Robotic Automation Research Articles

Developing standard criteria for robotic process automation candidate process selection

<p>Robotic process automation (RPA) is a cutting-edge technology that provides software robots to repeat and mimic the repeatable tasks that a human user earlier performed. The use of software robots is encouraging because of their cost efficiency and easy implementation. Selecting and prioritizing a candidate process for automation is always challenging as all the business processes in an organization are not equally suitable for RPA implementation. Various studies have highlighted several criteria found in the literature for determining, prioritising, and selecting a business process for RPA. Nevertheless, there are no set standards for evaluating and analyzing a certain process or its tasks to determine whether they may be automated to use RPA. This paper aims to develop standard criteria and propose a consistent model to select and prioritize candidate process for RPA projects. To assess these criteria's applicability in the context of RPA, surveys among subject matter experts (SMEs) are used to validate them. Principal component analysis (PCA) and correlation are used to identify the top 20 criteria. Naïve Bayes algorithm is applied on the collected data for decision-making. The developed multi-criteria model exhibits strong precision and recall measures, with training and validation accuracy of 96% and 90%, respectively.</p>

Faculty Assistant Bot-automation of administrative activities using robotic process automation

Faculty Assistant Bot-automation of administrative activities using robotic process automation

Automation in financial reporting: A conceptual framework for efficiency and accuracy in U.S. corporations

This paper reviews the role of automation in financial reporting within U.S. corporations, focusing on how advanced technologies such as artificial intelligence (AI), robotic process automation (RPA), and cloud-based systems are transforming traditional financial processes. By automating routine tasks, these technologies offer significant improvements in efficiency, accuracy, and scalability. The paper highlights key challenges, including system integration, data security, and regulatory compliance, while also discussing strategies for successful implementation, such as establishing strong technological infrastructures, ensuring data integrity, and fostering organizational readiness. Best practices for maximizing the benefits of automation are explored, emphasizing the need for continuous improvement and alignment with business objectives. Recommendations are provided for corporations looking to adopt or enhance automation in financial reporting, with a focus on strategic planning, employee training, and maintaining compliance with financial regulations.

Robotic process automation – research impulses from the BPM 2023 panel discussion

AbstractRobotic Process Automation is an established technology in organizations. In the last years, it has also received considerable attention in scholarly research with publications, special issues, and academic conferences dedicated to the topic. Given that Robotic Process Automation has now moved beyond the initial hype, we can ask what research should focus on in the future. To address this question, we conducted a panel discussion to discuss its current state and future development. This panel, which took place at the Robotic Process Automation forum at the Business Process Management Conference 2023, included experts from academia and industry, covering strategy consultants, implementers, and tool providers. In this report, we present insights from the panel discussions. We especially focus on three future research directions on Robotic Process Automation that emerged from the panel.

Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

This research explores advanced computational intelligence techniques aimed at enhancing real-time decision-making in autonomous systems. The increasing reliance on autonomous technologies across sectors such as transportation, healthcare, and industrial automation demands robust, adaptive, and reliable decision-making frameworks. This study introduces a novel hybrid model that integrates Reinforcement Learning (RL), Deep Neural Networks (DNN), and Fuzzy Logic to enable autonomous systems to make accurate and timely decisions in complex, dynamic environments. The proposed framework leverages RL for adaptive decision-making, DNNs for pattern recognition and prediction, and Fuzzy Logic for handling uncertainty in system states. Experimental evaluations were conducted using high-fidelity simulations across three scenarios: autonomous vehicle navigation, real-time patient monitoring in healthcare, and robotic process automation. Results indicate a 25% improvement in decision accuracy, a 30% reduction in response time, and enhanced robustness against environmental variability compared to conventional decision-making methods. The findings underscore the effectiveness of computational intelligence in supporting critical decisions in real-time, marking a significant step toward more capable and reliable autonomous systems.

Rancang Bangun Lengan Robot Berbasis Arduino Nano

Technological advances in the Industrial Revolution 4.0 era have had a big impact, especially in the industrial sector with the emergence of automatic robots which help increase efficiency and productivity. This research discusses the design of an Arduino Nano-based robot arm that is controlled using a joystick system. The objective of this research is to establish a robotic arm mechanical system that is responsive to the movement of the joystick and sevo motor as a driver of the closed feedback system and to identify the parameters for the success of the hard ware and software system in moving objects. The research stages used involve design analysis, device assembly, and system programming. The research results show that the developed robot arm is able to effectively move objects with success parameters including the type of shape and physical structure of the object being lifted. This research found that objects with simple shapes such as blocks have a higher level of ease of lifting, while soft physical structures also influence ease of lifting.

