A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework’s versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

Digital twin (DT) technology has become a cornerstone in the simulation and analysis of real-world systems, offering unparalleled insights into the lifecycle management of physical assets. By providing a real-time synchronized replica of the physical entity, DTs enable predictive maintenance, performance optimization, and lifecycle extension, which are pivotal for industries aiming for digital transformation. This paper presents a comprehensive comparative study of DT development of a robotic arm using two prominent simulation platforms: Unity and Gazebo. Unity, with its roots in the gaming industry, offers robust real-time rendering and a user-friendly interface, making it a versatile choice for various industries. Gazebo, traditionally used in robotics, provides detailed physics simulations and sensor data emulation, which is ideal for precise engineering applications. We explored the performance of both platforms in creating accurate and dynamic digital replicas. Through qualitative and quantitative analyses, this study evaluates each platform’s strengths and limitations. The study assesses these platforms across key performance metrics such as accuracy, latency, graphic quality, and integration with the Robot Operating System (ROS). The DTs were developed using a consistent physical setup and communication layer to ensure fair comparisons. The results indicate that Unity performed better in terms of accurately mimicking the robotic arm with lower latency, making it ideal for applications requiring high-fidelity visualizations and real-time responsiveness. However, Gazebo excels in its ease of ROS integration and cost-effectiveness, making it a suitable choice for smaller robotics and automation projects. This study conducts an empirical comparison of these platforms in terms of their performance in creating DTs of robotic arms which is not readily available. This paper aims to guide developers and organizations in selecting the appropriate platform for their DT initiatives, ensuring efficient resource utilization and optimal outcomes.

On the journey of true autonomous robotics, applying deep reinforcement learning (DRL) techniques to solve complex robotics tasks has been a growing interest in academics and the industry. Currently, numerous simulation frameworks exist for evaluating DRL algorithms with robots, and they usually come with prebuilt tasks or provide tools to create custom environments. Among these, one of the highly sought approaches is using Robot Operating System (ROS) based DRL frameworks for simulation and deployment in the real world. The current ROS-based DRL simulation frameworks like openai_ros or Gym-gazebo provide a framework for creating environments; however, they do not support training with vectorised environments for speeding up the training process and parallel simulations for testing and evaluating meta-learning, multi-task learning and transfer learning approaches. Therefore, we present MultiROS, a 3D robotic simulation framework with a collection of prebuilt environments for deep reinforcement learning (DRL) research to overcome these limitations. This package interfaces with the Gazebo robotic simulator using ROS and provides a modular structure to create ROS-based RL environments. Unlike the others, MultiROS provides support for training with multiple environments in parallel and simultaneously accessing data from each simulation. Furthermore, since MultiROS uses the popular OpenAI Gym interface, it is compatible with most OpenAI Gym based reinforcement learning algorithms that use third-party python frameworks.

Reinforcement Learning (RL) enables robots to learn and improve from data without being explicitly programmed. It is well-suited for tackling complex and diverse robotic tasks, offering adaptive solutions without relying on traditional, hand-designed approaches. However, RL solutions in robotics have often been confined to simulations, with challenges in transferring the learned knowledge or learning directly in the real world due to latency issues, lack of a standardized structure, and complexity of integration with real robot platforms. While the use of Robot Operating System (ROS) provides an advantage in addressing these challenges, existing ROS-based RL frameworks typically support sequential, turn-based agent-environment interactions, which fail to represent the continuous, dynamic nature of real-time robotics or support robust multi-robot integration. This paper addresses this gap by proposing UniROS, a novel ROS-based RL framework explicitly designed for real-time multi-robot/task applications. UniROS introduces a ROS-centric implementation strategy for creating RL environments that support asynchronous and concurrent processing, which is pivotal in reducing the latency between agent-environment interactions. This study validates UniROS through practical robotic scenarios, including direct real-world learning, sim-to-real policy transfer, and concurrent multi-robot/task learning. The proposed framework, including all examples and supporting packages developed in this study, is publicly available on GitHub, inviting wider use and exploration in the field.

Reinforcement Learning (RL) enables robots to learn and improve from data without being explicitly programmed. It is well-suited for tackling complex and diverse robotic tasks, offering adaptive solutions without relying on traditional, hand-designed approaches. However, RL solutions in robotics have often been confined to simulations, with challenges in transferring the learned knowledge or learning directly in the real world due to latency issues, lack of standardized structure, and the complexity of integrating with real robot platforms. Furthermore, existing robotic RL frameworks typically support sequential, turn-based agent-environment interactions, which fail to represent the continuous, dynamic nature of real-time robotics. This paper addresses this gap by proposing UniROS, a novel Robot Operating System (ROS)-based RL framework explicitly designed for real-time multi-robot/task applications. UniROS introduces a ROS-centric implementation strategy for creating RL environments that support asynchronous, concurrent processing, which is pivotal in reducing the latency between agent-environment interactions. This study validates UniROS through practical robotic scenarios, including direct real-world learning, sim-to-real policy transfer, and concurrent multi-robot/task learning. The proposed framework, including all the examples and supporting packages developed in this study, is publicly available on GitHub, inviting wider use and exploration in the field.