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Optimal Tuning of Robot–Environment Interaction Controllers via Differential Evolution: A Case Study on (3,0) Mobile Robots

Author

Listed:
  • Jesús Aldo Paredes-Ballesteros

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

  • Miguel Gabriel Villarreal-Cervantes

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

  • Saul Enrique Benitez-Garcia

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

  • Alejandro Rodríguez-Molina

    (Colegio de Ciencia y Tecnología, Universidad Autónoma de la Ciudad de México, Mexico City 06720, Mexico)

  • Alam Gabriel Rojas-López

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

  • Victor Manuel Silva-García

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

Abstract

Robotic systems operating in complex environments require optimized tuned interaction controllers to ensure accurate task execution while maintaining smooth and safe behavior. This paper presents a scalarized multi-objective tuning approach based on Differential Evolution (DE) to optimize robot–environment interaction control. The method balances trajectory tracking accuracy and control smoothness using repulsive forces derived from potential fields modeled as virtual springs. The approach is validated on a (3,0) omnidirectional mobile robot navigating predefined trajectories with obstacles. A comparative study of five DE variants shows that DE/best/1/bin and DE/best/1/exp offer the best performance. Simulation and experimental results, including validation with an actual force sensor, confirm the method’s effectiveness and applicability in scenarios with limited sensing capabilities or model uncertainty.

Suggested Citation

  • Jesús Aldo Paredes-Ballesteros & Miguel Gabriel Villarreal-Cervantes & Saul Enrique Benitez-Garcia & Alejandro Rodríguez-Molina & Alam Gabriel Rojas-López & Victor Manuel Silva-García, 2025. "Optimal Tuning of Robot–Environment Interaction Controllers via Differential Evolution: A Case Study on (3,0) Mobile Robots," Mathematics, MDPI, vol. 13(11), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1789-:d:1665793
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