A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

Respiratory Compensated Robot for Liver Cancer Treatment: Design, Fabrication, and Benchtop Characterization

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This paper presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a 4-DoF dual-stage cartesian platform used to control the pose of a 1-DoF needle insertion module. The active needle insertion module consists of a 3D printed flexible fluidic actuator capable of providing a step-like, grasp-insert-release actuation that mimics the manual insertion procedure. Force characterization of the needle insertion module indicates that the device is capable of producing 22.6 ± 0.40 N before the needle slips between the grippers. Static phantom targeting experiments indicate a positional error of 1.14 ± 0.30 mm and orientational error of 0.99° ± 0.36°. Static ex-vivo porcine liver targeting experiments indicate a positional error of 1.22 ± 0.31 mm and orientational error of 1.16° ± 0.44°. Dynamic targeting experiments with the proposed active motion compensation in dynamic phantom and ex-vivo porcine liver show 66.3% and 69.6% positional accuracy improvement, respectively. Future work will continue to develop this platform with the long-term goal of applying the system to RFA for HCC

Modellbasierte Herzbewegungsschätzung für robotergestützte Interventionen

Um bei robotergestützten Interventionen am schlagenden Herzen die Instrumente mit der Herzbewegung zu synchronisieren, ist eine Schätzung der Position des Interventionspunkts erforderlich. In dem vorgestellten Lösungsansatz wird unter Berücksichtigung von Positionsmessungen von Landmarken der Herzoberfläche, die durch optische Sensoren erfasst werden, die verteilte Herzbewegung modellbasiert geschätzt und die Herzoberfläche für beliebige Interventionspunkte rekonstruiert

Directional Estimation for Robotic Beating Heart Surgery

In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart

Mobility, Navigation and Localization Towards Robotic Endoscopy

With significant progress being made towards improving endoscope technology such as capsule endoscopy and robotic endoscopy, the development of advanced strategies for manipulating, controlling and more generally, easing the accessibility of these devices for physicians is an important next step. This work presents the development of several robotic platforms for experimentally testing navigation and localization strategies in robotic endoscopy followed by the development and testing of navigation strategies using these devices. Finally, visual and visual inertial localization and mapping is explored on two of these robotic systems. We first present a detailed description on the state-of-the-art with regard to minimally invasive robotic surgery and then follow this with in-depth description of our design and validation of two important systems, the Robotic Endoscope Platform (REP) and the Modular Endoscopy Simulation Apparatus (MESA), for exploring some of the challenges in robotic endoscopy. Following these descriptions we present a technique for autonomous navigation of the REP within the MESA as well as an attempt at applying Simultaneous Localization and Mapping (SLAM) to allow for the real-time localization of this system. Finally, we transition these techniques to the Endoculus, a complete robotic endoscope suitable for in vivo testing, and demonstrate both autonomous navigation for this device, and the implementation of three different SLAM systems for localization and mapping of the Endoculus system in real-time. Throughout these experiments we demonstrate the potential for advanced methods in computer vision along with other sensory techniques to substantially benefit endoscopy, enabling greater and greater autonomy of these systems and furthering the case for robotic endoscopy as a whole.</p