Control de Fuerza basado en la Admitancia Mecánica para Masajes Robóticos

• Cisnal, Ana ^[1] ; Oña Simbaña, Edwin Daniel ^[2] ; Jardón Huete, Alberto ^[3] ; Fraile, Juan Carlos ^[1] ; Pérez Turiel, Javier ^[1]
  1. [1] Universidad de Valladolid

     Universidad de Valladolid

     Valladolid, España

     
  2. [2] Universidad Carlos III de Madrid

     Universidad Carlos III de Madrid

     Madrid, España

     
  3. [3] Universidad Carlos 3 de Madrid

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• Localización: Jornadas de Automática, ISSN-e 3045-4093, Nº. 46, 2025
• Idioma: español
• DOI: 10.17979/ja-cea.2025.46.12150
• Títulos paralelos:
  • Force Control Based on Mechanical Admittance for Robotic Massages
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• Resumen
  • español

    Este estudio presenta un sistema robótico de masaje basado en control de fuerza por admitancia mecánica, implementado en un robot colaborativo Elfin 5 con un sensor de fuerza/par. La arquitectura modular implementada en ROS 2 integra interpolación de trayectorias y control de fuerza, que ajusta dinámicamente la posición cartesiana del efector final para mantener constante la fuerza de contacto y calcula las posiciones articulares mediante cinemática inversa. Se realizaron tres pruebas experimentales con referencia de 4 N, logrando un seguimiento preciso de fuerza (Emedio = 0,49N), aunque con incrementos significativos al cambiar de sujeto (Emedio = 0,79N) y velocidad (Emedio = 1,85N). Los resultados demuestran el potencial del sistema para mantener la fuerza deseada en el eje Z, pero destacan la necesidad de incorporar modelos realistas de piel y algoritmos adaptativos, como el aprendizaje por refuerzo, para mejorar el rendimiento y personalizar los masajes. Este trabajo sienta las bases para sistemas robóticos de masaje avanzados y seguros, complementando la intervención humana en rehabilitación física.

  • English

    This study presents a robotic massage system based on force control through mechanical admittance, implemented on an Elfin 5 collaborative robot with a force/torque sensor. The modular architecture implemented in ROS 2 integrates trajectory interpolation and force control, which dynamically adjusts the Cartesian position of the end-effector to maintain constant contact force and computes the joint positions via inverse kinematics. Three experimental tests were conducted with a reference force of 4 N, achieving precise force tracking (Emean = 0,49N), though with significant increases when changing subjects (Emean = 0,79N) and speed (Emean = 1,85N). The results demonstrate the system’s potential to maintain the desired force along the Z-axis, while highlighting the need to incorporate realistic skin models and adaptive algorithms, such as reinforcement learning, to improve performance and personalize massage therapy. This work lays the foundation for advanced and safe robotic massage systems, complementing human intervention in physical rehabilitation.

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