基于迭代学习算法的柔性针轨迹规划

Flexible Needle Path Planning Based on the Iterative Learning Algorithm

轨迹规划在机器人辅助柔性针穿刺中有着重要作用。在针-组织交互大变形下,针-组织耦合模型可以对由于针组织交互作用产生的针轨迹误差进行预测。提出一种基于迭代学习的轨迹规划算法:首先在离线状态下,基于可达域理论生成刚性空间下的初始路径集,并通过遗传模拟退火优化算法选择出最优路径;再基于针-组织耦合力学模型对柔性空间的针挠曲、组织形变进行预测,并根据偏差预测值和迭代学习算法不断修正柔性空间中的针控序列以满足穿刺精度要求,最后得到最优规划参数并且对针轨迹规划算法中的参数进行全局敏感性分析;最后,搭建了针穿刺试验台对提出的针轨迹算法进行验证,试验结果表明算法的轨迹平均误差小于0.45 mm。

Path planning plays an important role in robot-assisted flexible needle insertion procedures. The trajectory errors derived from the needle-tissue interaction can be predicted based on a proper needle-tissue interaction model, which is important for needle path planning, especially when the needle is inserted into highly deformable tissues. A novel iterative learning-based path planning method is proposed. First, a set of candidate paths are offline generated according to the reachable domain analysis under non-deformable environment, wherein the best candidate path is selected based on genetic simulated annealing algorithm(GSAA). Then, a real-time needle-tissue interaction model is utilized to predict the target deformation and needle deflection, which is combined with an iterative learning control-based path correction algorithm to achieve satisfactory trajectory accuracy. Finally, a global sensitivity analysis is applied to the proposed planning framework for parameter optimization. Needle insertion experiments are conducted with pre-defined targets and obstacles in phantoms to verify the proposed path planning methods. The results show that the error of the proposed algorithm can reach an average error below 0.45 mm.