Robot-assisted endovascular navigation provides significant advantages, including reduced radiation exposure for surgeons and improved patient safety. However, a major challenge is to control curvilinear instruments like guidewires precisely for smooth and accurate navigation while adapting to anatomical variations and external forces. Traditional segmentation-based approaches struggle with real-time prediction of the guidewire’s evolving shape, limiting their effectiveness in navigation tasks. In this paper, we propose SplineFormer, an explainable transformer network that predicts the continuous, structured representation of the guidewire as a B-spline. This formulation enables a compact, smooth, and explainable state representation that facilitates downstream navigation. By leveraging SplineFormer’s predictions within an imitation learning framework, our system successfully performs autonomous endovascular navigation. Experimental results show that SplineFormer achieves a 50% success rate when fully autonomously cannulating the Brachiocephalic Artery in a real robotic setup, demonstrating its potential for improved autonomous navigation in endovascular interventions.

Snake-like robots for endoscopic surgery make it possible to reach deep-seated lesions. With the use of small flexible tendon-driven instruments, it is possible to perform bimanual micro-surgical tasks that are challenging for standard endoscopic surgeries. Existing devices, however, lack articulated wrists and rolling motion of the end-effector. This paper presents a new instrument design with a distal-roll gripper for snake-like robots. The developed 5 DoFs miniaturized instruments with a diameter of 3 mm enable the deployment into narrow endoluminal channels. Issues related to actuation coupling, tendon slack, and backlash are addressed. Experimental results show that the distal-roll gripper can rotate 106°, and the actuated joints can achieve good repeatability and accuracy with the proposed compensation control scheme.

Snake-like robots are commonly used in Minimally Invasive Surgery as they are able to reach areas deep inside the human body. These robots have instruments that are deployed out of the robot's head and controlled via tendons, which connect the instrument to motors at the proximal end. In most currently available systems the instruments are lacking a rolling motion of the end-effector.In this paper, we present a new instrument prototype for a snake-like robot that can perform a stable in-place rolling motion. The prototype has a diameter of 4mm, uses 13 tendons and has 6 degrees of freedom. The robot can bend and roll to high angles, and strongly improves the dexterity compared to an instrument without rolling capabilities. In the evaluation we show that the rolling-tip gripper can rotate about 165° and is capable of applying forces up to 6.5N.

Over the last few years, there has been a significant rise in designing small, agile and flexible medical systems that can navigate through natural orifices. In the case of endoscopic surgery, existing systems vary significantly from each other which raises the question of the existence of a general design that can do it all. In this context, this paper proposes to use a genetic algorithm combined with recorded suturing and anatomical data to automatically design a pair of robotic instruments for the i2Snake under strict mechanical constraints. The resulting automatically generated instrument designs include a 6 degrees of freedom instrument that can follow a predefined trajectory accurately and a more simple 4 degrees of freedom instrument that can accomplish most of the task. The results also showed the importance of having a prismatic joint to gain the precision required for endoscopic surgery.

The use of snake-like robots for surgery is a popular choice for intra-luminal procedures. In practice, the requirements for strength, flexibility and accuracy are difficult to be satisfied simultaneously. This paper presents a computational approach for optimizing the design of a snake-like robot using serial rolling-joints and tendons as the base architecture. The method optimizes the design in terms of joint angle range and tendon placement to prevent the tendons and joints from colliding during bending motion. The resulting optimized joints were manufactured using 3D printing. The robot was characterized in terms of workspace, dexterity, precision and manipulation forces. The results show a repeatability as low as 0.9mm and manipulation forces of up to 5.6N.

Advances in optical imaging, and probe-based Confocal Laser Endomicroscopy (pCLE) in particular, offer real-time cellular level information for in-vivo tissue characterization. However for large area coverage, the limited field-of-view necessitates the use of a technique known as mosaicking to generate usable information from the incoming image stream. Mosaicking also needs a continuous stream of good quality images, but this is challenging as the probe needs to be maintained within an optimal working range and the contact force controlled to minimize tissue deformation. Robotic manipulation presents a potential solution to these challenges, but the lack of haptic feedback in current surgical robot systems hinders the technology's clinical adoption. This paper proposes a sensorless alternative based on processing the incoming image stream and deriving a quantitative measure representative of the image quality. This measure is then used by a controller, designed using model-free reinforcement learning techniques, to maintain optimal contact autonomously. The developed controller has shown near real-time performance in overcoming typical loss-of-contact and excess-deformation scenarios experienced during endomicroscopy scanning procedures.

Snake like continuum robots are increasingly used for minimally invasive surgery. Most robotic devices of this sort that have been reported to date are controlled in an open loop manner. Using shape sensing to provide closed loop feedback would allow for more accurate control of the robot's position and, hence, more precise surgery. Fiber Bragg Gratings, magnetic sensors and optical reflectance sensors have all been reported for this purpose but are often limited by their cost, size, stiffness or complexity of fabrication. To address this issue, we designed, manufactured and tested a prototype two-link robot with a built-in fiber-optic shape sensor that can deliver and control the position of a CO2-laser fiber for soft tissue ablation. The shape sensing is based on optical reflectance, and the device (which has a 4 mm outer diameter) is fabricated using 3D printing. Here we present proof-of-concept results demonstrating successful shape sensing - i.e. measurement of the angular displacement of the upper link of the robot relative to the lower link - in real time with a mean measurement error of only 0.7°.

The recent evolution of surgical robots has resolved a number of ergonomic issues associated with conventional minimally invasive surgery (MIS) in terms of aligned visiomotor axes, motion scaling and ergonomics. One of the latest advances is the introduction of human-robot cooperative control combining features such as active constraints, machine learning and automated movements. This paper aims to integrate these techniques into a framework which can be generalized to a wide range of surgical tasks. This paper proposes a system entitled Hubot; a Human-Robot collaborative framework which combines the strengths of the surgeon, the advantages of robotics and learning from demonstration into a single system. Hubot was successfully implemented on a Raven II surgical robot and a user study was conducted to evaluate its performance. Both a training and a simulated clinical case were investigated and showed promising results in comparison to fully manual task execution, including reduced completion time, fewer movements for the operator and improved efficiency.