Incremental Topological Modeling using Sonar Gridmap in Home Environment

Abstract-This paper presents a method of topological modeling in home environments using only low-cost sonar sensors. The proposed method constructs a topological model using sonar gridmap by extracting subregions incrementally. A confidence for each occupied grid is evaluated to obtain reliable regions in a local gridmap, and a convexity measure is used to extract subregions automatically. Through these processes, the topological model is constructed without predefining the number of subregions in advance and the extracted subregions are guaranteed the convexity. Experimental results verify the performance of proposed method in real home environment

A MYOELECTRIC PROSTHETIC ARM CONTROLLED BY A SENSOR-ACTUATOR LOOP

This paper describes new methods and systems designed for application in upper extremity prostheses. An artificial upper limb with this system is a robot arm controlled by EMG signals and a set of sensors. The new multi-sensor system is based on ultrasonic sensors, infrared sensors, Hall-effect sensors, a CO2 sensor and a relative humidity sensor. The multi-sensor system is used to update a 3D map of objects in the robot’s environment, or it directly sends information about the environment to the control system of the myoelectric arm. Occupancy grid mapping is used to build a 3D map of the robot’s environment. The multi-sensor system can identify the distance of objects in 3D space, and the information from the system is used in a 3D map to identify potential collisions or a potentially dangerous environment, which could damage the prosthesis or the user. Information from the sensors and from the 3D map is evaluated using a fuzzy expert system. The control system of the myoelectric prosthetic arm can choose an adequate reaction on the basis of information from the fuzzy expert system. The systems and methods were designed and verified using MatLab/Simulink. They are aimed for use as assistive technology for disabled people

Toward autonomous harbor surveillance

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Includes bibliographical references (p. 105-113).In this thesis we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach uses only onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. In addition, an architecture for a persistent mapping is proposed. With the intention of handling long term operations and repetitive surveillance tasks. The proposed architecture is flexible and supports different types of vehicles and mapping methods. The design of the system is demonstrated with an implementation of some of the key features of the system. In addition, methods for re-localization are considered. Finally, results from several experiments that demonstrate drift-free navigation in various underwater environments are presented.by Hordur Johannsson.S.M

UVC Dose Mapping by Mobile Robots

As infeções adquiridas em ambientes hospitalares são um problema persistente e crescente e a sua prevenção envolve a desinfeção de áreas e superfícies. A necessidade de métodos de desinfeção eficazes aumentou muito em consequência da pandemia de Covid-19. Um método eficaz é a utilização de exposição UVC porque a radiação UVC é absorvida pelos ácidos nucleicos e, portanto, é capaz de inativar microrganismos. Este método também traz muitas vantagens quando comparado com os métodos tradicionais de desinfeção. A desinfeção UVC pode ser realizada por equipamentos fixos que têm de ser deslocados de um local para outro de modo a desinfetar toda uma área, ou por um equipamento móvel autónomo que requer intervenção humana mínima para desinfetar completamente um ambiente. Esta dissertação foca em robôs móveis que desinfetam um ambiente utilizando radiação UVC. Estes robôs móveis são capazes de se mover autonomamente enquanto mapeiam o ambiente à sua volta e simultaneamente o desinfetam. Os robôs mantêm registo da dose aplicada a cada área do ambiente de modo a construir um mapa da dose e diferenciar as áreas completamente desinfetadas das que não o estão. Esta solução tem a vantagem de o robô realizar a desinfeção UVC sem necessitar de parar em cada área nem ter conhecimentos prévios sobre o ambiente. A validação desta solução foi realizada utilizando o rviz, uma ferramenta do Robot Operating System (ROS), e a LiDAR Camera L515. A câmara foi utilizada para recolher a informação necessária para a criação do mapa do ambiente e o rviz foi utilizado para visualizar o mapa da dose.Hospital-acquired infections are a persistent and increasing problem and their prevention involves disinfecting areas and surfaces. The necessity for effective disinfection methods has highly increased in consequence of the Covid-19 pandemic. An effective method is using UVC exposure because UVC radiation is absorbed by nucleic acids and, therefore, is able to inactivate microorganisms. This method also brings many advantages when compared with traditional disinfection methods. UVC disinfection can be performed by fixed equipments that have to be moved from place to place to disinfect an entire area, or by an autonomous mobile equipment that requires minimal human intervention to completely disinfect an environment. This dissertation is focused on mobile robots that disinfect an environment using UVC radiation. These mobile robots are able to move autonomously while mapping the surrounding environment and simultaneously disinfect it. The robots keep track of the dose applied to each area of the environment in order to build a dose map and differentiate areas that are completely disinfected from those that are not. This solution has the advantage of the robot performing UVC disinfection without needing to stop in each area nor having previous knowledge of the environment. The validation of this solution was performed using rviz, a Robot Operating System (ROS) tool, and the LiDAR Camera L515. The camera was used to capture the necessary information for creating the map of the environment and rviz was used to visualize the dose map

