ABSTRACT
The COVID-19 pandemic has emphasized the need for non-contact medical robots to alleviate the heavy workload and emotional stress experienced by healthcare professionals while preventing infection. In response, we propose a non-contact robotic diagnostic system for otolaryngology clinics, utilizing a digital twin model for initial design optimization. The system employs a master-slave robot architecture, with the slave robot comprising a flexible endoscope manipulation robot and a parallel robot arm for controlling additional medical instruments. The novel 4 degrees of freedom (DOF) control mechanism enables the single robotic arm to handle the endoscope, facilitating the process compared to the traditional two-handed approach. Phantom experiments were conducted to evaluate the effectiveness of the proposed flexible endoscope manipulation system in terms of diagnosis completion time, NASA task load index (NASA-TLX), and subjective risk score. The results demonstrate the system's usability and its potential to alternate conventional diagnosis. © 2013 IEEE.
ABSTRACT
The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self-driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self-driving labs enable access to a larger part of the chemical universe and reduce the time-to-solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data-centric ion to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self-driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self-driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.
ABSTRACT
Industrial automation has become increasingly more prominent in many industries, such as manufacturing, automotive, pharmaceuticals, and food processing industries, as the technology evolves and Industry 4.0 revolution advances. Th demand of automation and personnel with automation skills has ever been increasing since Covid-19 Pandemic. Industrial robots and machine vision inspection are essential systems for manufacturing automation. Industrial robots are capable of performing various tasks like part handling, machine tending, assembly, palletizing, arc welding, or laser cutting with high speeds, repeatability and accuracy. Machine Vision Inspection (MVI) systems are used for part quality inspection, manufacturing and assembly supervision and robot guidance. A MVI system integrated with an industrial robot provides a hand-eye coordination to the robot for flexible material handling and operations. Vision-guided robotics serves as the next-generation research instrument that opens new opportunities to advance the boundaries in science and engineering research. This paper focuses on teaching industrial robot programming to engineering students using an offline virtual robotic simulation software, Fanuc ROBOGUIDE and iRVision software. Using a virtual robot and offline programming with ROBOGUIDE reduces a risk by enabling visualization of the robot operations before an actual installation and operations. The ROBOGUIDE software will provide students with an experience of programming an industrial robot and will enhance the effectiveness of the teaching and learning process. The developed programs can be imported and implemented onto a real robot with a minimum configuration setup. The step by step approach of developing and programming a 2D vision guided material handling cell using ROBOGUIDE has been discussed in the paper such that other educators and students can learn and implement the project with ease. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.
ABSTRACT
In the context of the decrease in the number of industrial workers and the increase in labor costs, industrial robots have developed rapidly due to their many advantages. Especially after the COVID-19 epidemics, enterprises have accelerated the upgrade of robots intellectually. The quantity of china's industrial robots grew by 20% in 2020. The 2021 year's growth will reach 21%. At the same time, in the face of the global energy crisis, power rationing, energy conservation & emission reduction, the energy savings of robots are also inevitable. Under the same starting point and ending point, the energy consumption of different motion trajectory planning is very different. Based on the current mainstream industrial robot trajectory planning methods, this paper gives the trajectory algorithm formulas and optimizes them, and then combines the Lagrangian-Euler dynamics formula to derive the energy consumption formula. By simulating mainstream 6DOF manipulator robots, set the same starting point and ending point in the MatLab environment, testing different trajectories of various methods, planning and computing time-consuming, and energy consumption of the entire trajectory. The experimental results demonstrate that the energy consumption of the shortest path method is 1.4 times that of the quartic polynomial method, and the planning time is more than 800 times that of the quartic polynomial method. The energy consumption of the cubic Bezier curve method is 8.08 times that of the quartic polynomial method, and the planning time is 781 times that of the quartic polynomial method. The energy consumption of the seventh-degree polynomial method is 1.6 times that of the fourth-degree polynomial method, and the planning time is 1.28 times that of the quartic polynomial method. The time and energy consumption of the quartic polynomial and quantic polynomial methods are almost the same. Relatively speaking, the quartic polynomial interpolation method is better than the quintic polynomial. © 2022 IEEE.
ABSTRACT
In recent years, domestic robot industry is faced with a huge opportunity as well as severe challenges due to the four factors of age of a society, great power competition, COVID-19 and industrial upgrading. From the perspective of three elements of both pricing logic and promotion of industrial robot industry -economy, technology, talent and policy, taking EFORT industrial robots as an example, this paper analyzes the shortcomings and deficiencies in the current situation and the future development trend of industrial robot development, therefore finds the pain points of industrial robot enterprise development and makes a plan for the development of EFORT intelligent industrial robots. This paper summarizes the progress of EFORT intelligent robots and the training of robot application talents from the aspects of technology research and development, new application scenario development, business model innovation and the integration of the industrial chain and the education chain in. This paper has certain reference value for the current strategic decision-making of domestic robot industry enterprises. © 2022 SPIE.
