or cloud platform that coordinates the training process and collects model updates from different devices. We trained a neural network (CNN) and used a PID algorithm to generate a control signal that adjusts the position or other variable of the system based on the difference between the desired and actual values, using the relative, integrative and derivative terms to achieve the desired performance. Results through careful design and execution, there are several challenges to implementing FL in autonomous mobile robots, including the need to ensure data privacy and security, and the need to manage communications and the computational resources needed to train the model. Conclusion. We conclude that FL enables autonomous mobile robots to continuously improve their performance and adapt to changing environments and potentially improve the performance of vision-based obstacle avoidance strategies and enable them to learn and adapt more quickly and effectively, leading to more robust and autonomous systems.Федеративное обучение – это подход к машинному обучению, который позволяет нескольким устройствам или системам совместно обучать модель без обмена данными. Это особенно полезно для автономных мобильных роботов, поскольку позволяет им обучать модели, адаптированные к их конкретной среде и задачам, сохраняя конфиденциальность собираемых ими данных. Цель исследования состоит в том, чтобы научить модель распознавать и классифицировать различные типы объектов или обходить препятствия в окружающей среде. Материалы и методы: использовано федеративное машинное обучение для обучения моделей различным задачам, таким как распознавание объектов, обход препятствий, локализация и планирование пути с помощью автономного мобильного робота, работающего на складе. Робот оснащен датчиками и процессором для сбора данных и выполнения задач машинного обучения. Робот должен связываться с центральным сервером или облачной платформой, которая координирует процесс обучения и собирает обновления моделей с разных устройств. Нейронная сеть обучена с использованием алгоритма PID для генерации управляющего сигнала, который регулирует положение или другую переменную системы на основе разницы между желаемыми и фактическими значениями, используя относительные, интегративные и производные условия для достижения желаемой производительности. Результаты. Даже при условии тщательного проектирования и исполнения существует несколько проблем при реализации федеративного обучения в автономных мобильных роботах, включая необходимость обеспечения конфиденциальности и безопасности данных, а также необходимость управления коммуникациями и вычислительными ресурсами, необходимыми для обучения модели. Заключение. Был сделан вывод о том, что федеративное обучение позволяет автономным мобильным роботам постоянно повышать свою производительность и адаптироваться к изменяющимся условиям, а также потенциально улучшать эффективность стратегий обхода препятствий на основе зрения и позволяет им быстрее и эффективнее учиться и адаптироваться, что приводит к созданию более надежных и автономных систем

Dynamic obstacles avoidance algorithms for unmanned ground vehicles

En las últimas décadas, los vehículos terrestres no tripulados (UGVs) están siendo cada vez más empleados como robots de servicios. A diferencia de los robots industriales, situados en posiciones fijas y controladas, estos han de trabajar en entornos dinámicos, compartiendo su espacio con otros vehículos y personas. Los UGVs han de ser capaces de desplazarse sin colisionar con ningún obstáculo, de tal manera que puedan asegurar tanto su integridad como la del entorno. En el estado del arte encontramos algoritmos de navegación autónoma diseñados para UGVs que son capaces de planificar rutas de forma segura con objetos estáticos y trabajando en entornos parcialmente controlados. Sin embargo, cuando estos entornos son dinámicos, se planifican rutas más peligrosas y que a menudo requieren de un mayor consumo de energía y recursos, e incluso pueden llegar a bloquear el UGV en un mínimo local. En esta tesis, la adaptación de algunos algoritmos disponibles en el estado del arte para trabajar en entornos dinámicos han sido planteados. Estos algoritmos incluyen información temporal tales como los basados en arcos de curvatura (PCVM y DCVM) y los basados en ventanas dinámicas (DW4DO y DW4DOT). Además, se ha propuesto un planificador global basado en Lattice State Planner (DLP) que puede resolver situaciones donde los evitadores de obstáculos reactivos no funcionan. Estos algoritmos han sido validados tanto en simulación como en entornos reales, utilizando distintas plataformas robóticas, entre las que se incluye un robot asistente (RoboShop) diseñado y construido en el marco de esta tesis

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

Long-term robot mapping in dynamic environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 139-144).One of the central goals in mobile robotics is to develop a mobile robot that can construct a map of an initially unknown dynamic environment. This is often referred to as the Simultaneous Localization and Mapping (SLAM) problem. A number of approaches to the SLAM problem have been successfully developed and applied, particularly to a mobile robot constructing a map of a 2D static indoor environment. While these methods work well for static environments, they are not robust to dynamic environments which are complex and composed of numerous objects that move at wide-varying time-scales, such as people or office furniture. The problem of maintaining a map of a dynamic environment is important for both real-world applications and for the advancement of robotics. A mobile robot executing extended missions, such as autonomously collecting data underwater for months or years, must be able to reliably know where it is, update its map as the environment changes, and recover from mistakes. From a fundamental perspective, this work is important in order to understand and determine the problems that occur with existing mapping techniques for persistent long-term operation. The primary contribution of the thesis is Dynamic Pose Graph SLAM (DPG-SLAM), a novel algorithm that addresses two core challenges of the long-term mapping problem. The first challenge is to ensure that the robot is able to remain localized in a changing environment over great lengths of time. The second challenge is to be able to maintain an up-to-date map over time in a computationally efficient manner. DPG-SLAM directly addresses both of these issues to enable long-term mobile robot navigation and map maintenance in changing environments. Using Kaess and Dellaert's incremental Smoothing and Mapping (iSAM) as the underlying SLAM state estimation engine, the dynamic pose graph evolves over time as the robot explores new areas and revisits previously mapped areas. The algorithm is demonstrated on two real-world dynamic indoor laser data sets, demonstrating the ability to maintain an efficient, up-to-date map despite long-term environmental changes. Future research issues, such as the integration of adaptive exploration with dynamic map maintenance, are identified.by Aisha Naima Walcott.Ph.D