585 research outputs found
Analysis, Design and Fabrication of Micromixers, Volume II
Micromixers are an important component in micrototal analysis systems and lab-on-a-chip platforms which are widely used for sample preparation and analysis, drug delivery, and biological and chemical synthesis. The Special Issue "Analysis, Design and Fabrication of Micromixers II" published in Micromachines covers new mechanisms, numerical and/or experimental mixing analysis, design, and fabrication of various micromixers. This reprint includes an editorial, two review papers, and eleven research papers reporting on five active and six passive micromixers. Three of the active micromixers have electrokinetic driving force, but the other two are activated by mechanical mechanism and acoustic streaming. Three studies employs non-Newtonian working fluids, one of which deals with nano-non-Newtonian fluids. Most of the cases investigated micromixer design
Exploring Robot Teleoperation in Virtual Reality
This thesis presents research on VR-based robot teleoperation with a focus on remote environment visualisation in virtual reality, the effects of remote environment reconstruction scale in virtual reality on the human-operator's ability to control the robot and human-operator's visual attention patterns when teleoperating a robot from virtual reality.
A VR-based robot teleoperation framework was developed, it is compatible with various robotic systems and cameras, allowing for teleoperation and supervised control with any ROS-compatible robot and visualisation of the environment through any ROS-compatible RGB and RGBD cameras. The framework includes mapping, segmentation, tactile exploration, and non-physically demanding VR interface navigation and controls through any Unity-compatible VR headset and controllers or haptic devices.
Point clouds are a common way to visualise remote environments in 3D, but they often have distortions and occlusions, making it difficult to accurately represent objects' textures. This can lead to poor decision-making during teleoperation if objects are inaccurately represented in the VR reconstruction. A study using an end-effector-mounted RGBD camera with OctoMap mapping of the remote environment was conducted to explore the remote environment with fewer point cloud distortions and occlusions while using a relatively small bandwidth. Additionally, a tactile exploration study proposed a novel method for visually presenting information about objects' materials in the VR interface, to improve the operator's decision-making and address the challenges of point cloud visualisation.
Two studies have been conducted to understand the effect of virtual world dynamic scaling on teleoperation flow. The first study investigated the use of rate mode control with constant and variable mapping of the operator's joystick position to the speed (rate) of the robot's end-effector, depending on the virtual world scale. The results showed that variable mapping allowed participants to teleoperate the robot more effectively but at the cost of increased perceived workload.
The second study compared how operators used a virtual world scale in supervised control, comparing the virtual world scale of participants at the beginning and end of a 3-day experiment. The results showed that as operators got better at the task they as a group used a different virtual world scale, and participants' prior video gaming experience also affected the virtual world scale chosen by operators.
Similarly, the human-operator's visual attention study has investigated how their visual attention changes as they become better at teleoperating a robot using the framework.
The results revealed the most important objects in the VR reconstructed remote environment as indicated by operators' visual attention patterns as well as their visual priorities shifts as they got better at teleoperating the robot. The study also demonstrated that operators’ prior video gaming experience affects their ability to teleoperate the robot and their visual attention behaviours
Designing a New Tactile Display Technology and its Disability Interactions
People with visual impairments have a strong desire for a refreshable tactile interface that can provide immediate access to full page of Braille and tactile graphics. Regrettably, existing devices come at a considerable expense and remain out of reach for many. The exorbitant costs associated with current tactile displays stem from their intricate design and the multitude of components needed for their construction. This underscores the pressing need for technological innovation that can enhance tactile displays, making them more accessible and available to individuals with visual impairments. This research thesis delves into the development of a novel tactile display technology known as Tacilia. This technology's necessity and prerequisites are informed by in-depth qualitative engagements with students who have visual impairments, alongside a systematic analysis of the prevailing architectures underpinning existing tactile display technologies. The evolution of Tacilia unfolds through iterative processes encompassing conceptualisation, prototyping, and evaluation. With Tacilia, three distinct products and interactive experiences are explored, empowering individuals to manually draw tactile graphics, generate digitally designed media through printing, and display these creations on a dynamic pin array display. This innovation underscores Tacilia's capability to streamline the creation of refreshable tactile displays, rendering them more fitting, usable, and economically viable for people with visual impairments
