Search

Current robotic endoluminal interventions require surgeons to hold a proximal joystick to control the distal flexible robot under 2-D X-ray guidance. However, the 2-D X-ray image is not intuitive, which not only increases the risk of surgical misoperation but also the workload of surgeons. Moreover, contact teleoperation exposes surgeons to the potentially infectious environment. To address it, this article proposes an augmented-reality-assisted touchless teleoperated robot for endoluminal intervention, called ARei. It aims to provide immersive experiences with augmented information. The robot integrates perceptual information obtained from an electromagnetic (EM) sensor, a shape sensor, and the virtual anatomy into a head-mounted display (HMD). The touchless teleoperation is used to control the robot, with the assistance of gesture recognition technology. Results show that the mean error of the calibration between the HMD and the EM tracking system is 4.67 mm, and the mean distance error between the points measured by the EM sensor and the points obtained by shape reconstruction with calibration is 5.19 mm (3.05%).

Cable-driven continuum manipulators have shown excellent benefits to work for endoluminal intervention. Demand of small diameter for confined anatomy limits the usage of multiple actuations, leading to limited DOFs and bending shapes. To address this problem, this letter proposes a multi-contact-aided continuum manipulator with anisotropic bending shapes. First, contact-aided compliant mechanisms (CCMs) are configured at different locations to introduce the specific constraints to enable the anisotropy. Then, the forward and inverse kinematic models considering contact blocks are built. The reachable workspace of the continuum manipulator is given, and simulation cases are studied to demonstrate the superiority of the proposed manipulator. Finally, a 3D-printed physical prototype is fabricated, and preliminary experiments are conducted to evaluate the model accuracy. Results show an average shape error of 1.98 mm (4.2%) and a maximum of 3.58 mm (7.6%). A robotic bronchoscopy platform integrated with the continuum manipulator is developed to conduct the airway phantom experiments. The shape deviation of the proposed manipulator from the centerline of the bronchus is 31.8% smaller than that of the constant curvature manipulator. The experiments validate the system-level feasibility and effectiveness of the developed continuum manipulator.

Purpose Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique. Based on the unique functions of TMS, it has been widely used in clinical, scientific research and other fields. Nowadays, the robot-assisted automatic TMS has become the trend. In order to simplify the operation procedures of robotic TMS and reduce the costs, the purpose of this paper is to apply the marker-based augmented-reality technology to robotic TMS system. Design/methodology/approach By using the marker of ARToolKitPlus library and monocular camera, the patient’s head is positioned in real time. Furthermore, the force control is applied to keep contact between the coil and subject’s head. Findings The authors fuse with visual positioning which is based on augmented-reality and force-control technologies to track the movements of the patient’s head, bring the coil closer to the stimulation site and increase treatment effects. Experimental results indicate that the trajectory tracking control of robotic TMS system designed in this paper is practical and flexible. Originality/value This paper provides a trajectory tracking control method for the robotic TMS. The marker-based augmented-reality technology is implemented which simplifies the operation procedures of robotic TMS as well as reduce the costs. During the treatment process, the patients would wear an AR glasses, which can help patients relax through virtual scenes and reduce the uncomfortableness produce by treatment.

Purpose This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment. Design/methodology/approach First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems. Findings The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller. Originality/value A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.