Simultaneous Compliance and Registration Estimation for Robotic Surgery

Abstract

Abstract-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.

Hybrid motion/force control of multi-backbone continuum robots

“…The framework proposed in this work makes the first step toward improving situation awareness, estimation and mapping in challenging environments such as minimally invasive robotic surgery. The proposed algorithm was already used in a proof of concept of simultaneous localization and mapping of body organs (BodySLAM) (Sanan et al, 2014) by providing simultaneous shape and stiffness information of soft environments. Future work will demonstrate the proposed framework on multi-segment continuum robots and provide efficient and precise algorithms for shape estimation and stiffness imaging (Goldman et al, 2012) in deep surgical sites.…”

“…In Tully et al (2012), the use of contact detection and a constrained Kalman filter to register a flexible robot to a flexible environment was demonstrated. In Sanan et al (2014), we extended this framework to use force controlled scans of a flexible environment based on the algorithm described in this work, to register shape and stiffness maps of the environment to pre-operative models.…”

On the inseparable nature of sensor selection, sensor placement, and state estimation for continuum robots or “where to put your sensors and how to use them”

“…In this case, RTS-smoothing updates the proximal pose (i.e., the initial registration) using sensor observations conditioned on the robot's kinematics, improving its accuracy. In [16], this is done using a priori models of surgical-site stiffness.…”

Using Bayesian optimization to guide probing of a flexible environment for simultaneous registration and stiffness mapping

“…Our group has previously developed a method for simultaneously estimating the registration and stiffness distribution over the surface of a flexible environment using a Kalman filtering approach called CARE [17] and a more recent model update method, called Complementary Model Update (CMU), that decouples stiffness estimation from registration, resulting in a more robust implementation. Similar to CARE, the CMU uses the force and position information obtained by interaction of the surgical tool with the organ to estimate the local stiffness and to register the organ to its preoperative model.…”