Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.
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Supply chain management plays an essential role in our economy, as evidenced by recent COVID-19-induced supply chain challenges. Traditional supply chain management faces security and efficiency issues, but they can be addressed by leveraging digital twins and blockchain technology. The integration of blockchain technology can benefit the digital twins through improved security, traceability, transparency, and efficiency of digital twin data processing. A digital twin is an exact virtual representation of a physical asset, system, or process to synchronise data for the monitoring, simulation, and prediction of performance. Thus, the combination of blockchain and digital twins can refine the concepts of both technologies and reform supply chain management to advance into Industry 4.0. In this literature survey, we provide a comprehensive literature review of the blockchain-based digital twin solutions to optimise the processes of data management, data storage, and data sharing. We also investigate the key benefits of the integration of blockchain and digital twins and study their potential implementation in various processes of supply chains, including smart manufacturing, intelligent maintenance, and blockchain-based digital twin shop floor, warehouse, and logistics. This paper has implications for research and practice, which we detail in future research opportunities.
The control of pneumatically driven soft robots typically requires electronics. Microcontrollers are connected to power electronics that switch valves and pumps on and off. As a recent alternative, fluidic control methods have been introduced, in which soft digital logic gates permit multiple actuation states to be achieved in soft systems. Such systems have demonstrated autonomous behaviors without the use of electronics. However, fluidic controllers have required complex fabrication processes. To democratize the exploration of fluidic controllers, we developed tube-balloon logic circuitry, which consists of logic gates made from straws and balloons. Each tube-balloon logic device takes a novice five minutes to fabricate and costs $0.45. Tube-balloon logic devices can also operate at pressures of up to 200 kPa and oscillate at frequencies of up to 15 Hz. We configure the tube-balloon logic device as NOT-, NAND-, and NOR-gates and assemble them into a three-ring oscillator to demonstrate a vibrating sieve that separates sugar from rice. Because tube-balloon logic devices are low-cost, easy to fabricate, and their operating principle is simple, they are well suited for exploring fundamental concepts of fluidic control schemes while encouraging design inquiry for pneumatically driven soft robots
Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is inaccurate, unstable, and unfriendly to non-professional operators. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment operators are interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches.
In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human-robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a 6-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov based stability analysis is provided to prove both convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human-robot interactions for a kinematically redundant robotic manipulator.
It is imperative for all stakeholders that digital forensics investigations produce reliable results to ensure the field delivers a positive contribution to the pursuit of justice across the globe. Some aspects of these investigations are inevitably contingent on trust, however this is not always explicitly considered or critically evaluated. Erroneously treating features of the investigation as trusted can be enormously damaging to the overall reliability of an investigations findings as well as the confidence that external stakeholders can have in it. As an example, digital crime scenes can be manipulated by tampering with the digital artefacts left on devices, yet recent studies have shown that efforts to detect occurrences of this are rare and argue that this leaves digital forensics investigations vulnerable to accusations of inaccuracy. In this paper a new approach to digital forensics is considered based on the concept of Zero Trust, an increasingly popular design in network security. Zero Trust describes the practitioner mindset and principles upon which the reliance on trust in network components is eliminated in favour of dynamic verification of network interactions. An initial definition of Zero Trust Digital Forensics will be proposed and then a specific example considered showing how this strategy can be applied to digital forensic investigations to mitigate against the specific risk of evidence tampering. A definition of Zero Trust Digital Forensics is proposed, specifically that it is a strategy adopted by investigators whereby each aspect of an investigation is assumed to be unreliable until verified. A new principle will be introduced, namely the multifaceted verification of digital artefacts that can be used by practitioners who wish to adopt a Zero Trust Digital Forensics strategy during their investigations...
Future wireless services must be focused on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) that are challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of a digital twin has been recently proposed. A digital twin uses a virtual representation along with security-related technologies (e.g., blockchain), communication technologies (e.g., 6G), computing technologies (e.g., edge computing), and machine learning, so as to enable the smart applications. In this tutorial, we present a comprehensive overview on digital twins for wireless systems. First, we present an overview of fundamental concepts (i.e., design aspects, high-level architecture, and frameworks) of digital twin of wireless systems. Second, a comprehensive taxonomy is devised for both different aspects. These aspects are twins for wireless and wireless for twins. For the twins for wireless aspect, we consider parameters, such as twin objects design, prototyping, deployment trends, physical devices design, interface design, incentive mechanism, twins isolation, and decoupling. On the other hand, for wireless for twins, parameters such as, twin objects access aspects, security and privacy, and air interface design are considered. Finally, open research challenges and opportunities are presented along with causes and possible solutions.
A Virtual Reality-based Training and Assessment System for Bridge Inspectors with an Assistant Drone
Over 600,000 bridges in the U.S. must be inspected every two years to identify flaws, defects, or potential problems that may need follow-up maintenance. Bridge inspection has adopted unmanned aerial vehicles (or drones) for improving safety, efficiency, and cost-effectiveness. Although drones can operate in an autonomous mode, keeping inspectors in the loop is critical for complex tasks in bridge inspection. Therefore, inspectors need to develop the skill and confidence to operate drones in their jobs. This paper presents the design and development of a virtual reality-based training and assessment system for inspectors assisted by a drone in bridge inspection. The system is composed of four integrated modules: a simulated bridge inspection developed in Unity, an interface that allows a trainee to operate the drone in simulation using a remote controller, data monitoring and analysis to provide real-time, in-task feedback to trainees to assist their learning, and a post-study assessment supporting personalized training. The paper also conducts a proof-of-concept pilot study to illustrate the functionality of this system. The study demonstrated that TASBID, as a tool for the early-stage training, can objectively identify the training needs of individuals in detail and, further, help them develop the skill and confidence in collaborating with a drone in bridge inspection. The system has built a modeling and analysis platform for exploring advanced solutions to the human-drone cooperative inspection of civil infrastructure.
We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.
Serially connected robots are promising candidates for performing tasks in confined spaces such as search-and-rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve mobility. However, a challenge in designing and controlling such devices lies in the coordination of high-dimensional redundant modules in a way that improves mobility. Here we develop a general framework to control serially connected multi-legged robots. Specifically, we combine two approaches to build a general shape control scheme which can provide baseline patterns of self-deformation ("gaits") for effective locomotion in diverse robot morphologies. First, we take inspiration from a dimensionality reduction and a biological gait classification scheme to generate cyclic patterns of body deformation and foot lifting/lowering, which facilitate generation of arbitrary substrate contact patterns. Second, we use geometric mechanics methods to facilitates identification of optimal phasing of these undulations to maximize speed and/or stability. Our scheme allows the development of effective gaits in multi-legged robots locomoting on flat frictional terrain with diverse number of limbs (4, 6, 16, and even 0 limbs) and body actuation capabilities (including sidewinding gaits on limbless devices). By properly coordinating the body undulation and the leg placement, our framework combines the advantages of both limbless robots (modularity) and legged robots (mobility). We expect that our framework can provide general control schemes for the rapid deployment of general multi-legged robots, paving the ways toward machines that can traverse complex environments under real-life conditions.
This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.
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