Kraftsensorlose Manipulator Kraftsteuerung zur Abtastung unbekannter, harter Oberflächen

Die vorliegende Arbeit zeigt ein Verfahren zur kraftgesteuerten Kontaktierung unbekannter harter Freiformflächen mit einem Standard–6DOF-Industriemanipulator (z.B. Manutec R2). Die bisher entwickelten Verfahren auf dem Gebiet der Manipulatorkraftregelung waren auf teure, fragile, mehrdimensionale Kraft-/Momentensensoren am Manipulator-Endeffektor angewiesen, die bei dem in dieser Arbeit entwickelten Ansatz der sensorlosen Kraft-/Geschwindigkeitsregelung überflüssig werden. Die Einstellung der gewünschten Kontaktkraft zu der unbekannten Umgebung erfolgt ausschließlich über eine robuste, beobachtergestützte Regelung der Motorströme der Gelenkantriebe. In freien Bewegungsphasen garantierte eine kaskadierte Kraft-/Geschwindigkeitsregelung vordefinierte Heranfahrgeschwindigkeiten an die unbekannte Kontaktoberfläche. Hierdurch eröffnen sich vollkommen neue Einsatzszenarien für die kraftkontrollierte Kontaktierung und Bearbeitung unbekannter Oberflächen oder Werkstücke beliebiger Härte und Steifigkeit

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Optimisation of docking locations for remotely operated vehicles.

This thesis describes work aimed at developing practical methods for determining the best docking locations for an underwater remotely operated vehicle (ROV) when inspecting an offshore platform. ROVs are used extensively in the offshore oil and gas industry to conduct a large variety of intervention tasks such as visual inspection, operational monitoring, equipment installation and operation, debris recovery, and so on. However, they have found only limited use in the more difficult tasks such as the detailed inspection of complex weld geometries. These complex welds are, however, found extensively in the construction of the majority of offshore structures and platforms ('oil rigs'). Furthermore, there is a safety requirement to have them inspected regularly since failure of these welds can potentially lead to catastrophic failure of the structures, the majority of which are manned. A number of specialist ROV systems have been developed that are able to attach onto platform structures and use their manipulators to conduct inspection. However, due to the short reach of the manipulators and the complex geometry of the welds (often encumbered with protruding pipes and other fittings) the success of any inspection is crucially dependent on a good initial choice of ROV docking position. This thesis will describe the problems and current manual planning methods, and then detail the development of two new methods for automated optimisation of docking positions - firstly using neural networks, and secondly using more conventional numerical processing. This thesis will also review related work in the field, such as the development of neural networks and their applications in the general offshore environment and in the control of ROVs and robot manipulator arms, and other approaches to ROV docking. It will further describe the use of the system developed here for planning docking positions on example commercial ROV inspection work programmes

On the development and enhancement of artificial intelligence algorithms for swarm robots in real world applications

Swarm robotics is an area where using artificial intelligence (AI) can show a great deal of improvement. Obstacle avoidance, object detection, mapping and navigation are some the major algorithms required for successful execution of various tasks in the field of robotics. There is a challenge in applying these algorithms in a manner that swarm robots can use effectively. These five areas can be further researched to provide a platform for real world applications. This research aims to tackle the challenges involved in applying the aforementioned algorithms to swarm robotics and comparing the results with single robot systems. These techniques can be optimized by leveraging the advantage of swarm robots communication and scalability. The proposed algorithms were tested and validated using swarm robots along with profiling and simulations. For obstacle avoidance, two algorithms were devoloped. The first used a novel and modified force field method and the second used artificial neural networks (ANN). The results showed that the modified force field method performed better for static environments while ANNs worked better for dynamic environments. For object detection, the proposed algorithm uses an image classifier developed using ANN. The image classifier was trained to identify blocks of various colours using a convolutional neural network technique. This algorithm was then applied to swarm robotics using two proposed methods and results showed that multiple robots viewing objects from different angles provided better results as compared to single robot systems. This was validated with a 97% accuracy. In two dimension (2D) mapping, the proposed algorithm was developed using simultaneous localization and mapping (SLAM). The results showed that a single robot can require upto 3.5x more time for covering a given area compared to a swarm size of ten robots. This research shows a great deal of contribution in applying swarm robotics for surveilance purposes by showcasing the ability for swarm robotics to coordinate and execute the required task in an efficient time frame. The proposed three-dimension (3D) mapping algorithm used octomaps and occupancy grids to map out an image taken from a camera mounted on swarm robots. The images were obtained from various angles using multiple swarm robots. AI algorithms with a focus on swarm robotics are developed and enhanced for real world applications including fire-fighting, surveillance, fault analysis and construction. Results showed that swarm robots were able to complete a given task by up to six times faster as compared to a single robot. The overall contribution of this research lays a platform for further applications by showcasing the effectiveness of robotic algorithms in a swarm robot environment.Heriot-Watt University Fee Scholarshi

Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors