Robotic Automation Research Articles

Revolutionizing Healthcare: A Prototype Serving Robot for Enhanced Medical Assistance

This paper delves into the transformative potential of service robots in healthcare, addressing the growing need for innovative solutions. We examine the collaborative efforts between academia and the healthcare industry to develop and deploy service robots. The focus is on preventing infection spread, reducing human error, and optimizing patient care. Additionally, the paper explores the contemporary field of Robotic Process Automation (RPA) and its applications in relieving mundane tasks within the healthcare industry. The interdisciplinary analysis provides valuable insights into reshaping healthcare practices through robotic innovations

MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration

Abstract 3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.

Increasing Trends of Artificial Intelligence With Robotic Process Automation in Health Care: A Narrative Review.

This review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary sourcejournals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.

Intelligent automation of financial and accounting processes in business process outsourcing centers in Poland

Purpose: The aim of the article is to assess the degree of intelligent automation of financial and accounting processes in services centres that operate in the business process outsourcing (BPO) market in Poland and to identify future trends and prospects.Methodology/approach: The main research data comprise various reports on the modern business services sector in Poland for 2017–2023. The methods used in the research include a literature review, case study analysis, and deductive reasoning.Findings: The research results have shown that the financial and accounting process outsourcing market in Poland is changing. There has been a gradual advancement of services provided by BPO, shared services centres (SSC) and global business services (GBS) centres. An undoubted driver of the change is technology, which brings trans-formative potential. This technology is changing the nature of work – from repetitive work to work that requires in-depth analysis and complex problem-solving. There is an increasing tendency to implement intelligent process automation (IPA), which includes, among other things, robotic process automation (RPA) solutions using artificial intelligence (AI), in service centres. The increasing automation of repetitive financial and accounting processes is driven by the need to optimize costs, increase the speed of service and improve the quality of services provided by outsourcing centres.Research limitations/implications: The conclusions presented in the article are important for the development and improvement of financial and accounting processes. They are of cognitive value and can be analyzed by decision-makers at various management levels in service centres in Poland and worldwide.Originality/value: The article narrows the research gap in the field of intelligent automation of financial and accounting processes. The research is part of the trend to continuously improve business processes. The conclusions can be a starting point for future research conducted from a macroeconomic perspective, with a particular focus on qualitative research.

Regulating learning module for patient monitoring interactive event detecting robots

Process automation robots in smart healthcare services are reliable in providing interactive patient services. The patient requirements are identified and approached through automated learning and responses. A Regulating Learning Interaction Module (RLIM) using Sigmoid Optimized Neural Networks is designed for robots in such sensitive environments. This learning module trains the robots for various services/ user interactions over patient demands. In particular, the patient interacting features on monitoring and responses/ event triggering are induced through the learning. The miscommunicated interactions are streamlined using sigmoid optimization from the conditional neural layers for assessment. This assessment delivers the possible choices to improve event detection/ response delivery. The neural network regulates the training patterns from the user inputs, histories, and patient diagnosis data for which the events are detected. This event relates the patient with the diagnosis, assistance, and monitoring for better assistance. The performance of this proposed module is validated using interaction detection, response precision, event generation rate, and miscommunication.

Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision

Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as “I=AXB.” To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.

Effect of Artificial Intelligence (AI) on Financial Decision-Making: Mediating Role of Financial Technologies (Fin-Tech)

The main objective of the current study is to shed light on the mediating effect of financial technology (Fin-Tech) on the relationship between artificial intelligence (encompassing natural language processing (NLP), machine learning algorithms, computer vision, predictive analytics, robotic process automation (RPA), blockchain technology, and deep learning) and financial decision-making from the perspective of financial managers within Jordan's commercial banking sector. Realizing this objective required the use of quantitative methodology. A questionnaire was self-administered by 86 financial managers in the Jordanian banking sector. Primary data was analyzed using AMOS. Results of analysis confirmed that FinTech plays a significant mediating role between AI applications and financial decision-making. Machine learning was identified as the most impactful AI technique, facilitating more informed decisions through advanced data analysis and pattern recognition beyond the scope of traditional analysis methods. The novelty of current research is in the fact that it offers valuable insights into the intersection of AI and Fin-Tech within the Jordanian financial sector. It contributes to the understanding of how advanced AI techniques can enhance financial decision-making, emphasizing the importance of multidisciplinary expertise in the development of AI-driven financial systems. The findings have significant implications for both theoretical understanding and practical application in the finance industry. Doi: 10.28991/HIJ-2024-05-03-015 Full Text: PDF

Towards discovering erratic behavior in robotic process automation with statistical process control

Towards discovering erratic behavior in robotic process automation with statistical process control

How does robotic process automation create value for firms?

AbstractRPA’s potential for efficiency improvement highly recommends the analysis of its applicability to the organizations’ processes. This paper analyses the value of RPA as its impact on the main activity groups in the value chain: Procurement, Internal Operations and Marketing and Sales. The analysis of RPA’s contribution for each of the activity groups addresses a research question through an exploratory approach: “How does RPA create value for organizations and which RPA features are critical for such value creation?”. The data was collected through interviews, analyzed using content analysis, and shows that RPA has impacts on both downstream and internal dimensions. Regarding upstream dimension further research is required to ascertain RPA’s potential.

About managing the number of simultaneously functioning software robots of different types

Objectives. The study sets out to justify the relevance and investigate approaches for solving the problem of managing the number of simultaneously functioning software robots of various types under conditions of limited computational resources and changes in sets of executable tasks.Methods. A proposed solution is based on models and methods of scenario management, linear programming, inventory management, queuing theory, and machine learning. The described methods are valid for different compositions and preconditions for generating initial data, as well as ensuring the relevance horizons of the obtained solutions.Results. The initial data is obtained via the presented approach for determining the computational resource parameters for operating a single software robot. The resources are determined by analyzing the composition of the software and information services used by an actual software robot. Problem statements and mathematical models are developed for cases involving scenario management and linear programming methods. Methods for real-time management of the number of software robots and their sequential local optimization are proposed based on the abovementioned solution sequences. The developed method for generating statistical data based the results of applying the sequential local optimization method is used to identify deficient and non-deficient computational resources. Some results of working in the multi-functional center of RTU MIREA software robots developed on the Аtоm.RITA platform are outlined.Conclusions. The emerging problem of managing the number of simultaneously operating software robots of various types for cases involving scenario control methods and linear programming is formalized. This problem is relevant in the field of automation of business processes of organizations. The use of mathematical methods for solving this problem opens up opportunities for expanding the functional capabilities of robotic process automation platforms, as well as increasing their economic efficiency to create competitive advantages by optimizing the use of IT infrastructure components.

EA-CTFVS: An Environment-Agnostic Coarse-to-Fine Visual Servoing Method for Sub-Millimeter-Accurate Assembly

Peg-in-hole assembly, a crucial component of robotic automation in manufacturing, continues to pose challenges due to its strict tolerance requirements. To date, most conventional peg-in-hole assembly algorithms have been validated only within simulated environments or under limited observational scenarios. In this paper, an environment-agnostic coarse-to-fine visual servoing (EA-CTFVS) assembly algorithm is proposed. Firstly, to solve the frequent issue of visual blindness during visual servoing, a bottleneck pose is proposed to be used as the desired pose for the visual servoing. Secondly, to achieve accurate assembly, a coarse-to-fine framework is constructed, in which a rough pose is given by the coarse controller to remove large initial alignment errors. For the fine controller, a twin network-based fine controller is provided to improve assembly accuracy. Furthermore, EA-CTFVS utilizes the Oriented Bounding Box (OBB) of objects as the input for visual servoing, which guarantees the system’s ability to operate effectively in diverse and complex scenes. The proposed EA-CTFVS achieves a successful assembly rate of 0.92/0.89 for initial alignment errors of 15/30 cm and 0.6 mm tolerance in real-world D-sub plug assembly tasks under complex scenarios.

