Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13111789

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

Institutions: Instituto Politécnico Nacional, Universidad Autónoma de la Ciudad de México

Language: English

Topic: Control and Dynamics of Mobile Robots

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Instituto Politécnico Nacional