Murilo M. Marinho

A dual quaternion linear-quadratic optimal controller for trajectory tracking

This work addresses the task-space design problem of a linear-quadratic optimal tracking controller for robotic manipulators using the unit dual quaternion formalism. The efficiency, compactness, and lack of singularity of the representation render the unit dual quaternion a suitable framework for simultaneously describing the attitude and the position of the end-effector. Motivated by the advantages of this kinematic description, we propose a new task-space linear-quadratic optimal tracking controller in order to find an optimal trajectory for the end-effector, providing a tool to balance more conveniently the end-effector error and its task-space velocity. This is possible because the kinematic control problem using the dual quaternion transformation invariant error can be reduced to an affine time-varying system. The proposed optimal tracking controller allows the compensation of trajectory induced disturbances, as well as other modeled additive disturbances and known bias. Simulation results with different design parameters provide a performance overview, in comparison with standard kinematic controllers with and without a feed-forward term, for tracking a desired reference.

Conceptual design of a versatile robot for minimally invasive transnasal microsurgery

The da Vinci surgical system is now widely adopted in hospitals and research centers worldwide, with more than half a million clinical cases [1]. However, minimally invasive microsurgical procedures such as the transsphenoidal pituitary tumor resection has yet to be contemplated with robotic aid. Such necessity inspired some groups to use the da Vinci system in head and neck surgery, but with limited success [2], due to its large size, thick tool diameter exceeding 5 mm, and lack of force feedback.

Towards robust needle segmentation and tracking in pediatric endoscopic surgery.

Neonatal tracheoesophageal fistula surgery poses technical challenges to surgeons, given the limited workspace and fragile tissues. In previous studies from our collaborators, a neonatal chest model was developed to allow surgeons to enhance their performance, such as suturing ability, before conducting actual surgery. Endoscopic images are recorded while the model is used, and surgeon skill can be manually assessed by using a 29-point checklist. However, that is a time-consuming process. In the checklist, there are 15 points that regard needle position and angle that could be automatized if the needle could be efficiently tracked. This paper is a first step towards the goal of tracking the needle. Pixel HSV color space channels, opponent color space channels, and pixel oriented gradient information are used as features to train a random forest model. Three methods are compared in the segmentation stage: single pixel features, pixel and its immediate 10-by-10 square window features, and the features of randomly offset pixels in a larger 169-by-169 window. Our analysis using 9-fold cross-validation shows that using randomly offset pixels increases needle segmentation f-measure by 385 times when comparing with single pixel color, and by 3 times when comparing with the immediate square window even though the same amount of memory is used. The output in the segmentation step is fed into a particle filter to track the full state of the needle.

On the use of general-purpose serial-link manipulators in eye surgery

Vitreo-retinal surgery is known to be very challenging because of the minuscule structures of the retina, which are smaller than the hand tremor amplitude of ∼100 μm. In this work, the use of a serial-link general-purpose manipulator in eye surgery to increase the accuracy was assessed. The proposed system showed a 3-sigma accuracy of 22 ± 36 μm when tracing a 1 mm square with an ophthalmic micropipette. The 3-sigma remote center of motion error was 0.14±0.23 mm when tracing a 7 mm square.

Development of a virtual reality simulator for robotic brain tumor resection

The goal of this work was to develop a virtual reality simulator for minimal invasive micro robotic neurosurgery, focusing on the case of transsphenoidal approach pituitary gland tumor resection. Particularly we work on the force feedback, collision avoidance and dynamic motion scaling. The main challenges in our work were the real time collision detection and the deformation of the soft tissues.

Reduced errors in robot-aided minimally invasive surgery through online condition number optimization

The use of robots in surgery is of great research interest following the widespread adoption of da Vinci systems in surgical rooms worldwide. Research interest has grown, as such robot aid still cannot fully perform procedures requiring smaller instruments and more precise movements. In addition to added flexibility, serial-link manipulators can also provide these capabilities for surgical procedures. In surgical procedures, robot control techniques should strive for low errors along with slow joint speeds. This paper discusses control theory and conducts an analysis on the shortcomings of damped inverses and singular value decomposition inverses. The use of online local optimization of the condition number of the Jacobian to increase the dexterity of redundant serial-link manipulators with arbitrary geometries was proposed. Techniques in previous studies were compared in a simulated minimally invasive surgical task. The proposed technique utilized inner motions to autonomously reposition the manipulator in high dexterity configurations without disturbing the main task. Hence, the proposed technique decreased the remote center-of-motion error by 27% and reduced the joint velocity requirements for the same trajectory by 34%.