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Probabilistische Methoden für die Roboter-Navigation am Beispiel eines autonomen Shopping-Assistenten

Abstract Autonomous navigation, in addition to interaction, is a basic ability for the operation of a mobile service robot. Here, important subskills are selfocalization, path planning, and motion control with collision avoidance. A further pre-condition for many navigation tasks ist the generation of an environment model from sensor observationa, often in combination with autonomous exploration. In this thesis, these challenges are considered in the context of the development of an interactive mobile shopping guide, which is able to provide information about the shop's products to customers of a home improvement store and guide them to the respective location. The focus of this work lies on the initial environment mapping. A method for Simultaneous Localization and Mapping (SLAM) has been developed, which in contrast to other comparable approaches does not assume the use of high-precision laser range scanners. Instead, sonar range sensors are used mainly, which feature an inferior spatial resolution and increased measurement noise. The resulting Map-Match-SLAM algorithm is based on the well known Rao-Blackwellized Particle Filter (RBPF), in combination with local maps for representation of most recent observations and and a map matching function for comparison of local and global maps. By adding a memory-effcient global map representation and dynamic adaption of the number of particles, online mapping is possible even under high state uncertainty resulting from the sensor characteristics. The use of local maps for representation of the observations and the sensor-independent weighting function make Map-Match-SLAM applicable for a wide range of different sensors. This has been demonstrated by mapping with a stereo camera and with a single camera, in combination with a depth-from-motion algorithm for pre-processing. Furthermore, a SLAM assistant has been developed, which is generating direction hints for the human operator during the mapping phase, in order to ensure a route that enables optimal operation of the SLAM algorithm. The assistant represents an intermediate step between purely manual mapping and completely autonomous exploration. A second main part of the work presented here are methods for the autonomous operation of the robot. For selflocalization, a map matching approach with local maps is used, similar to the proposed SLAM algorithm. Improvements of robustness and precision are achieved in combination with an existing visual localization approach which is using omnidirectional camera images. Path planning is done by the utilization of standard graph search algorithms. To that purpose, the grid cells of the global map are regarded as graph nodes. Comparitive analysis is presented for search algorithms with and without heuristics (A*/Dijkstra algorithm), for the specifcs of typical operation areas. Two different algorithms have been developed for motion control and collision avoidance: A reactive method, which is an enhancement of the existing Vector Field Histogram (VFH) approach, is experimentally compared with a new anticipative method based on sampling and stochastic search in the trajectory space. All the developed methods are employed on a team of shopping robots, which have been in permanent public test operation in a home improvement store for six months currently. The description of navigation methods is complemented by an overview of further software componentsof the robots, e.g. for Human-Robot-Interaction, and a detailed description of the control architecture for coordination of the subsystems. Analysis of long term test operation proves that all the applied methods are suitable for real world applications and that the robot is accepted and regarded as a valuable service by the customers.Die autonome Navigation stellt neben der Interaktionsfähigkeit eine Grundlage für die Funktion eines mobilen Serviceroboters dar. Wichtige