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<p>Cable-driven hyper-redundant manipulators (CDHRMs) have significant potential for application in narrow space exploration and minimally invasive surgery owing to their high degrees of freedom and flexibility. However, existing designs generally suffer from insufficient stiffness, limited load capacity, and poor positioning accuracy. To address these challenges, we propose a bio-inspired CDHRM modeled after the biomechanical properties of the human spine. The modular design integrates bionic spinal segments and spherical joints to balance compliance with a high payload capacity and enhanced stiffness. For accurate control, we developed a hybrid kinematic model combining a Piecewise Constant Curvature (PCC) approach with quaternion representation. Additionally, to solve high-dimensional inverse kinematics (IK), we propose an improved Particle Swarm Optimization (PSO) algorithm incorporating a k-dimensional tree (KDT). Simulations and prototype experiments validate the proposed system. Compared to conventional methods, our KDT-guided PSO algorithm reduces the average IK solving error by 57% and achieves a 92% convergence rate. Furthermore, the physical prototype demonstrates a fast dynamic response (settling time &lt; 0.5&#xa0;s), high stiffness (average 0.709&#xa0;N/mm), and high precision (repeatability error ≤ 1.6&#xa0;mm). These integrated advancements provide an effective rigid-flexible design and control paradigm for narrow-space operations.</p>

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Design of a Bionic Spinei Inspired Cable-driven Hyper-redundant Manipulator with High-precision Inverse Kinematics Algorithm

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Huiling Wei,
  • Chengbin Liang,
  • Hui Xiao,
  • Weilin Chen,
  • Mingyou Chen,
  • Yanqin Li,
  • Lufeng Luo

摘要

Cable-driven hyper-redundant manipulators (CDHRMs) have significant potential for application in narrow space exploration and minimally invasive surgery owing to their high degrees of freedom and flexibility. However, existing designs generally suffer from insufficient stiffness, limited load capacity, and poor positioning accuracy. To address these challenges, we propose a bio-inspired CDHRM modeled after the biomechanical properties of the human spine. The modular design integrates bionic spinal segments and spherical joints to balance compliance with a high payload capacity and enhanced stiffness. For accurate control, we developed a hybrid kinematic model combining a Piecewise Constant Curvature (PCC) approach with quaternion representation. Additionally, to solve high-dimensional inverse kinematics (IK), we propose an improved Particle Swarm Optimization (PSO) algorithm incorporating a k-dimensional tree (KDT). Simulations and prototype experiments validate the proposed system. Compared to conventional methods, our KDT-guided PSO algorithm reduces the average IK solving error by 57% and achieves a 92% convergence rate. Furthermore, the physical prototype demonstrates a fast dynamic response (settling time < 0.5 s), high stiffness (average 0.709 N/mm), and high precision (repeatability error ≤ 1.6 mm). These integrated advancements provide an effective rigid-flexible design and control paradigm for narrow-space operations.