Planning and Real Time Control of a Minimally Invasive Robotic Surgery System

This paper introduces the planning and control software of a teleoperating robotic system for minimally invasive surgery. It addresses the problem of how to organize a complex system with 41 degrees of freedom including robot setup planning, force feedback control and nullspace handling with three robotic arms. The planning software is separated into sequentially executed planning and registration procedures. An optimal setup is first planned in virtual reality and then adapted to variations in the operating room. The real time control system is composed of hierarchical layers. The design is flexible and expandable without losing performance. Structure, functionality and implementation of planning and control are described. The robotic system provides the surgeon with an intuitive hand-eye-coordination and force feedback in teleoperation for both hands

Induction of severe hypoxemia and low lung recruitability for the evaluation of therapeutic ventilation strategies: a translational model of combined surfactant-depletion and ventilator-induced lung injury

Background: Models of hypoxemic lung injury caused by lavage-induced pulmonary surfactant depletion are prone to prompt recovery of blood oxygenation following recruitment maneuvers and have limited translational validity. We hypothesized that addition of injurious ventilation following surfactant-depletion creates a model of the acute respiratory distress syndrome (ARDS) with persistently low recruitability and higher levels of titrated "best" positive end-expiratory pressure (PEEP) during protective ventilation. Methods: Two types of porcine lung injury were induced by lung lavage and 3 h of either protective or injurious ventilation, followed by 3 h of protective ventilation (N = 6 per group). Recruitment maneuvers (RM) and decremental PEEP trials comparing oxygenation versus dynamic compliance were performed after lavage and at 3 h intervals of ventilation. Pulmonary gas exchange function, respiratory mechanics, and ventilator-derived parameters were assessed after each RM to map the course of injury severity and recruitability. Results: Lung lavage impaired respiratory system compliance (C-rs) and produced arterial oxygen tensions (PaO2) of 84 +/- 13 and 80 +/- 15 (FIO2 = 1.0) with prompt increase after RM to 270-395 mmHg in both groups. After subsequent 3 h of either protective or injurious ventilation, PaO2/FIO2 was 104 +/- 26 vs. 154 +/- 123 and increased to 369 +/- 132 vs. 167 +/- 87 mmHg in response to RM, respectively. After additional 3 h of protective ventilation, PaO2/FIO2 was 120 +/- 15 vs. 128 +/- 37 and increased to 470 +/- 68 vs. 185 +/- 129 mmHg in response to RM, respectively. Subsequently, decremental PEEP titration revealed that C-rs peaked at 36 +/- 10 vs. 25 +/- 5 ml/cm H2O with PEEP of 12 vs. 16 cmH(2)O, and PaO2/FIO2 peaked at 563 +/- 83 vs. 334 +/- 148 mm Hg with PEEP of 16 vs. 22 cmH(2)O in the protective vs. injurious ventilation groups, respectively. The large disparity of recruitability between groups was not reflected in the C-rs nor the magnitude of mechanical power present after injurious ventilation, once protective ventilation was resumed. Conclusion: Addition of transitory injurious ventilation after lung lavage causes prolonged acute lung injury with diffuse alveolar damage and low recruitability yielding high titrated PEEP levels. Mimicking lung mechanical and functional characteristics of ARDS, this porcine model rectifies the constraints of single-hit lavage models and may enhance the translation of experimental research on mechanical ventilation strategies

New Inverse Kinematics Algorithms Combining Closed Form Solutions with Nonlinear Optimization for Highly Redundant Robotic Systems

This paper presents inverse position kinematics algorithms with real time capability for Justin, a robotic system with high redundancy and many degrees of freedom. The combination of closed form solutions for parts of the kinematic chain embedded in a nonlinear equation solver is shown to be advantageous. The algorithms are evaluated with the DLR service robot Justin both in simulation and reality. Calculation times of 1 ms are achieved, including various optimization criteria for redundancy resolution. In case only a single arm with 7 DoF is considered, fast calculation time of 250 s is reached. With inclusion of an iterative step, reachability can be shown in more than 99% of the calculations regardless of the initial guess. The problem of weighting in multi-criteria optimization problems remains, though in the chosen approach the tool tip position is never compromised by other criteria due to the partially closed form solution. The presented algorithm can be applied to inverse position kinematics for all manipulators with serial or tree structure and redundant joints in case closed form solutions are available for parts of the kinematic chain

The Autopointer: A New Augmented-Reality Device for Transfer of Planning Data into the Operating Room.

