基于改进RRT算法的套管柔性针运动规划

运动规划是运动控制的基础,然而套管柔性针穿刺软组织的运动规划问题面临着巨大的挑战:一方面由于套管柔性针的运动是个非完整约束运动,另一方面要求套管柔性针绕过人体的某些生理结构障碍和敏感组织准确穿刺靶点。基于分析现有运动规划算法存在的不足,提出基于改进的快速探索随机树(RRT)的套管柔性针运动规划算法。提出贪婪启发策略并结合可达引导策略来改进传统RRT算法;引入直线段路径,采用直线、曲线结合的路径形式;同时考虑入射姿态的规划。基于套管柔性针的运动学模型,分别在二维和三维有障碍环境下进行仿真研究。结果表明,所提出的运动规划算法不论是在运算速度和收敛性上还是在路径形式和搜索的鲁棒性上都优于目前普遍采用的运动规划算法。这些优势为将来的实时运动规划奠定基础。最后对规划的路径进行了穿刺实验,结果证明实验路径与规划路径十分吻合,验证了所提出的路径规划算法的正确性和规划路径的可行性。

Motion planning of the cannula flexible needle based on the improved RRT algorithm

Motion planning is the basis for the motion control. However, motion planning for a cannula flexible needle insertion in the soft tissue is a great challenge. On the one hand, the kinematics of the needle is a nonholonomic motion, and on the other hand, the needle has to steer clear of the anatomical obstacles and sensitive organs to reach the target precisely. By analyzing the deficiency of existing motion planning algorithms, a motion planning algorithm for the cannula flexible needle is proposed based on an improved Rapidly Exploring Random Trees (RRT). The Greedy Heuristic strategy is proposed, which is combined with the Reachability Guided strategy to improve the conventional RRT. Linear segment is introduced into the planning, and the combination path of the linear and curvilinear segments is adopted. Insertion orientations are taken into account at the same time. Simulations are performed in 2D and 3D environments with obstacles based on the kinematic model of the needle. Results show that the proposed algorithm yields superior results compared with the commonly used algorithm in terms of computational speed, convergence, form of path and robustness of searching ability. These superiorities potentially provide the basis for the upcoming real time motion planning. At last, the experiment for the planned path is carried, and experimental results show that experimental paths agree with the planned ones very well, which not only proves the validity of the proposed path planning algorithm but also proves the feasibility of the planned path.