Continuum manipulators have gained significant attention as a promising alternative to rigid manipulators, offering notable advantages in terms of flexibility and adaptability within intricate workspace. However, the broader application of high degree-of-freedom (DoF) continuum manipulators in intricate environments with multiple obstacles necessitates the development of an efficient inverse kinematics (IK) solver specifically tailored for such scenarios. Existing IK methods face challenges in terms of computational cost and solution guarantees for high DoF continuum manipulators, particularly within intricate workspace that obstacle avoidance is needed. To address these challenges, we have developed a novel IK solver for continuum manipulators that incorporates obstacle avoidance and other constraints like length, orientation, etc., in intricate environments, drawing inspiration from optimization-based path planning methods. Through simulations, our proposed method showcases superior flexibility, efficiency with increasing DoF, and robust performance within highly unstructured workspace, achieved with acceptable latency.

翻译：连续体机械臂作为刚性机械臂的有前景替代方案，因其在复杂工作空间中展现出的显著柔性与适应性优势而备受关注。然而，高自由度连续体机械臂在存在多重障碍物的复杂环境中的广泛应用，亟需开发专门针对此类场景的高效逆运动学求解器。现有逆运动学方法在处理高自由度连续体机械臂时，特别是在需要避障的复杂工作空间中，面临计算成本与解的存在性保证方面的挑战。为解决这些问题，受基于优化的路径规划方法启发，我们开发了一种新型连续体机械臂逆运动学求解器，该求解器能够在复杂环境中整合避障约束及长度、朝向等其他约束条件。仿真实验表明，所提方法在可接受的延迟下，展现出优异的灵活性、随自由度增加的高效性，以及在高度非结构化工作空间中的鲁棒性能。