To transfer preoperatively planned data into the operating room (OR), registration is necessary as well as a method to localize the planned data in the OR. This data may comprise e.g. entry point positions into the human body in case of minimally invasive interventions or cutting trajectories. State of the art methods for localization are e.g.: • The robot itself is used as a pointing device (exploiting the forward kinematics) to position other devices and the workpiece with respect to each other. • An optically tracked pointer is used to find positions in the OR, assisted either by a VR representation of the scene where the pointer is visualized, or by simple commands ("move right/left/up/down..."). • The plan is projected with e.g. video or laser projectors. This work presents a first prototype of the autopointer, a new patent pending device using a handheld optically tracked laser scanner to localize preoperatively planned data in the operating room (OR)

A workspace analysis method to support intraoperative trocar placement in minimally invasive robotic surgery (MIRS)

This paper presents a new method to calculate and display an approximated workspace of a surgical robot in nearly realtime. Displaying this information on a screen in the operation room could support the surgeon during intraoperative trocar placement for teleoperated minimally invasive robotic surgery (MIRS). We give a short overview on existing trocar placement procedures in teleoperated MIRS and describe the possibilities and limitations of workspace analysis methods to support the surgeon during trocar placement. Our new method uses MIRS-specific simplifications to reduce the search space and enable the creation of a reduced workspace map. It was implemented for the DLR MiroSurge system. The implementation can create a reduced workspace map and display a mesh representation of the map in less than 2 seconds. We give a short outlook on how this method could be embedded in trocar placement procedures in the operation theater and what our future plans are with this method

Planning of Workplaces with Multiple Kinematically Redundant Robots

This thesis provides new methods for planning and optimization of robotic workplaces, i.e. workplaces with assistance of a robotic system. The robots may work autonomously or in cooperation with man. The thesis covers cooperation between multiple robots and lays stress on robots with kinematic redundancy. As highly demanding application area, optimal design and preoperative planning for minimally invasive and open robotic surgery is chosen. Optimization and planning of robotic workplaces are important instruments to cope with the increased complexity of today’s robotic applications and to ensure safe operation. Algorithms and devices to facilitate and partially automatize planning and optimization are, however, barely existent so far – with currently available tools the user mostly has to resort to a trial and error approach. Especially for complicated tasks requiring e.g. several robots cooperating in an unstructured environment, it is very improbable that a good (if at all sufficient) setup of the robotic workplace can be found this way. Therefore, the inclusion of algorithms to automatize the planning procedure as presented in this thesis is the evident next step to be taken. Closed form solutions for inverse kinematics and singularities provide the core of a reliable workplace optimization system and are developed in this thesis for serial kinematically redundant robots. Unlike state of the art methods, the presented inverse kinematics computation does not suffer from algorithmic singularities. The thesis describes an accordingly developed software library for inverse kinematics and shows both a planning procedure involving the medical robot KineMedic, and a real-time application, providing inverse kinematics for Cartesian control of the robotic system Justin with a computation time of lower than 0.6 ms for all 18 considered joints. Based on the closed form solutions, the thesis presents a complete procedure for workplace optimization and robot synthesis that uses a two-step algorithm based on Genetic Algorithms and a subsequent Sequential Quadratic Programming method. The thesis develops several optimization criteria and demonstrates the performance of the methods with a preoperative planning procedure as well as with the kinematic design optimization of the KineMedic system. The algorithms implemented in this thesis help the human during the decision taking procedure, by e.g. providing a preselection of (according to the chosen criteria) good solutions or by carrying out an optimization in a certain subspace while leaving the determination of the remaining parameters to the human. The thesis facilitates the transfer of a chosen setup into the real environment using a handheld contact-free surface-based registration procedure with an overall worst case error of 3 mm and a new handheld device to automatically project relevant structures into the real environment