The Convergence of SAP PP, RPA, and IBP: A Comprehensive Analysis of Integration Benefits and Challenges in Modern Manufacturing

The integration of SAP Production Planning (PP) with Robotic Process Automation (RPA) and SAP Integrated Business Planning (IBP) represents a significant advancement in manufacturing and supply chain management. This article examines the synergies, challenges, and potential benefits of combining these technologies in modern production environments. Through a comprehensive analysis of existing literature, case studies, and industry best practices, we propose a framework for seamless integration that enhances operational efficiency, improves forecast accuracy, and enables real-time decision-making. Our findings reveal that successfully implementing this integrated approach can substantially improve production planning, resource allocation, and overall supply chain performance. However, organizations face notable challenges regarding technical complexity, data synchronization, and change management. This article contributes to the growing body of knowledge on digital transformation in manufacturing by providing actionable insights and recommendations for practitioners. Furthermore, it identifies key areas for future research, including the role of artificial intelligence and machine learning in further optimizing these integrated systems.

A Process Analysis Framework to Adopt Intelligent Robotic Process Automation (IRPA) in Supply Chains

Intelligent Robotic Process Automation (IRPA) combines Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate complex unstructured tasks, improve decision-making, and cope with changing scenarios. A process analysis framework for IRPA adoption was developed by identifying key factors through a literature review and semi-structured expert opinion survey. The employed experts in the survey comprised RPA/IRPA consultants, RPA/IRPA initiative team leaders, and RPA/IRPA developers with three years or more experience. For the initial factor collection phase, there were a total of eighteen (18) responses, and for the factor evaluation phase, a total of twenty-six (26) experts were used to collect responses. Identified factors were shortlisted and evaluated using a Relative Importance Index (RII) analysis. The study’s findings are presented through a Causal-Loop Diagram (CLD) to illustrate the relationships between factors. The framework provides practical guidance for organizations planning to adopt IRPA, informing decision-making, resource allocation, and strategy development. The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption. Three major secondary factors were identified: digital data input, integration with existing systems, and the cost of adopting new technologies. This research contributes to the added value to existing knowledge and serves as a foundation for future research in IRPA adoption.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Configuring social assistance: conceptualizations and implications of the adoption of robotic process automation in the Swedish personal social services

ABSTRACT Many European countries’ public organisations have turned to digital automation as a strategy to enhance the efficiency of their service delivery. One example of this is the use of Robotic Process Automation (RPA) in the provision of Social Assistance (SA) in Sweden. Swedish SA is a means-tested subsidy intended as a last resort for the economically most vulnerable persons, and caseworkers exercise a high degree of discretion when deciding on clients’ eligibility for the benefit. Based on semi-structured interviews with stakeholders at the national and local levels, this study explores how these actors conceptualise and rationalise the adoption of RPA in the SA context and discusses how this relates to the mutual configuration of RPA and SA. The results show that RPA is framed as a simple technology to alleviate caseworkers’ administrative workload so that they can instead focus on supporting clients towards self-sufficiency. However, local actors describe RPA adoption as a challenging process with modest outcomes. As such, the study suggests that stakeholders’ simplified conceptualisation of RPA allows them to shift responsibility for its practical adoption to local PSS organisations, which ultimately pushes the PSS organisations towards configuring their SA casework processes around the limited capabilities of the technology.

A novel trajectory planning method for mobile robotic grinding wind turbine blade

As the core component of wind turbine, wind turbine blade requires two grinding processes in the production. However, mobile robotic automation grinding of wind turbine blades is considered to be a challenging task due to the high aspect ratio and compound surface of the wind turbine blade. The trajectories generated by most robotic grinding trajectory planning algorithms are often found to be inferior in grinding large compound surface workpieces, as they are typically designed for robotic machining with a fixed base. In this paper, a novel iso-planar algorithm based on oriented bounding box (OBB) of the workpiece is developed to plan the grinding trajectories by taking into consideration the characteristics of blade. The constant chord length algorithm employing Taylor quadratic expansion is then developed to discretize trajectory into grinding points. Considering the characteristics of compound surface, a post-processing strategy is proposed to eliminate redundant grinding points and generate consistent tool orientations on compound surface. Based on these three steps, a workstation location optimization model for improving robot manipulability is introduced to determine a series of workstation locations. Furthermore, the grinding and movement synchronization strategy based on mobile platform trajectory interpolation is proposed to enhance the efficiency of machining large workpieces. The simulation and experiments demonstrate the effectiveness of the proposed trajectory planning method for mobile robotic grinding wind turbine blade, the rationality of the optimization model and the feasibility of grinding and movement synchronization strategy.

