Robot Software Research Articles

Robotics Software: Past, Present, and Future

Robotics is powered by software. Software tools control the rate of innovation in robotics research, drive the growth of the robotics industry, and power the education of future innovators and developers. Nearly 900,000 open-source repositories on GitHub are tagged with the keyword robotics—a potentially vast resource, but only a fraction of those are truly accessible in terms of quality, licensability, understandability, and total cost of ownership. The challenge is to match this resource to the needs of students, researchers, and companies to power cutting-edge research and real-world industrial solutions. This article reviews software tools for robotics, including both those created by the community at large and those created by the authors, as well as their impact on education, research, and industry.

Robotic Process Automation Based Data Extraction from Handwritten Medical Forms.

This paper proposes to create an RPA(robotic process automation) based software robot that can digitalize and extract data from handwritten medical forms. The RPA robot uses a taxonomy that is specific for the medical form and associates the extracted data with the taxonomy. This is accomplished using UiPath studio to create the robot, Google Cloud Vision OCR(optical character recognition) to create the DOM (digital object model) file and UiPath machine learning (ML) API to extract the data from the medical form. Due to the fact that the medical form is in a non-standard format a data extraction template had to be applied. After the extraction process the data can be saved into databases or into a spreadsheets.

Security Automation in Application Development Using Robotic Process Automation (RPA)

In the contemporary digital landscape, the rapid advancement of application development necessitates a parallel evolution in security measures. Traditional security practices often struggle to keep pace with the dynamic nature of modern software development. To address these challenges, Robotic Process Automation (RPA) emerges as a transformative tool, offering substantial potential to automate security tasks within the application development lifecycle. This paper explores the integration of RPA into security automation, focusing on its capabilities to enhance security protocols, minimize human error, and streamline processes across various stages of development. Security automation through RPA involves the deployment of software robots to perform repetitive, rule-based tasks that are essential to maintaining and enforcing security standards. These tasks include automated code scanning, vulnerability assessments, compliance checks, and incident response, all of which are crucial to the development and deployment of secure applications. By automating these processes, organizations can significantly reduce the time and resources required to secure applications, ensuring that security measures are not compromised by the speed and agility demanded by modern development practices.

An Effective Method for Constructing a Robot Operating System Node Knowledge Graph Based on Open-Source Robotics Repositories

Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes.

Robotic process automation using process mining — A systematic literature review

Process mining (PM) aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. PM is seen as one way to address such difficulty. This paper aims to assess the applicability of process mining in accelerating and improving the implementation of RPA, along with the challenges encountered throughout project lifecycle.A systematic literature review was conducted to examine the approaches where PM techniques were used to understand the as-is processes that can be automated with software robots. Seven databases were used to identify papers on this topic. A total of 32 papers, all published since 2018, were selected from 605 unique candidate papers and then analyzed.There is a steady increase in the number of publications in this domain, especially during the year 2022, which suggests a raising interest in the combined use of PM with RPA. The literature mainly focuses on the methods to record the events that occur at the level of user interactions with the application, and on the preprocessing methods that are needed to discover routines with the steps that can be automated. Important challenges are faced with preprocessing such event logs, and many lifecycle steps of automation projects are weakly supported by existing approaches suggesting corresponding research areas in need of further attention.

From Hype to Reality: Navigating the Challenges of RPA Implementation

The term Robotic Process Automation (RPA) was first utilised in 2012 (Hindle, 2018). The term began to gain traction in 2014 when businesses started to announce major savings because of automation. In early 2016, RPA back office automation became more significant but during this period it was still relatively limited (Willcocks & Lacity, 2016). From 2016 to 2017 the global RPA market increased by 64% (Fersht, 2017). They also reported a 42% increase in the global market for RPA between 2017 and 2018 and an anticipated increase of a further 94% between 2018 and 2021 (Fersht, 2017). The demand for RPA has increased using AI and software robots are expected to be the key drivers of growth. RPA continues to gather prominence throughout Europe as companies are eager to improve customer experience and to offer a streamlined service particularly for the financial services industry (Information Services Group, Inc., 2018). The key objective is to improve organizational performance and the customers’ experience using an RPA solution. The RPA solution will enable the organization to provide around the clock robot assistance to the client, enhancing effectiveness and efficiency of the compliance process, faster return on investment (ROI), reduced processing time as well as helping to meet deadlines, improved quality with reduced human errors (Pattfaut and Borghoff, 2022). However, RPA is an extensive process and requires expertise in this space, structured input, and governance to achieve optimal implementation. The research objective of this investigation therefore is to explore methods for automating business processes while reducing operating costs and improving efficiency. In doing so, three research questions are presented: 1) What are the key phases in an RPA implementation?: 2) What activities are involved in each of the phases?; and 3) What should be the focus of each of these activities?