Acceptance of Rpa in Public Sector Institutions

ABSTRACT Due to the increasing demands for modern communication channels and the economical fulfillment of public tasks, innovative technologies are needed to improve the efficiency and interoperability of existing IT infrastructures in public institutions. Robotic process automation (RPA) is a promising technology that has the potential to pragmatically automate administrative processes in public institutions, thereby providing much-needed work load reduction for public employees. However, any RPA project will not be successful if the relevant stakeholders’ acceptance of the new technology is insufficient. Until today, research did not address which factors influence the acceptance of RPA in public sector institutions. A relevant question nonetheless, as the peculiarities of the public sector in the operation of processes are well known. Therefore, this study examines the drivers of RPA acceptance and usage behavior in public sector institutions. As a result of a grounded theory study, a theoretical framework with factors for evaluating RPA user acceptance is developed and an understanding of its facilitators is gained. The findings also provide practical recommendations for guiding RPA implementation in public sector institutions.

Towards robotic laboratory automation plug & play: Reference architecture model for robot integration

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

Robot material processing and hardware-in-the-loop-based real-time simulations

Abstract This paper presents a cyber-physical production system that consists of a simulation, an industrial robot cell, and sensors. The industrial robot hardware, used for welding and additive manufacturing applications, is connected or “in-the-loop” with a real-time target machine on which simulations are running. These simulations are updated in real-time by the data provided by process sensors. Particular focus is given to wire-arc additive manufacturing (WAAM). Still, the cyber-physical system allows use in other robot-based material processes, such as sheet forming, (dis)assembly and material handling applications. It is also argued that the proposed cyber-physical system can be used so that it competes against the concept of using machine learning to optimize manufacturing processes. The proposed cyber-physical system enables the transition from traditional robot automation to autonomous robot systems.

Depth Image Assisted Aiming for Scoring Goal in Wheeled Soccer Robot

Wheeled soccer robot, as an automatic robot, required to have an advanced decision making system based on information it grasp from its surrounding. One of the most crucial decision making ability is to determine aiming angle when it is scoring goal.This research will enhance the aiming ability for scoring goal by predicting unguarded area of goal. Combination of depth image and RGB image information will be used to predict the position of unguarded space in goal. This position will be converted into aiming angle for the robot. Intel Realsense D435i depth camera will be used to get RGB and depth image simultaneouslyBy using this method, the system is capable to predict unguarded area in all of 60 test points, with 1.3% average error for the predicted coordinate.

Direct Screening of PET Hydrolase Activity in Culture Medium Based on Turbidity Reduction.

The development of an efficient screening method for the activity of PET-degrading enzymes represents a significant technological advance in the field of enzyme research, with the potential to facilitate the advancement of enzymes for PET recycling. By examining the stable conditions of PET suspension and enzyme production conditions, we developed a method to quantify PET-degrading enzyme activity in E. coli culture medium using turbidity reduction as an indicator. High PET concentration or ionic strength caused aggregation of PET, and the best condition for activity detection was 0.5 mg mL-1 PET in 50 mM sodium phosphate pH 7.0. Preculture of E. coli increased the purity of enzyme secreted in medium. To evaluate the screening method, 720 colonies of the PET2-7M-H229X-F233X mutant library were analyzed and three candidates of high-activity mutants were obtained. The thermostability of the mutants could also be easily measured by measuring the residual activity after heat treatment. The H229T-F233M mutant showed 3.4 times higher degradation rate against PET film than the template enzyme at the initial time. The molecular dynamics simulation implied that the F233M mutation makes space for making an α helix and that the H229T mutation resolved the steric hindrance with Trp199. These mutations were speculated to change the angle of the Trp199 side chain of PET2 to an angle similar to that of the Trp185 of IsPETase, making it suitable for PET binding to the active center. Screening of activity using PET suspensions is compatible with robotic automation and is expected to be useful for validating computationally predicted mutations.