ABSTRACT
The pedagogy of a first-year engineering course in manufacturing is presented. This course entitled Manufacturing and Society involves collaboration with social science, is based on industrial robots as the central theme to attract students’ interests and utilizes the flipped classroom approach for delivery. We hypothesize that, in one semester, recent high school graduates will be able to gain knowledge in manufacturing by learning the computer-aided engineering (CAD) software, applying CAD to design a penholder, fabricating the penholder using additive manufacturing and computer-aided manufacturing (CAM) software, programming the robot to create a toolpath for the pen, drawing using the pen on the penholder guided by a robot, and elaborating on impacts of robotic painting on society from a social science perspective. This course is designed to give students, regardless of their intended major in engineering, broad knowledge in manufacturing via 10 engineering, 3 social science, and 10 technical communication lectures;8 labs;and 4 projects. The social science lectures and discussions focus on how knowledge about society can be used to inform design and manufacturing decisions, social science research methods for understanding how engineers and technology can impact people's lives, and changing trends in work, the workplace, and the future workforce as it relates to manufacturing. This course aimed to give undergraduate first-year engineering students a positive view of advanced manufacturing and its impact on society. Student evaluations and comments were positive and affirmed the learning objective of teaching manufacturing to the first-year engineering students. The flipped classroom approach was demonstrated to be ideal during the COVID-19 pandemic with limited capacity for in-person lectures and labs. The use of flipped classrooms allowed students to learn at their own pace, review and reinforce knowledge, have a closer interaction with instructors, and reduce the number of technical errors using simulation tools. This course with the support of flipped classroom pedagogy can be successfully implemented in the post-pandemic era, devoting the time of the class to answer questions, expand upon the class content and have a closer in-person interaction with students. © 2022
ABSTRACT
This paper describes key technologies for the use of the six-legged walking system Mantis in a multi robot team performing cooperative tasks for the construction of an In-Situ Resource Utilization (ISRU) facility on the Moon. Autonomous multi robot cooperation is one of several key technologies that hold promise for In-Situ space exploration and ISRU facility construction. Therefore, the PRO-ACT (Planetary RObots deployed for Assembly and Construction Tasks) project aimed to develop a multi robot team which can act (semi-) autonomous. PRO-ACT is a European project with many partners working on space robotics technologies, the so-called building blocks. These building blocks have been implemented and extended on Mantis to cover the scope of multi-robot cooperative scenarios. To allow the different partners to execute commands without considering the robot's control framework, a communication interface was developed to provide a common and generic way to send commands and receive sensor data. It facilitates access to all robots and to their simulated counterparts. Due to the impact of COVID-19, most of the testing, including the final demonstration, was performed remotely, with robots available at the partners' premises. © 2022 IEEE.
ABSTRACT
In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.
ABSTRACT
Newtechnologies,includingIndustry4.0,arerapidlychangingtraditionalandhigh-techindustries, andformingtheadvancedmanufacturingsectorwithintheindustrialcomplex.BynowRussiahasbeencharacterized by a modest presence in the world advanced manufacturing markets – Russia’ share is less than 0.6% in certain markets and less than 0.4% in global advanced manufacturing. This is partially explained by the scarce commodity range in the Russian export basket, which determines weak representation of Industry 4.0 goods. Large part in the Russian advanced manufacturing exports is traditional for the Russian economy goods — airplanes, turbojet engines, fuel rods. However, we observe positive structural changes in Russian exports caused by the COVID-19 pandemic. Russia has increased exports of goods with bio- and additive technologies, optoelectronics. It seems that the entry to new advanced manufacturing markets for Russia can form the basis for long-term growth. International experience shows that advanced manufacturing exports are often supported by advanced manufacturing imports. We find that Russian economy significantly underutilizes this channel. Although Russian advanced manufacturing imports are gradually growing, it is focused primarily on final consumer goods. We consider industrial robots as a case of advanced manufacturing imports that deserves special attention as a widespread cross-cutting technology that can significantly transform the technological level of industries. Our estimates show that, although the robotics market in Russia is relatively small and has low growth rates relatively to investment in fixed assets, companies importing industrial robots are larger and more productive. This evidence allows us to determine the import of industrial robotics as one of the priority directions of modernization of the Russian manufacturing. Based on the analysis we identify and discuss three growth opportunities for Russia in global advanced manufacturing: 1) support of exports of services in optoelectronics and ICT through the use of accumulated human capital and competencies and taking into account fast transformation of business models in industry, 2) support of exports of biotechnology products, taking into account positive reputational effects and expanding Russian pharmaceutical exports under the COVID-19 pandemic;3) support of technology companies in wide number of advanced manufacturing taking into account current favorable environment for the birth of tech startups in Russia. © 2022 New Economic Association. All rights reserved.
