Modeling and optimization of small-scale soft magnetic robots for enhanced locomotion

Small-scale soft robots are vital in medical scenarios requiring access to confined spaces. This study explores motion optimization for such robots, emphasizing magnetically responsive materials that enable shape changes through controlled magnetic fields. These materials provide robots with versatile and agile locomotion capabilities. Using genetic algorithms, we present a novel approach to optimize magnetic field parameters and robot dimensions. In magnetic field optimization, the results validate prior experimental observations while providing new insights into determining optimal magnetic field characteristics for walking motions. Deviations from optimal magnetic field magnitudes directly affect walking velocity, revealing a nonlinear relationship between magnetic fields and locomotion speed, diverging from linear modeling results. Furthermore, we find that both excessively low and high robot heights hinder walking speed: lower heights reduce surface friction, while higher heights compromise agility. This comprehensive optimization strategy advances the capabilities of small-scale soft magnetic robots. Our work in motion modeling and optimization enhances the understanding of these robots’ dynamics and unlocks new possibilities for deployment in complex environments. These findings reaffirm the significance of soft robots in medical and confined-space applications, offering a robust foundation for further innovations in this evolving field.