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

Jesús Aldo Paredes-Ballesteros, Miguel Gabriel Villarreal-Cervantes (), Saul Enrique Benitez-Garcia (), Alejandro Rodríguez-Molina, Alam Gabriel Rojas-López and Victor Manuel Silva-García
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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

Mathematics, 2025, vol. 13, issue 11, 1-28

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.

Keywords: controller tuning; robot–environment interactions; omnidirectional mobile robot; differential evolution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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