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

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.