Simultaneous Compliance and Registration Estimation for Robotic Surgery
Simultaneous Compliance and Registration Estimation for Robotic Surgery
复制标题
机器人手术的同时合规性和配准估计
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
H. Choset
中科院分区：
文献类型：
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作者：
S. Sanan;Steven Tully;A. Bajo;N. Simaan;H. Choset
利用SLAM社区开创的技术，我们提出了一种新的过滤方法，称为同时遵守和注册估计或关怀。CARE类似于SLAM，因为它在创建地图的同时确定手术机器人的姿势，但在这种情况下，地图是与器官的术前模型相关联的顺应性地图，而不仅仅是像界标位置这样的位置信息。这个问题假设机器人与环境的接触和变形。这种触诊具有双重目的：1）它提供必要的几何信息以将机器人对准或配准到先验模型，以及2）通过在不同力下的触诊，可以计算环境的刚度/顺应性。通过允许机器人触诊其环境与不同的力量，我们创建一个力平衡的弹簧模型内的卡尔曼滤波器的框架来估计组织和机器人的位置。概率框架允许信息融合和计算效率。该算法的实验评估使用连续体机器人与两个台式柔性结构的相互作用。
Leveraging techniques pioneered by the SLAM community, we present a new filtering approach called simultaneous compliance and registration estimation or CARE. CARE is like SLAM in that it simultaneously determines the pose of a surgical robot while creating a map, but in this case, the map is a compliance map associated with a preoperative model of an organ as opposed to just positional information like landmark locations. The problem assumes that the robot is forcefully contacting and deforming the environment. This palpation has a dual purpose: 1) it provides the necessary geometric information to align or register the robot to a priori models, and 2) with palpation at varying forces, the stiffness/compliance of the environment can be computed. By allowing the robot to palpate its environment with varying forces, we create a force balanced spring model within a Kalman filter framework to estimate both tissue and robot position. The probabilistic framework allows for information fusion and computational efficiency. The algorithm is experimentally evaluated using a continuum robot interacting with two benchtop flexible structures.