Teilleistungen sind dabei die Selbstlokalisation, die Pfadplanung und die Bewegungssteuerung unter Vermeidung von Kollisionen. Eine Voraussetzung für viele Navigationsaufgaben ist zudem die Erstellung eines Umgebungsmodells aus sensorischen Beobachtungen, unter Umständen in Verbindung mit einer selbständigen Exploration. Diese Teilprobleme wurden in der vorgelegten Arbeit vor dem Hintergrund der Entwicklung eines interaktiven mobilen Shopping-Lotsen bearbeitet, welcher Kunden eines Baumarktes Informationen zu Produkten zur Verfügung stellen und sie auf Wunsch zum Standort der gesuchten Waren führen kann. Den methodischen Kern der Arbeit bildet die initiale Umgebungskartierung. Dafür wurde ein Verfahren zum Simultaneous Localization and Mapping (SLAM) entwickelt, welches im Gegensatz zu vergleichbaren Ansätzen nicht auf den Einsatz hochgenauer Laser-Range-Scanner ausgerichtet ist. Stattdessen wurden hauptsächlich Sonar-Sensoren benutzt, die sich durch eine wesentlich geringere räumliche Auflösung und höhere Messunsicherheit auszeichnen. Der entwickelte Map-Match-SLAM-Algorithmus beruht auf dem bekannten Rao-Blackwellized Particle Filter (RBPF), welcher mit einer lokalen Karte zur Repräsentation der aktuellen Umgebungsbeobachtungen sowie einer Map-Matching-Methode zum Vergleich der lokalen und globalen Karte kombiniert wurde. Durch eine speichereffiziente Darstellung der globalen Karte und dynamische Adaption der Partikel-Anzahl ist trotz der aus den sensorischen Beschränkungen resultierenden großen Zustandsunsicherheit die Online-Kartierung möglich. Durch die Transformation der Beobachtungen in eine lokale Karte und die sensorunabhängige Bewertungsfunktion ist das Map-Match-SLAMVerfahren für ein breites Spektrum unterschiedlicher Sensoren geeignet. Dies wurde exemplarisch durch die Kartierung unter Nutzung einer Stereo-Kamera-Anordnung und einer einfachen Kamera in Verbindung mit einem Depth-from-Motion-Verfahren gezeigt. Aufbauend auf dem Kartierungsalgorithmus wurde zudem ein SLAM-Assistent entwickelt, welcher während der Kartierungsphase Aktionsvorschläge für den menschlichen Bediener präsentiert, die eine optimale Funktion des SLAM-Algorithmus gewährleisten. Der Assistent stellt damit eine Zwischenstufe zwischen rein manueller Steuerung und komplett autonomer Exploration dar. Einen weiteren Schwerpunkt der Arbeit stellen die Verfahren für die autonome Funktion des Roboters dar. Für die Selbstlokalisation wird ebenso wie beim SLAM ein Map Matching mit lokalen Karten eingesetzt. Eine Verbesserung der Robustheit und Genauigkeit wird durch die Kombination dieses Ansatzes mit einem vorhandenen visuellen Selbstlokalisations-Verfahren auf Basis einer omnidirektionalen Kamera erzielt. Für die Bestimmung des optimalen Pfades zu einem Zielpunkt kommen Standard-Algorithmen zur Pfadsuche in Graphen zum Einsatz, die Zellen der Karte werden dazu als Graphknoten interpretiert. Die Arbeit präsentiert vergleichende Untersuchungen zur Effizienz von Algorithmen mit und ohne Suchheuristik (A*/Dijkstra-Algorithmus) in der konkreten Einsatzumgebung. Für die Bewegungssteuerung und Kollisionsvermeidung wurden zwei verschiedene Algorithmen entwickelt: Einem reaktiven Verfahren, welches eine Weiterentwicklung des bekannten Vector Field Histogram (VFH) darstellt, wird ein neues antizipatives Verfahren auf Basis von Sampling und stochastischer Suche im Raum der möglichen Bewegungstrajektorien gegenüber gestellt und experimentell verglichen. Die entwickelten Methoden kommen auf mehreren Shopping-Robotern zum Einsatz, die sich seit ca. sechs Monaten im dauerhaften öffentlichen Testbetrieb in einem Baumarkt befinden. Neben den Navigationsmethoden gibt die Arbeit einen Überblick über die weiteren Module des Roboters, z.B. für die Nutzer-Interaktion, und beschreibt detailliert die Steuerarchitektur zur Koordinierung der Teilleistungen. Die Eignung aller eingesetzten Methoden für den Einsatz in einer realen Anwendung und die hohe Akzeptanz der Nutzer für das entwickelte Gesamtsystem werden durch die Auswertung von Langzeittests nachgewiesen