Perceived Effects of Robotic Process Automation on Work Tasks and Processes: A Study of an IT Company in the Republic of Serbia

Perceived Effects of Robotic Process Automation on Work Tasks and Processes: A Study of an IT Company in the Republic of Serbia

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

A programmable platform for probing cell migration and proliferation.

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot® (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of in vitro migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and "hands-free," schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.

Development of an automated process for monitoring of amiodarone treatment

Abstract Background Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to risk of side effects during long-term administration. Robotic process automation (RPA) represents the use of software robots to automate repetitive and routine tasks typically performed by humans. Purpose We aimed to develop an RPA using a diagnostic classification algorithm and clinical support system for follow-up of amiodarone treatment. Methods Based on international guidelines and clinical specialist advisory we constructed an RPA which was studied in a test environment in parallel with routine amiodarone treatment. Expert advice was integrated through a series of collaborative workshops, ensuring alignment of the algorithm with current best practices in thyroid and liver disease management. The RPA provided recommendations on the time interval to next laboratory testing and suggestions for patient management to the physician, who was serving as a human-in-the-loop responsible for clinical decisions. Results After iterative improvements the RPA was evaluated against physician orders (n=390 paired orders) without technical failures. The recommended time interval to next follow-up was (mean ± SD) 4.5 ± 2.4 vs. 3.1 ± 1.4 months for RPA vs. physician (p<0.0001). While normal laboratory findings resulted in recommended follow-up in 6 months by the RPA (72.2%) this was only ordered in 9.9% by the physician (Figure). Physicians’ orders clustered between 3-4 months (58.5%). All patients diagnosed with new side effects (n=12) were detected and communicated correctly by the RPA, whereas only 8 were correctly detected and responded to by the physician (Table). Conclusions An automated process constitutes a reliable alternative to the current fully manual management of amiodarone follow-up. It may reduce manual labour, frequency of laboratory testing and detect side effects with increased precision, thereby reducing costs and enhancing patient value. While the RPA shows promise, further research is needed to explore its adaptability to other treatment scenarios.

The Role of Automation in Enhancing Cybersecurity: a Technical Analysis

Intelligent automation, a convergence of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is reshaping modern business operations across various sectors. This article examines the transformative impact of intelligent automation on organizational efficiency, cost structures, and innovation capabilities. Recent industry surveys indicate a significant increase in adoption rates, with 67% of business leaders implementing intelligent automation at scale in 2023, up from 48% in 2020. The integration of AI and ML with RPA has enabled businesses to automate complex, cognitive tasks, leading to an average 35% reduction in operational costs and a 50% improvement in process cycle times. While challenges such as data privacy, ethical considerations, and workforce reskilling persist, the potential benefits of intelligent automation are substantial. This article explores quantifiable impacts across industries, examines real-world case studies, and anticipates future trends including hyperautomation, autonomous decision-making, and integration with emerging technologies like blockchain and edge computing. The article concludes that strategic implementation of intelligent automation solutions will be crucial for organizations to maintain competitiveness and drive innovation in the increasingly digital business landscape.

Optimizing Structured Data Processing Through Robotic Process Automation

Optimizing Structured Data Processing Through Robotic Process Automation

Impact of Artificial Intelligence on Auditing and the Future of Human Workforce Replacement

The advancements in artificial intelligence (AI) over the recent years have been drastic and stunning, and they have revolutionized the world in such ways as influencing the advancement of other fields such as accounting. This research paper aims to study the deep influence of Artificial Intelligence (AI) on the auditing profession. They plan to assess the strengths and the existing applications to auditing: machine learning, natural language processing, deep learning, and robotic process automation. These technologies allow the auditors to identify issues that they were previously unable to, or it would have taken them so much of their time to be able to locate. The paper also brings out the various risks related to the application of AI to auditing tasks. Some challenges include data quality and its privacy, ethical dilemmas, opaqueness, and regulatory matters on the use of Artificial Intelligence. Also, the soundness of AI choices and the ability to explain such decisions remain an issue, especially with complex financial data that depend on human perception and experience. Additionally, the paper also examines the possibility of AI imposing to replace human auditors in the future while comparing the technological aspects of the audit together with the humanistic aspects.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

Framework to enable and test conversational assistant for APIs and RPAs

AbstractIn the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.