Cloud BPM Application (Appian) Robotic Process Automation Capabilities

Cloud Business Process Management (BPM) platforms, such as Appian, have gained significant traction in recent years due to their ability to streamline and automate business processes. One key feature that has revolutionized process automation is Robotic Process Automation (RPA). RPA enables the creation of software robots that mimic human actions and interact with various applications, systems, and data sources to perform repetitive and rule-based tasks.This abstract focuses on the RPA capabilities within the Appian platform and its impact on business process automation. Appian's RPA capabilities provide organizations with the ability to integrate and orchestrate human-centric and robot-centric tasks within their business processes, resulting in improved efficiency, accuracy, and productivity.By leveraging Appian's RPA capabilities, organizations can automate manual, time-consuming tasks, such as data entry, data validation, and data extraction, by creating software robots that execute these tasks with speed and precision. The platform offers a user-friendly interface for designing, deploying, and managing software robots, enabling both technical and non-technical users to participate in the automation journey. Appian’s RPA capabilities also facilitate seamless integration with existing systems and applications, allowing organizations to leverage their investments in legacy systems while enhancing their automation capabilities. Through APIs and connectors, software robots can interact with databases, web services, enterprise applications, and other sources of data, enabling end-to-end process automation. Additionally, the Appian platform provides comprehensive monitoring, reporting, and analytics capabilities for RPA. Organizations can track and measure the performance of software robots, identify bottlenecks, and gain insights into process efficiency and effectiveness. This data-driven approach allows for continuous improvement and optimization of automated processes. Furthermore, the cloud-based nature of the Appian platform offers scalability, flexibility, and accessibility. Organizations can rapidly scale their RPA deployments based on business needs, leverage cloud resources for increased computing power, and enable access to software robots from any device and location. In conclusion, Appian's RPA capabilities within the Cloud BPM application offer organizations the opportunity to achieve significant advancements in process automation. By leveraging software robots to perform repetitive tasks, organizations can enhance operational efficiency, reduce errors, and allocate resources to more value-added activities. The abstract highlights the key features and benefits of Appian's RPA capabilities, setting the stage for further exploration and understanding of how this technology can revolutionize business process management.

Development of a Human-Robot Interaction System for Industrial Applications

The use of robots is increasing day by day. In this study, it was aimed to develop manufacturing-assistant robot software for small production plants involving non-mass production. The main purpose of this study is to eliminate the difficulty of recruiting an expert robot operator thanks to the developed software and to facilitate the use of robots for non-experts. The developed software consists of three parts: the convolutional neural network (CNN), process selection-trajectory generation, and trajectory regulation modules. Before the operations in these modules are executed, operators record the desired process and the trajectory of the process in the video by hand gestures and index finger. Then recorded video is separated into images. The separated images are classified by the CNN module and the positions of landmarks (joint and fingernail of index finger) were calculated by the same module and using the images. Eight different pre-trained CNN structures were tested in the CNN module, and the best result Xception structure (test loss = 0.0051) was used. The desired process was determined and the trajectory of the process was created with the CNN output data. The connection of the generated trajectory with the object was detected by the trajectory regulation module, and unnecessary trajectory parts were cleaned. Regulated trajectory and desired tasks such as welding or sealing were simulated via an industrial robot in a simulation environment. As a result, an industrial robot could be programmed by non-expert operators for companies whose production line is not standard by using the developed software.

Significance and Impact of Artificial Intelligence and Immersive Technologies in the field of Education

Teachers are active ingredients of educational system. Pandemic made us aware of online learning. Teachers improvising the learners to add values to educational behavior and attitude. Education never ages, the importance of its lifelong learning everywhere at every time. Artificial intelligence (AI) is a broad context, helps to develop educational strategies for present day scenario. It allows educators to benchmark and develop critical analysis to redesign educational policies for the implementation of innovative learning and teaching strategies in educational institutions. AI helps in educational transformation and accelerating basic educational skills by introducing software robots implemented in classrooms and other devices to make reminders for essential educational activities and assignments. In European countries they are utilizing teaching assistants as robots paired with Augmented Reality (AR) and Virtual Reality (VR) system and now they are providing MR (Mixed reality) altogether called Immersive Technologies promotes lifelong learning capabilities which makes learners engaged in creation, active learning collaboration, problem solving and makes learning as a real life experience in all educational perspectives.

