Design of patterns in tubular robots using DNN-metaheuristics optimization

Concentric-tube robots for minimally invasive surgery pose a potential risk of tissue rupture because of the structural instabilities caused by high value of bending-to-torsional-stiffness ratio (EI/GJ). In this study, a novel optimization method based on metaheuristic optimization accelerated by a deep neural network (DNN)-based surrogate model to obtain optimized pattern parameters is presented. The method minimizes EI/GJ while con-forming to the minimum compliance constraints and geometric restrictions. The proposed optimization process utilizes a DNN trained using 855 datasets generated by finite element analysis that cover the pattern design parameter space. The pattern design parameters were derived from topology optimization. The results demon-strate that the proposed optimization method yielded pattern designs that outperformed previous designs within a reasonable time frame (less than 900 s) without requiring manual parametric study or sensitivity analysis.