为复杂环境中的连续体机械臂设计高效逆运动学求解器。
An Optimization-Based Inverse Kinematics Solver for Continuum Manipulators in Intricate Environments
- 基于优化方法构建逆运动学求解框架，支持障碍物避让等约束。
- 在高自由度下仍保持低延迟与稳定性能，仿真验证有效。
- 适合需灵活避障的精密操作场景，如医疗手术机器人。
连续体机械臂因其灵活性和适应性，在复杂工作空间中备受关注，是刚性机械臂的有力替代方案。然而，在存在多个障碍物的复杂环境中，高自由度（DoF）连续体机械臂的广泛应用仍受限于逆运动学（IK）求解效率与解的可靠性。现有方法在计算成本与解的保证方面面临挑战，尤其在需要障碍物避让的复杂场景中表现不佳。为此，本文提出一种新型逆运动学求解器，借鉴基于优化的路径规划思想，融合障碍物避让、长度、姿态等多类约束，专为复杂环境设计。通过仿真验证，该方法在高自由度下展现出优异的灵活性、计算效率与鲁棒性，同时保持可接受的延迟，适用于高度非结构化的工作空间。
原文摘要 · Abstract (English)
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.
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