A Clustering Extension of HUEPs for the Analysis of Performance Anomalies in Robots

Errors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely, Hybrid Unsupervised Exploratory Plots (HUEPs) are extended through density-based clustering techniques, that are applied together with unsupervised exploratory projection models. As a result, intuitive and informative visualizations of software performance are obtained, supporting the monitoring and anomaly detection tasks. The proposed clustering extension of HUEPs is thoroughly validated on a massive and up-to-date open dataset, obtaining promising results.

Process Optimization Using RPA as an Element of change Management in the Organization

Robotic Process Automation (RPA) technology is being used more and more often in small and medium enterprises. This tool is becoming increasingly important in relieving workers from time consuming and boring tasks. The aim of the research was to identify current problems encountered by enterprises when implementing RPA solutions, with particular attention paid to the aspect of change perceived by the employees and the management. As part of the empirical work, monographic research was conducted using the case study procedure in 10 selected small and medium-sized companies operating in Poland. During expert interviews, workers’ resistance against changes was noticed as the greatest obstacle in introducing RPA. Workers’ lack of knowledge about this technology and its tools and the lack of proper change management in organizations are the main factors which impede implementation of RPA. The core actions to success in implementation of software robots are: building awareness among workers, support of leaders, and monitoring of implementation progress.

Advancements in Robotic Systems and Human Robot Interaction for Industry 4.0

Robotic systems are software and algorithms used to mechanize iterative human processes. Robotic Process Automation (RPA) operates based on simple principles and business logic, enabling it to engage with various information systems by using pre-existing graphical user interfaces. The process is the use of non-invasive software robots, often referred to as “bots,” to automate actions that are repetitive in nature and governed by predefined rules. The integration of data analytics, artificial intelligence (AI), process mining, and cognitive computing is now being used to expand the capabilities of RPA, enabling it to do more intricate jobs. This study investigates the progress made in robotic systems and the interaction between humans and robots in Industry 4.0 context. The paper examines the use of RPA, the incorporation of AI into robotic systems, and the advancement of autonomous driving and mobile robots. The study also emphasizes the significance of efficient human-robot interaction strategies and the possible influence of artificial intelligence (AI) on the prospective progress of intelligent and independent service robots. Furthermore, this study delves into the obstacles and forecasts pertaining to the development of sophisticated machine intelligence.

Prospects for digitalization and robotization of the economy of Kazakhstan

A key factor in the development of Kazakhstan’s economy is the digital transformation of manufacturing enterprises, the use of digital information and communication technologies to restructure the business so that all decisions are taken on the basis of data. The structure of digital transformation is presented. End-to-end digital technologies are used to collect, store, process, search, and transmit data electronically. The proliferation of IoT technologies and the use of data from IoT devices to improve automated solutions and optimize industrial production plays an important role in the transition to digital production. The most important task of modern business models is to create an omnichannel space, synchronizing data and information in all digital and physical interaction channels to meet the needs of customers at any time and in any place. The prospects for the development of digitalization and robotization of our economy are related to the development and implementation of robotic means. An important area of development of digitalization and robotization of the economy of our republic is the development and application of software robot managers. The structure of the software robot manager is given. It is noted that digitalization and robotization technologies will provide unique opportunities for Kazakh enterprises, including high accuracy of forecasting and making management decisions based on data, multiple cost reductions, providing a better quality “customer experience”.

