Continuum robots offer high flexibility and multiple degrees of freedom, making them ideal for navigating narrow lumens. However, accurately modeling their behavior under large deformations and frequent environmental contacts remains challenging. Current methods for solving the deformation of these robots, such as the Model Order Reduction and Gauss-Seidel (GS) methods, suffer from significant drawbacks. They experience reduced computational speed as the number of contact points increases and struggle to balance speed with model accuracy. To overcome these limitations, we introduce a novel finite element method (FEM) named Acc-FEM. Acc-FEM employs a large deformation quasi-static finite element model and integrates an accelerated solver scheme to handle multi-contact simulations efficiently. Additionally, it utilizes parallel computing with Graphics Processing Units (GPU) for real-time updates of the finite element models and collision detection. Extensive numerical experiments demonstrate that Acc-Fem significantly improves computational efficiency in modeling continuum robots with multiple contacts while achieving satisfactory accuracy, addressing the deficiencies of existing methods.

Haptic feedback enhances user interaction with systems by adding the sense of touch, thereby improving immersion and realism in applications like virtual reality (VR), augmented reality (AR), video games, education, and robotic surgery. To address the challenges in mechanically actuated haptic feedback devices such as limited mobility, mechanical wear, and complex mechanical structures, several research sought to develop electromagnetic haptic feedback systems. However, they also suffer from the rapid decay of magnetic force with distance, thus restricting their workspace size and application potential. In this paper, we propose a novel electromagnetic haptic feedback device that is actuated by magnetic torque instead of magnetic force. By controlling the magnetic torque, which decays with distance only at a thirdorder rate, our device achieves a large workspace—a 200-mm-diameter hemisphere—while still delivering perceptible realtime haptic feedback within the hemisphere. While using the device, the user wears a lightweight haptic thimble housing a permanent magnet on their finger, which enables 2 degree-offreedom (DoF) haptic feedback. A 13-coil electromagnet array serves as the source of the magnetic field. A mathematical model is proposed to determine the currents in the electromagnet array to generate the desired amount of haptic feedback torque. We conducted two experiments to prove the viability of the device. A haptic feedback accuracy experiment was conducted and validated the device's ability to generate sufficient torque within a large workspace. A user evaluation experiment showed that the device achieved an overall accuracy of 77.86% in a virtual enclosure exploration task, indicating its effectiveness and usability in haptic feedback applications.

Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.