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Purpose>This paper aims to provide details of recent commercial and technological developments that are driving robotic warehouse automation.Design/methodology/approach>Following a short introduction, this first provides a commercial background and identifies the factors driving the market growth. It then gives examples of robotics companies, products and applications that exploit innovations in artificial intelligence (AI). It then considers future prospects, and finally, brief conclusions are drawn.Findings>Amazon’s acquisition of Kiva led to a community of new robot manufacturers and the realisation by major e-commerce companies that robotic automation would be required to maintain competitiveness. The Covid pandemic caused a surge in e-commerce and a critical shortage of labour, which further highlighted the need for automation and boosted robotic deployments. Recent advances in AI have resulted in a rapidly growing community of companies producing AI-powered robots which offer advanced capabilities such as mixed product picking, sorting and kitting. These are being deployed by a growing number of e-commerce and logistics companies and are paving the way towards ever-higher levels of warehouse automation. Full automation will soon become a reality.Originality/value>This paper identifies the factors driving the rapidly developing warehouse robot business by considering the companies, products, technology and applications.
ABSTRACT
Exploring planets requires cooperative robotics technologies that make it possible to act independently of human influence. So-called multi-robot teams, consisting of different and synchronized robots, can solve problems that cannot be handled by a single robot. The PRO-ACT (Planetary RObots deployed for Assembly and Construction Tasks) project aimed to develop and demonstrate key technologies for robot collaboration in the construction of future ISRU (In-Situ Resource Utilization) facilities on the Moon. To this end, the following robots were used: Veles-a rover with six wheels and a 7-DoF (Degree of Freedom) arm, Mantis-a six-legged walking system, and a mobile gantry that can be used for payload manipulation or 3D printing. The project further developed existing software and hardware developed in previous space robotics projects and integrated them into the robotic systems involved. The software enables collaborative tasks such as transportation, mapping and navigation. Due to the Covid-19 situation, the final demonstration was performed remotely for defined mission scenarios. The intensive remote test campaigns provided valuable lessons learned that are directly applicable to future space missions. In addition, PRO-ACT opens a new way for multi-robot collaboration. The paper describes the developed robotic software and hardware as well as the final mission scenarios performed in lunar analogues with Mantis tested in the test field with granules in the DFKI Space Hall in Bremen, Germany, with Veles tested in Warsaw, Poland and with the mobile gantry tested in Elgoibar, Spain. In addition one mission scenario, manipulation tasks with two robotic systems, was performed with two Panda robotic arms in Toulouse, France. The paper concludes with the results of the final demonstration of the multi-robotics team. © 2021 International Astronautical Federation, IAF. All rights reserved.
ABSTRACT
Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation.
ABSTRACT
With the occurrence of the COVID-19 pandemic, many health workers in hospitals, nursing homes and quarantine facilities were put at increased health risk in their workplace. One effective solution to reduce this risk is to use Telerobotics which enables health workers to carry out their tasks remotely. Such a system must be able to perform a wide range of tasks in an unpredictable environment. This research paper will focus on the pick and place of small objects, which is one of the most common tasks in health care settings. This research designs a prototype telerobotic system using an innovative and new control method to remotely pick and place small objects and will perform the following test: An operator will use a virtual reality headset to remotely control a robot arm located more than 44 kilometres away from the operator and pick several common everyday objects and place them into a container. By developing this proof of concept prototype, this research will help to accelerate the adoption of telerobotic technologies in health care and other industries. © 2021 Australasian Robotics and Automation Association. All rights reserved.
ABSTRACT
JD.com pioneered same-day delivery as a standard service in China's business-to-consumer e-commerce sector in 2010. To balance the urgent need to meet growing demands while maintaining high-quality logistics services, the company built intelligent warehouses that use analytics to significantly improve warehouse efficiency. The brain of the intelligent warehouse system is the dispatching algorithm for storage rack-moving robots, which makes real-time dispatching decisions among robots, racks, and workstations after solving large-scale integer programs in seconds. The intelligent warehouse technology has helped the company decrease its fulfillment expense ratio to a world-leading level of 6.5%. The construction of intelligent warehouses has led to estimated annual savings of hundreds of millions of dollars. In 2020, JD.com delivered 90% of its first-party-owned retail orders on the same day or on the day after the order was placed. The agility of such intelligent warehouses has allowed JD.com to handle 10 times the normal volume of orders during peak sales seasons and has also helped the company respond quickly to COVID-19 and ensure the rapid recovery of production capabilities.