Advances in Gas Sensing and Mapping for Mobile Robotics

Esta tesis aborda el problema de la detección, cuantificación y mapeo de sustancias olorosas empleando un robot móvil equipado con una nariz electrónica. En robótica móvil se emplean los sistemas de muestreo abierto (Open Sampling Systems - OSS), los cuales están caracterizados por introducir importantes fuentes de incertidumbre en las medidas de gases obtenidas. Estas fuentes de incertidumbre se deben principalmente a los mecanismos de dispersión de los gases y al comportamiento dinámico de los sensores de gas, los cuales complican en gran medida las tareas de detección de gases con robots móviles. En esta tesis se proponen contribuciones en tres sub-áreas de la robótica móvil olfativa. Referente a la detección de sustancias olorosas en OSS, y especialmente enfocando a paliar el problema de la lenta recuperación de los sensores basados en tecnología de óxido de metal (Metal Oxide Semiconductor - MOX), se proponen dos contribuciones: un nuevo diseño de nariz electrónica (Multi Chamber Electronic Nose - MCE-nose) y un enfoque basado en el modelado dinámico de estos sensores. Referente a la cuantificación de gases, se propone un novedoso enfoque probabilístico el cual permite la estimación de la concentración del gas junto con su incertidumbre asociada, algo imprescindible para aplicaciones de robótica olfativa. Finalmente, relacionado con el estudio de la distribución espacial de los gases, esta tesis contribuye con la propuesta de un método probabilístico para la generación de mapas de gas. Este novedoso método permite, por primera vez, considerar tanto los obstáculos presentes en el entorno, como el envejecimiento (factor temporal) de las medidas de gas

Environment and task modeling of long-term-autonomous service robots

Utilizing service robots in real-world tasks can significantly improve efficiency, productivity, and safety in various fields such as healthcare, hospitality, and transportation. However, integrating these robots into complex, human-populated environments for continuous use is a significant challenge. A key potential for addressing this challenge lies in long-term modeling capabilities to navigate, understand, and proactively exploit these environments for increased safety and better task performance. For example, robots may use this long-term knowledge of human activity to avoid crowded spaces when navigating or improve their human-centric services. This thesis proposes comprehensive approaches to improve the mapping, localization, and task fulfillment capabilities of service robots by leveraging multi-modal sensor information and (long- term) environment modeling. Learned environmental dynamics are actively exploited to improve the task performance of service robots. As a first contribution, a new long-term-autonomous service robot is presented, designed for both inside and outside buildings. The multi-modal sensor information provided by the robot forms the basis for subsequent methods to model human-centric environments and human activity. It is shown that utilizing multi-modal data for localization and mapping improves long-term robustness and map quality. This especially applies to environments of varying types, i.e., mixed indoor and outdoor or small-scale and large-scale areas. Another essential contribution is a regression model for spatio-temporal prediction of human activity. The model is based on long-term observations of humans by a mobile robot. It is demonstrated that the proposed model can effectively represent the distribution of detected people resulting from moving robots and enables proactive navigation planning. Such model predictions are then used to adapt the robot’s behavior by synthesizing a modular task control model. A reactive executive system based on behavior trees is introduced, which actively triggers recovery behaviors in the event of faults to improve the long-term autonomy. By explicitly addressing failures of robot software components and more advanced problems, it is shown that errors can be solved and potential human helpers can be found efficiently