External Steering of Vine Robots via Magnetic Actuation

This paper explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25 mm diameter vine robot with integrated magnetic tip capsule, including 6 Degrees of Freedom (DOF) localization and camera and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.

Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting

Computer vision technologies markedly enhance the automation capabilities of robotic-assisted minimally invasive surgery (RAMIS) through advanced tool tracking, detection, and localization. However, the limited availability of comprehensive surgical datasets for training represents a significant challenge in this field. This research introduces a novel method that employs 3D Gaussian Splatting to generate synthetic surgical datasets. We propose a method for extracting and combining 3D Gaussian representations of surgical instruments and background operating environments, transforming and combining them to generate high-fidelity synthetic surgical scenarios. We developed a data recording system capable of acquiring images alongside tool and camera poses in a surgical scene. Using this pose data, we synthetically replicate the scene, thereby enabling direct comparisons of the synthetic image quality (29.592 PSNR). As a further validation, we compared two YOLOv5 models trained on the synthetic and real data, respectively, and assessed their performance in an unseen real-world test dataset. Comparing the performances, we observe an improvement in neural network performance, with the synthetic-trained model outperforming the real-world trained model by 12%, testing both on real-world data.

Computational Design of Magnetic Soft Shape-Forming Catheters using the Material Point Method

Magnetic Soft Catheters (MSCs) are capable of miniaturization due to the use of an external magnetic field for actuation. Through careful design of the magnetic elements within the MSC and the external magnetic field, the shape along the full length of the catheter can be precisely controlled. However, modeling of the magnetic-soft material is challenging due to the complex relationship between magnetic and elastic stresses within the material. Approaches based on traditional Finite Element Methods (FEM) lead to high computation time and rely on proprietary implementations. In this work, we showcase the use of our recently presented open-source simulation framework based on the Material Point Method (MPM) for the computational design of magnetic soft catheters to realize arbitrary shapes in 3D, and to facilitate follow-the-leader shape-forming insertion.

Six-degree-of-freedom Localization Under Multiple Permanent Magnets Actuation

Localization of magnetically actuated medical robots is essential for accurate actuation, closed loop control and delivery of functionality. Despite extensive progress in the use of magnetic field and inertial measurements for pose estimation, these have been either under single external permanent magnet actuation or coil systems. With the advent of new magnetic actuation systems comprised of multiple external permanent magnets for increased control and manipulability, new localization techniques are necessary to account for and leverage the additional magnetic field sources. In this letter, we introduce a novel magnetic localization technique in the Special Euclidean Group SE(3) for multiple external permanent magnetic field actuation and control systems. The method relies on a milli-meter scale three-dimensional accelerometer and a three-dimensional magnetic field sensor and is able to estimate the full 6 degree-of-freedom pose without any prior pose information. We demonstrated the localization system with two external permanent magnets and achieved localization errors of 8.5 ? 2.4 mm in position norm and 3.7 ? 3.6? in orientation, across a cubic workspace with 20 cm length.

Independent Control of Two Magnetic Robots using External Permanent Magnets: A Feasibility Study

The ability to have multiple magnetic robots operate independently in the same workspace would increase the clinical potential of these systems allowing collaborative operation. In this work, we investigate the feasibility of actuating two magnetic robots operating within the same workspace using external permanent magnets. Unlike actuation systems based on pairs of electromagnetic coils, the use of multiple permanent magnets comes with the advantage of a large workspace which better suits the clinical setting. In this work, we present an optimization routine capable of generating the required poses for the external magnets in order to control the position and orientation of two magnetic robots. We show that at a distance of 15cm, minimal coupling between the magnetic robots can be achieved (3.9\% crosstalk) each embedded with 5mm diameter, 5mm length NdFeB magnets. At smaller distances, we observe that the ability to independently control the robot torques decreases, but forces can still achieve independent control even with alignment of the robots. We test our developed control system in a simulation of two magnetic robots following pre-planned trajectories in close proximity (60 mm) showing a mean positional error of 8.7 mm and mean angular error of 16.7 degrees.

Accelerating Surgical Robotics Research: Reviewing 10 Years of Research with the dVRK

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.