Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Safe navigation and human-robot interaction in assistant robotic applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes

There is a paradigm shift in current manufacturing needs that is causing a change from the current mass-production-based approach to a mass customization approach where production volumes are smaller and more variable. Current processes are very adapted to the previous paradigm and lack the required flexibility to adapt to the new production needs. To solve this problem, an innovative industrial mobile manipulator is presented. The robot is equipped with a variety of sensors that allow it to perceive its surroundings and perform complex tasks in dynamic environments. Following the current needs of the industry, the robot is capable of autonomous navigation, safely avoiding obstacles. It is flexible enough to be able to perform a wide variety of tasks, being the change between tasks done easily thanks to skills-based programming and the ability to change tools autonomously. In addition, its security systems allow it to share the workspace with human operators. This prototype has been developed as part of THOMAS European project, and it has been tested and demonstrated in real-world manufacturing use cases.This research was funded by the EC research project “THOMAS—Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems” (Grant Agreement: 723616) (www.thomas-project.eu/)

A framework for the synergistic integration of fully autonomous ground vehicles with smart city

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This paper proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

Indoor and outdoor localization for AGVs in the primary aluminum industry

The goal of this project is to analyze two types of AGV indoor and outdoor localization techniques in an aluminum smelter building with a particular operative vehicle. The indoor localization is performed with the ARTag markers system while the outdoor localization employs multiple Wi-Fi transceivers to trilaterate the position of the vehicle based on the RSSI value. Finally, raw estimated pose is fused with the IMU sensor data using the extended kalman filter to increase localization accuracy.openEmbargo tempraneo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

Modeling Robotic Systems with Activity Flow Graphs

Autonomous robotic systems are becoming increasingly common in our society, with research efforts towards automated goods transportation, service robots and autonomous cars. These complex systems have to solve many different problems in order to function robustly. Two especially important areas of interest are perception and high level control. Intelligent systems have to perceive their surroundings in order to facilitate autonomy. With an understanding of the environment, they then can make their own decisions based on high level control policies defined by the developers. Robotic systems differ drastically in their sensory capabilities, their computational power, and their designated tasks. When developing algorithms, however, we need to have a common modeling framework that enables us to generalize and re-use existing solutions. A modular approach, which is coherent across different platforms, also allows faster prototyping of new systems. In this dissertation we develop a modeling framework based on data flow that achieves this goal. We first extend the existing Synchronous Data Flow (SDF) model and combine it with reactive programming ideas and finite-state machines. Together, these existing frameworks enable us to model many aspects of complex robotic systems. We apply this model to a robot in a warehouse scenario to demonstrate the viability of the approach. Using three disjoint formalisms to model a robotic system has many downsides. In a first unification step we merge SDF and reactive programming into Hybrid Flow Graphs (HFGs), where we explicitly model synchronous and asynchronous data flow. We then apply the HFG model to the perception system of an autonomous transportation robot. In a last step, we eliminate the need for separate finite-state machines by introducing the concept of activity into the data flow. We therefore unify the different aspects into a single and coherent framework which we call Activity Flow Graphs (AFGs). The flow of activity enables us to model high level state directly in the data flow graph. The result is a single computation graph that can express both perception and high level control aspects of any robotic system. We then demonstrate this with multiple high level robotic system models. Finally, we make use of the uniform AFG model to provide a single graphical user interface that allows a developer to rapidly prototype complete robotic systems. Since all aspects of a robot can be implemented using the same theoretical framework, there is no need to switch between different paradigms. The user interface is designed to give immediate feedback, which speeds up prototyping, testing and evaluation, as well as debugging when working with real robots.Autonome Roboter werden zunehmend zu einem wichtigen Bestandteil unserer Gesellschaft, in Bereichen wie dem automatisierten Gütertransport, in der Servicerobotik und bei autonomen Automobilen. Diese komplexen Systeme müssen viele Problem lösen, um robust zu funktionieren. Zwei sehr wichtige Anwendungsfelder sind die Umgebungswahrnehmung und die Ablaufplanung. Intelligente Systeme müssen ihre Umgebung wahrnehmen, um autonom agieren zu können. Mit einem Verständnis der Umwelt können sie Entscheidungen treffen, welche auf abstrakten Richtlinien der Entwickler basieren. Verschiedene Roboter weichen stark in ihren sensorischen Fähigkeiten, in ihrer Rechenleistung und in ihren zu lösenden Aufgaben voneinander ab. Bei der Entwicklung von Algorithmen wird jedoch ein einheitliches Modellierungssystem benötigt, welches die Wiederverwendung von existierenden Lösungen erlaubt. Ein modulares System, welches über mehrere Plattformen hinweg genutzt werden kann, ermöglicht eine schnellere Entwicklung von neuen Systemen. In dieser Dissertation wird ein auf Datenfluss basierendes Modell entwickelt, welches diese Anforderungen erfüllt. Zuerst wird das existierende Synchronous Data Flow (SDF) Modell erweitert und mit Elementen von reaktiver Programmierung und endlichen Zustandsautomaten kombiniert. Zusammen können so viele Aspekte von Robotern modelliert werden. Das Modell wird auf einen Roboter in einem Warenhausszenario angewandt, um den Ansatz zu validieren. Drei verschiedene Formalismen zur Modellierung von Robotern zu verwenden hat viele Nachteile. In einem ersten Vereinigungsschritt werden SDF und reaktive Programmierung zu hybriden Flussgraphen (HFG) kombiniert, bei denen synchroner und asynchroner Datenfluss explizit modelliert werden. Dann wird das HFG-Modell auf die Wahrnehmungsmodule eines autonomen Transportsystems angewandt. Anschließend wird der Bedarf eines Zustandsautomaten beseitigt, indem das Konzept der Aktivität in den Datenfluss eingeführt wird. Dadurch werden alle Aspekte in einem einzigen, schlüssigen System vereinigt, welches Aktivitätsflussgraph (AFG) genannt wird. Der Aktivitätsfluss ermöglicht es, den höheren Systemzustand direkt im Datenflussgraphen zu modellieren. Als Ergebnis erhalten wir einen einzigen Berechnungsgraphen, der sowohl zur Beschreibung der Umgebungswahrnehmung als auch zur Kontrolle der höheren Abläufe benutzt werden kann. Dies wird anhand mehrerer Robotersysteme demonstriert. Eine graphische Benutzerschnittstelle wird bereitgestellt, welche von dem einheitlichen Modell Gebrauch macht, um ein schnelles Prototyping von Robotern zu ermöglichen. Da alle Aspekte mit demselben System modelliert werden, muss nicht zwischen verschiedenen Paradigmen gewechselt werden. Die Nutzerschnittstelle erleichtert Entwicklung, Test und Validierung von Algorithmen sowie das Auffinden von Fehlern bei echten Robotern

Digital Twins for Industry 4.0 in the 6G Era

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios.Comment: Accepted for publication in IEEE Open Journal of Vehicular Technolog

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations

A Framework for the Synergistic Integration of Fully Autonomous Ground Vehicles With Smart City

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This article proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%