Digitalization of Accounting: Implementation Features and Efficiency Assessment

The digitalization of accounting processes offers significant benefits, including increased efficiency and reduced errors in accounting data. However, the successful implementation of new and modernization of existing computer programs in the accounting system requires consideration of cost, technical, professional, and managerial factors. This study aims to provide a comprehensive analysis of qualitative and quantitative parameters that can form the basis for making decisions on the integration of automated accounting data processing, robotic process automation, artificial intelligence, blockchain, or their combinations into existing systems for the full digitalization of accounting. The integration of software robots and artificial intelligence into accounting systems offers various benefits related to the creation of a non-standard environment where the generation, transfer, and exchange of traditional accounting information is accelerated and improved through the integration of data from other management systems and databases. These improvements contribute to the efficiency and reliability of the automated accounting system. The introduction of software components such as robotic process automation and artificial intelligence helps to free accounting staff from the routine of repetitive operations and ensures automatic prioritization and sequencing of accounting registration in the digital space. Integration of bots into an automated accounting system also facilitates document sharing and provides remote automated monitoring. This increases interaction between departments and improves control over operations that require additional attention from managers.To assess the effectiveness of such software solutions, the authors critically evaluate existing approaches to determining the qualitative indicators that are expected to be obtained after the introduction of software innovations and propose to supplement the performance criteria with quantitative indicators, among which are the time-saving ratio in document processing, the error rate and the resource-saving ratio. In conclusion, this study substantiates the need to consider quantitative parameters and coefficients that can be used to assess the effectiveness of implementing software solutions in automated accounting systems for the digitalization of financial information. The results emphasize the need for a holistic approach to assessing the effectiveness, reliability, and security of these solutions. Improved technical knowledge and training of accounting professionals in security assessment are crucial for the successful implementation and use of digitalization of accounting systems.

Performance Improvement of Multi-Robot Data Transmission in Aggregated Robot Processing Architecture with Caches and QoS Balancing Optimization

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish–subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

Modeling variability in self-adapting robotic systems

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects.

Security by envelopment – a novel approach to data-security-oriented configuration of lightweight-automation systems

ABSTRACT Organisations’ increasing adoption of lightweight automation, such as robotic process automation (RPA), raises concerns about the associated systems’ robustness and security, with data-security concerns becoming further accentuated when tools of this sort are deployed for handling of potentially sensitive data. However, literature on designing these tools in a manner mitigating risks related to organisational data security has remained scarce. This paper addresses this gap by presenting a study in which RPA was successfully designed for a process wherein the software robot handles sensitive personal data. Informed by work on the mindlessness of automation, sociotechnical envelopment, and security by design, this empirical study, employing action design research at Wärtsilä Corporation, pointed to three design principles, related to envelopment, access rights, and audit trails. By adhering to these, Wärtsilä created envelopes around the robot that afford the automation’s safe operation and processing of the sensitive data. This research advances the theory of sociotechnical envelopment’s design and deployment by introducing a novel approach in security by envelopment to elaborate on the security-oriented envelopment of mindless automation agents. The paper also discusses the practical utility of the artefact designed, in terms of both design and evaluation.

Unmanned aerial vehicle navigation in underground structure inspection: A review

Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

Survey Paper on Development of ROS for Fault Detection of Underwater Cables

An introduction to ROS, an open source robot operating system, is given in this paper. In terms of process management and scheduling, ROS is not an operating system. This study explains how Robotic Operating System (ROS) can be used to control items (such as vehicles) remotely and cautiously without human intervention at the location. Instead, it gives heterogeneous computing clusters a structured communication layer on top of the host operating system. This document gives a quick explanation of how ROS fits into the current robot software architecture and how we may utilize it for AUVs (Automated under water vehicle). One of the media that connects the entire world to the internet is optical cable, which is typically installed underground or under water. As a result, it is challenging to inspect them thoroughly because it costs more to do so. To address this issue, we are presenting a solution that involves developing a robotic operating system that would assist in checking the underwater/underground cables. To put this into practice, we have been utilizing VMWare Workstation to virtually install Ubuntu OS, where we will be installing ROS packages, with the ROS-Gazebo toolbox serving as one of the primary tools. We are testing the implemented software with the standard inputs. We are using light radiation as the primary factor to assess the condition of the optical cable.

Robotics Process Automation (RPA) and Project Risk Management

Abstract
 The Robotics process is a technological software program to improve digital technology systems. The robotic process identifies the factors to process data in a digital security system to mitigate risk in managing projects. Atisalat is a renowned company that successfully utilizes a robotic process in their service, and they are the pioneer in using the robotic system in UAE. By adopting the process, they have facilitated the digital model development objective of UAE through VISION 2021.
 Digital transformation in project management is analyzed to change adaptability and shift of cultural processes both in adopting new technologies and project management. The robotic process also helps Etisalat to secure the position of the 4th strongest brand in the world. Technological innovation changes the system of network channels. The company implemented Pega centralized decision engine through the robotic system for implementing the best-personalized action and to address the gap in professional communication. The software robots used by the company support to make the back-office task 70% faster than before with accuracy. The robotic process enhances telecommunication systems and also develops the industry related to telecommunication. Technology adaptation through the introduction of an RPA system improves customer experience and serves excellence to get revolutionized with AI. Society also gets accustomed to using technology for fun.