Data-driven Prediction of Internal Turbulences in Production Using Synthetic Data
Production planning and control are characterized by unplanned events or so-called turbulences. Turbulences can be external, originating outside the company (e.g., delayed delivery by a supplier), or internal, originating within the company (e.g., failures of production and intralogistics resources). Turbulences can have far-reaching consequences for companies and their customers, such as delivery delays due to process delays. For target-optimized handling of turbulences in production, forecasting methods incorporating process data in combination with the use of existing flexibility corridors of flexible production systems offer great potential. Probabilistic, data-driven forecasting methods allow determining the corresponding probabilities of potential turbulences. However, a parallel application of different forecasting methods is required to identify an appropriate one for the specific application. This requires a large database, which often is unavailable and, therefore, must be created first. A simulation-based approach to generate synthetic data is used and validated to create the necessary database of input parameters for the prediction of internal turbulences. To this end, a minimal system for conducting simulation experiments on turbulence scenarios was developed and implemented. A multi-method simulation of the minimal system synthetically generates the required process data, using agent-based modeling for the autonomously controlled system elements and event-based modeling for the stochastic turbulence events. Based on this generated synthetic data and the variation of the input parameters in the forecast, a comparative study of data-driven probabilistic forecasting methods was conducted using a data analytics tool. Forecasting methods of different types (including regression, Bayesian models, nonlinear models, decision trees, ensemble, deep learning) were analyzed in terms of prediction quality, standard deviation, and computation time. This resulted in the identification of appropriate forecasting methods, and required input parameters for the considered turbulences
Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning
Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts
A Common Digital Twin Platform for Education, Training and Collaboration
The world is in transition driven by digitalization; industrial companies and educational institutions are adopting Industry 4.0 and Education 4.0 technologies enabled by digitalization. Furthermore, digitalization and the availability of smart devices and virtual environments have evolved to pro- duce a generation of digital natives. These digital natives whose smart devices have surrounded them since birth have developed a new way to process information; instead of reading literature and writing essays, the digital native generation uses search engines, discussion forums, and on- line video content to study and learn. The evolved learning process of the digital native generation challenges the educational and industrial sectors to create natural training, learning, and collaboration environments for digital natives.
Digitalization provides the tools to overcome the aforementioned challenge; extended reality and digital twins enable high-level user interfaces that are natural for the digital natives and their interaction with physical devices. Simulated training and education environments enable a risk-free way of training safety aspects, programming, and controlling robots. To create a more realistic training environment, digital twins enable interfacing virtual and physical robots to train and learn on real devices utilizing the virtual environment. This thesis proposes a common digital twin platform for education, training, and collaboration. The proposed solution enables the teleoperation of physical robots from distant locations, enabling location and time-independent training and collaboration in robotics.
In addition to teleoperation, the proposed platform supports social communication, video streaming, and resource sharing for efficient collaboration and education. The proposed solution enables research collaboration in robotics by allowing collaborators to utilize each other’s equipment independent of the distance between the physical locations. Sharing of resources saves time and travel costs. Social communication provides the possibility to exchange ideas and discuss research. The students and trainees can utilize the platform to learn new skills in robotic programming, controlling, and safety aspects.
Cybersecurity is considered from the planning phase to the implementation phase. Only cybersecure methods, protocols, services, and components are used to implement the presented platform. Securing the low-level communication layer of the digital twins is essential to secure the safe teleoperation of the robots. Cybersecurity is the key enabler of the proposed platform, and after implementation, periodic vulnerability scans and updates enable maintaining cybersecurity. This thesis discusses solutions and methods for cyber securing an online digital twin platform.
In conclusion, the thesis presents a common digital twin platform for education, training, and collaboration. The presented solution is cybersecure and accessible using mobile devices. The proposed platform, digital twin, and extended reality user interfaces contribute to the transitions to Education 4.0 and Industry 4.0
経尿道的結石破砕術における精密レーザー照射のための形状記憶合金を用いた多方向屈曲デバイス
Tohoku University芳賀洋一課
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