Continuum robots, characterized by their high flexibility and infinite degrees of freedom (DoFs), have gained prominence in applications such as minimally invasive surgery and hazardous environment exploration. However, the intrinsic complexity of continuum robots requires a significant amount of time for their motion planning, posing a hurdle to their practical implementation. To tackle these challenges, efficient motion planning methods such as Rapidly Exploring Random Trees (RRT) and its variant, RRT*, have been employed. This paper introduces a unique RRT*-based motion control method tailored for continuum robots. Our approach embeds safety constraints derived from the robots' posture states, facilitating autonomous navigation and obstacle avoidance in rapidly changing environments. Simulation results show efficient trajectory planning amidst multiple dynamic obstacles and provide a robust performance evaluation based on the generated postures. Finally, preliminary tests were conducted on a two-segment cable-driven continuum robot prototype, confirming the effectiveness of the proposed planning approach. This method is versatile and can be adapted and deployed for various types of continuum robots through parameter adjustments.

翻译：连续型机器人以其高度灵活性和无限自由度在微创手术、危险环境探测等领域受到广泛关注。然而，其内在复杂性导致运动规划耗时显著增加，制约了实际应用部署。为应对这一挑战，快速扩展随机树及其改进算法RRT*等高效运动规划方法被引入相关研究。本文提出了一种专为连续型机器人设计的RRT*运动控制方法，通过嵌入源自机器人姿态状态的安全约束，实现了快速变化环境下的自主导航与避障功能。仿真结果表明，该方法能够在多重动态障碍物场景中高效规划轨迹，并基于生成姿态提供稳健的性能评估。最后，在双段缆索驱动连续型机器人样机上开展的初步试验验证了所提规划方法的有效性。该方案具有通用性，可通过参数调整适配应用于各类连续型机器人。