Model-based design optimization for motion decoupling in dual-segment flexible robots

Abstract

The design of a multiple-segment notched tube is distinctively appropriate for miniature surgical robots, attributing to its superior structural compliance and the provision of a substantial lumen within a constrained diameter. However, the cable-driven mechanism commonly adopted in this design naturally leads to inter-segment motion coupling, limiting its clinical potential. This study introduces a model-based design optimization method aimed at concurrently minimizing the coupled motion of a two-segment notched flexible robot and meeting diverse design and performance criteria. In the design optimization framework, a single-segment mechanics model, a 3-dimensional (3D) coupled mechanics model, and a 3D stiffness model have been developed and integrated to simultaneously minimize the coupled bending angle and satisfy surgery-specific performance requirements. A back propagation neural network (BPNN) has also been adopted to achieve computationally efficient optimization process. A case study is conducted on two-segment notched flexible robot optimization design for maxillary sinus surgery, with the mechanics models being verified experimentally. Ultimately, the optimized robot demonstrates its ability to provide visualization of the maxillary sinus cavity, including the blind spots. The proposed robot design optimization approach offers a systematic and efficient methodology to minimize motion coupling without compromising robot performance, thus fundamentally enabling procedure-specific design of multi-segment flexible surgical robots.