Integration of a Digital Twin Framework for Trajectory Control of a 2RRR Planar Parallel Manipulator Using ROS/Gazebo and MATLAB

Carlos Andrés Mesa-Montoya, Néstor Iván Marín Peláez, Kevin David Ortega-Quiñones, German Andrés Holguín-Londoño, Libardo Vicente Vanegas-Useche, Gian Carlo Daraviña-Peña, Edwan Anderson Ariza-Echeverri and Diego Vergara ()
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Carlos Andrés Mesa-Montoya: Programa de Ingeniería Mecánica, Facultad de Mecánica Aplicada, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Néstor Iván Marín Peláez: Programa de Ingeniería Mecánica, Facultad de Mecánica Aplicada, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Kevin David Ortega-Quiñones: Programa de Ingeniería Eléctrica, Facultad de Ingenierías, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
German Andrés Holguín-Londoño: Programa de Ingeniería Eléctrica, Facultad de Ingenierías, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Libardo Vicente Vanegas-Useche: Programa de Ingeniería Mecánica, Facultad de Mecánica Aplicada, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Gian Carlo Daraviña-Peña: Programa de Ingeniería Mecánica, Facultad de Mecánica Aplicada, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Edwan Anderson Ariza-Echeverri: Grupo de Nuevos Materiales y Didáctica de las Ciencias, Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia
Diego Vergara: Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Ávila, 05005 Ávila, Spain

Future Internet, 2025, vol. 17, issue 4, 1-23

Abstract: Digital twin (DT) technology is transforming industrial automation by enabling the real-time simulation, predictive control, and optimization of complex systems. This study presents a DT-based kinematic control method designed for trajectory planning and execution in a 2RRR planar parallel manipulator. The framework utilizes ROS/Gazebo for virtual modeling and MATLAB’s Guide tool for a human–machine interface, establishing a synchronized virtual–physical environment. By dynamically bridging design and manufacturing phases, the DT model enhances operational insight through real-time data exchange and control flexibility. Statistical analyses, including the comparative hypothesis testing of angular positions and velocities with a 95% confidence level, validate the model’s precision, demonstrating a high degree of fidelity between the virtual model and the physical system. These findings confirm the DT’s reliability as an effective tool for trajectory programming, highlighting its potential in industrial robotics where adaptability and data-driven decision making are essential. This approach contributes to the evolving landscape of Industry 4.0 by supporting intelligent manufacturing systems with improved accuracy and efficiency.

Keywords: kinematic control; trajectory planning; inverse kinematics; Industry 4.0 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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