Advances in 2D Material Transfer Systems for van der Waals Heterostructure Assembly

The assembly of van der Waals (vdW) heterostructures using 2D material transfer systems has revolutionized the field of materials science, enabling the development of novel electronic and optoelectronic devices and the probing of emergent phenomena. The innovative vertical stacking methods enabled by these 2D material transfer systems are central to constructing complex devices, which are often challenging to achieve with traditional bottom-up nanofabrication techniques. Over the past decade, vdW heterostructures have unlocked numerous applications leading to the development of advanced devices, such as transistors, photodetectors, solar cells, and sensors. However, achieving consistent performance remains challenging due to variations in transfer processes, contamination, and the handling of air-sensitive materials, among other factors. Several of these challenges can be addressed through careful design considerations of transfer systems and through innovative modifications. This mini-review critically examines the current state of transfer systems, focusing on their design, cost-effectiveness, and operational efficiency. Special emphasis is placed on low-cost systems and glovebox integration essential for handling air-sensitive materials. We highlight recent advancements in transfer systems, including the integration of cleanroom environments within gloveboxes and the advent of robotic automation. Finally, we discuss ongoing challenges and the necessity for further innovations to achieve reliable, cleaner, and scalable vdW technologies for future applications.

Bot log mining: An approach to the integrated analysis of Robotic Process Automation and process mining

Process mining and Robotic Process Automation (RPA) are two technologies of great interest in research and practice. Process mining uses event logs as input, but much of the information available about processes is not yet considered since the data is outside the scope of ordinary event logs. RPA technology can automate tasks by using bots, and the executed steps can be recorded, which could be a valuable data source for process mining. With the use of RPA technology expected to grow, an integrated view of steps performed by bots in business processes is needed. In process mining, various techniques to analyze processes have already been developed. Most RPA software also includes basic measures to monitor bot performance. However, the isolated use of bot-related or process mining measures does not provide an end-to-end view of bot-enabled business processes. To address these issues, we develop an approach that enables using RPA logs for process mining and propose tailored measures to analyze merged bot and process logs. We use the design science research process to structure our work and evaluate the approach by conducting a total of 14 interviews with experts from industry and research. We also implement a software prototype and test it on real-world and artificial data. This approach contributes to prescriptive knowledge by providing a concept on how to use bot logs for process mining and brings the research streams of RPA and process mining further together. It provides new data that expands the possibilities of existing process mining techniques in research and practice, and it enables new analyses that can observe bot-human interaction and show the effects of bots on business processes.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

Purpose Gas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks. Design/methodology/approach GBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device. Findings The robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate. Practical implications The robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields. Originality/value The robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

Navigating the digital frontier: transformative technologies reshaping public administration

We are witnessing unprecedented, swift transformations across all industries in the current era. Advancements in information and communication technology have provided us with a tremendous opportunity for change. Our research delves into the profound impacts of digital transformation, extending beyond the public sector to encompass a variety of fields. The public sector, in particular, is experiencing its own set of transformations and changes. Our research offers an in-depth analysis of digital transformation in the public sector, with an emphasis on the key technologies driving this evolution. These include artificial intelligence and machine learning, the Internet of Things (IoT), cloud computing, big data and analytics, digital twins, robotic process automation, augmented reality, and the metaverse. We thoroughly conceptualize these technologies, assessing their relevance and impact on the public sector. Moreover, we identify the potential challenges and present diverse global examples. Our findings reveal that technologies such as artificial intelligence and machine learning (including Generative AI, ChatGPT, and Google Gemini/Bard), IoT, cloud computing, big data and analytics, digital twins, robotic automation, and augmented reality are pivotal in enhancing or creating new value propositions. These technologies have shown remarkable efficacy in elevating the quality of services between individuals and governmental entities, reducing burdens, boosting efficiency, and profoundly transforming public administration and political science. Moreover, aligning these technologies with institutional requirements is essential for successful transformation. Nevertheless, it is noted that these technologies will also refine the competencies of human resources in public administration. Implemented practices reveal that civil servants need higher expertise and improve their skill sets compared to past benchmarks. Based on these insights, we advocate for higher education institutions and other bodies responsible for public sector human resource development to integrate more practical and theoretical technology-focused courses into their curricula. A comprehensive review of the existing literature indicates a scarcity of studies that holistically investigate these technologies and assess them through the lens of field experts, researchers, and policymakers. Our study addresses this gap and contributes significantly to understanding these technologies’ transformative potential.