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.

中文翻译：

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基于模型的双节柔性机器人运动解耦设计优化

多段切口管的设计特别适合微型手术机器人，因为其具有卓越的结构顺应性，并且在有限的直径内提供了大量的管腔。然而，该设计中普遍采用的电缆驱动机构自然会导致节段间的运动耦合，限制了其临床潜力。本研究介绍了一种基于模型的设计优化方法，旨在同时最小化两段缺口柔性机器人的耦合运动并满足不同的设计和性能标准。在设计优化框架中，开发并集成了单段力学模型、3D 耦合力学模型和 3D 刚度模型，以同时最小化耦合弯曲角度并满足手术特定的性能要求。还采用反向传播神经网络（BPNN）来实现计算高效的优化过程。对上颌窦手术两段式缺口柔性机器人优化设计进行了案例研究，并通过实验验证了力学模型。最终，优化后的机器人展示了其提供上颌窦腔（包括盲点）可视化的能力。所提出的机器人设计优化方法提供了一种系统且有效的方法，可以在不影响机器人性能的情况下最大限度地减少运动耦合，从而从根本上实现多段柔性手术机器人的特定程序设计。