Motion Planning under Uncertainty for Medical Needle Steering Using Optimization in Belief Space.
Motion Planning under Uncertainty for Medical Needle Steering Using Optimization in Belief Space.
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DOI：
10.1109/iros.2014.6942795
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Alterovitz R
中科院分区：
文献类型：
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作者：
Sun W;Alterovitz R
我们提出了一种基于优化的运动规划器的医疗可操纵针，明确考虑运动和传感的不确定性，同时引导针的目标在3D解剖结构。用于针转向的运动规划是具有挑战性的，因为针是非完整和欠致动的系统，针的运动可能在插入期间由于未建模的针/组织相互作用而被扰动，并且诸如超声成像和X射线投影成像的医学感测模态通常仅提供噪声和部分状态信息。为了考虑这些不确定性，我们引入了一个运动规划器，计算轨迹和相应的线性控制器的信念空间-在状态空间的分布空间。我们制定的针转向运动规划问题作为一个部分可观察的马尔可夫决策过程（POMDP），近似高斯的信念状态。然后，我们计算一个局部最优轨迹和相应的控制器，在置信空间中最小化的成本函数，考虑避免障碍物，不安全的控制输入的处罚，和目标获取精度。我们将运动规划器应用到模拟场景中，并表明在置信空间中的局部优化使我们能够计算出更高质量的计划，而不仅仅是在针的状态空间中进行规划。
We present an optimization-based motion planner for medical steerable needles that explicitly considers motion and sensing uncertainty while guiding the needle to a target in 3D anatomy. Motion planning for needle steering is challenging because the needle is a nonholonomic and underactuated system, the needle’s motion may be perturbed during insertion due to unmodeled needle/tissue interactions, and medical sensing modalities such as ultrasound imaging and x-ray projection imaging typically provide only noisy and partial state information. To account for these uncertainties, we introduce a motion planner that computes a trajectory and corresponding linear controller in the belief space - the space of distributions over the state space. We formulate the needle steering motion planning problem as a partially observable Markov decision process (POMDP) that approximates belief states as Gaussians. We then compute a locally optimal trajectory and corresponding controller that minimize in belief space a cost function that considers avoidance of obstacles, penalties for unsafe control inputs, and target acquisition accuracy. We apply the motion planner to simulated scenarios and show that local optimization in belief space enables us to compute higher quality plans compared to planning solely in the needle’s state space.