## 5 Qualitative Analysis
### Automation in Industrial Collaborative Robotics Applications  
Automation in industrial collaborative robotics applications refers to the process of enabling collaborative robotic systems to perform tasks and operations autonomously, with minimal human intervention. It involves blending advanced technologies, such as digital twin software solutions, to streamline operations and enhance productivity in industrial settings. By harnessing the potential of advanced technologies, a Digital Twin-enabled framework can be created to automate processes, optimize performance, and enable proactive decision-making in industrial robot applications. For example, [PERSON] et al. developed task-level programming software that defines robot skills using kinesthetic teaching [73]. Their results indicated that personnel new to operating robots were quicker to adapt to their software solution in a manufacturing setting. A digital twin framework can benefit from similar programming software in preparing industrial operators for digital twin use. Programming software skills are important to create a digital twin in an industrial automation application. One of the goals of automation is to reduce the reliance on manual labor and repetitive tasks, allowing collaborative robots to handle routine operations independently [74]. [PERSON] and [PERSON] studied a case where the manual labor of assembling a battery pack was reduced by 50% using a digital twin of a human-robot collaboration cell [16]. This digital twin optimized the manufacturer's cell layout and collision avoidance, verified that their production rate could be maintained with reduced manual labor, and enabled more flexibility in their volume production. To achieve successful automation in industrial robot applications, several factors come into play. The design and implementation of digital twin software solutions tailored specifically for collaborative robots are crucial in enabling data transfer in the system, as demonstrated by [PERSON] et al. [75]. The available data types from the different sensors and other data acquisition sources on the robot did not natively match the data derived in the virtual environment, so a software solution, a custom Java application in this case, alleviates this constraint by handling and transforming the data for recognition at each end of the system. This is a likely scenario for industry, given the complex variety of data sources in each facility. These solutions should possess the necessary functionalities and capabilities to effectively monitor, analyze, and simulate collaborative robotic systems. Digital twin software solutions provide a digital representation of the physical robotic system, allowing for precise monitoring and control [76]. Through seamless data exchange between the digital twin and the physical robot, collaborative robotic systems can adapt to changing conditions, optimize their actions, and improve overall performance [77]. Effective communication and synchronization mechanisms are essential for seamless data exchange between the digital twin and the physical robot. This is the difference between a digital snapshot of the system at a point in time versus a live, dynamic model that always represents the system in real time. [PERSON] et al. indicated that the change of industrial network systems from legacy fieldbus to ethernet-based networks is indicative of the enhanced performance enabled by real-time data transmission [78]. This ensures real-time monitoring, coordination, and control, enabling the digital twin to accurately reflect the state of the physical system and facilitate optimal decision-making for automation. However, the Industrial Internet-of-Things (IIoT) trend may be a challenge to ethernet-based networks, trading improved infrastructure costs for latency due to wireless transmission [55]. Moreover, integrating artificial intelligence (AI) and machine learning (ML) algorithms into digital twin software solutions enhances automation capabilities.