Towards accurate motion compensation in surgical robotics

Abstract

This paper proposes a method for accurate robotic motion compensation of a freely moving target object. This approaches a typical problem in medical scenarios, where a robotic system needs to compensate physiological movements of a target region related to the patient. An optical tracking system measures the poses of the robot's end-effector and the moving target. The task is to track the target with the robot in a desired relative pose. Arbitrary motion in 6 DoF is covered. The position controller of the medical light-weight robot MIRO is enhanced by a Cartesian displacement observer. The proposed observer feedback preserves the dynamics of the robot, while achieving high accuracy in task space. The target object is equipped with an inertial measurement unit in addition to tracking markers. Target sensor data is fused by an extended Kalman filter in a tightly coupled approach. The robot control and the target tracking in the task space aim to combine accuracy, dynamic performance and robustness to marker occlusions. The algorithms are verified with the DLR MIRO, an experimental target platform, and a commercial tracking system. The experiments demonstrate rapid convergence to desired Cartesian poses and good dynamic tracking performance even at higher target motion speed.

Accurate multi-robot targeting for keyhole neurosurgery based on external sensor monitoring

“…Tracking system performance for robot control was also improved by adding inertial measurements units 18 in order to increase the control robustness with respect to marker occlusions, to compensate for delay of the optical system and to reduce noise.…”

Optically tracked multi-robot system for keyhole neurosurgery

“…Kalman filters were also used for data fusion of redundant sensory information (from encoders and optical tracking systems) [9]. Tracking systems performances for robot control can be improved by the addition of inertial measurements units [10] in order to increase the control robustness with respect to marker occlusions, to compensate for delay of the optical system and to reduce noise.…”

Development and preliminary assessment of a robotic platform for neuroendoscopy based on a lightweight robot

“…As anticipated, in order to achieve accurate tool targeting, the aforementioned calibration was complemented by a closed‐loop compensation strategy exploiting online data from the OT (and the LWR encoders). For this purpose, we considered the compensation strategies reported in and , hereafter, respectively labelled as CS1 and CS2 for ease of development. The corresponding control schemes are briefly discussed below, while full details on implementation and assessment are reported in the Supporting Information S1 for ease of readability.…”

“…Schematic of the proposed closed‐loop compensation strategies: (a) CS1, adapted from and (b) CS2, adapted from . Here X represents the map T AR EE , as acquired by the optical tracker, whereas X d , and X ' denote the desired value, the estimated value, and the one provided by the robot, respectively…”