Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot’s ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

Transorbital Neuro Surgery (TNS) offers a novel treatment towards the lesion inside skull pursuing minimal invasiveness. Most conventional TNS tools are rigid and straight, limiting the dexterity and accessibility in passing a small port. Bendable and steerable surgical tools provides an alternative for this issue. In this work, we proposed a dual-segment slender surgical robot arm for TNS, which is a Co-Axial Slender Tubular robot (CAST), and modelled it using novel approaches. Another contribution is tendon-mortise shaped slits along the axial direction, enhancing the overall stiffness. The bending of CAST is actuated by pushing/pulling distance, and the maximum diameter is only 1.7mm with high dexterity after mounting on a rigid robot arm. Experiments demonstrates that the proposed the slit design doubles the stiffness properties compared to traditional rectangle slit designs. The path-following task shows that the position error was maximally 3mm in open-looped control. Test on a skull model demonstrates that the whole system could successfully perform electrocoagulation procedure inside the depth of skull in a robotized manner effectively.

Severe chronic pulmonary endarterectomy needs a dissector to delicately remove proliferative intima located in the depth of the pulmonary artery. This work proposed a novel endoscopic robotized steerable dissector for this surgery, enabling easier access to curved deep artery branches. The handheld surgical dissector also provides suction and visualization for surgeons to enhance effectiveness. The steerable section is a cable-driven hinged structure, and through an antagonistic mechanism regulating the cable tension, the overall stiffness is adjusted to adapt various surroundings. The mapping between actuation space and shape configuration and tip force estimation model are respectively established for further closed-loop control scheme, achieving adaptive positioning and safe surgery. Experiments first demonstrate the feasibility of the proposed models and ex vitro trials validated the usage and effectiveness of the robotized dissector.

Complex pipeline network should be inspected regularly for safety. In general, these tasks are often completed by large pipe-climbing robots or customized equipment. Most of them are not effective, and cannot work on pipes with uncertain barriers. Moreover, some pipes are mounted in constrained scenarios, so bulky robots are not applicable. This paper presents a tethered soft-rigid pipe-climbing robot to fill the gap. Two indispensable actions, i.e. embracing pipe and moving along it, are realized by a soft component and a 3D printed wheel mechanism. The latter includes two forces: thrust force from the compressed air and tractive force from wheels, to drive the robot comprehensively, and a lightweight body (only 160g) benefits agile motion. In operation, pressure exerted on the soft component enables the robot to embrace pipes of different diameters, with controllable adhesion force, and locomotion force is also regulated. Inspired from vehicle, an elastic damper is attached between the wheel structure and the robot body, which can effectively alleviate vibration when crossing barriers. In addition, theoretical models are constructed to analyse and control thrust force, and the locomotion performance is analysed by dynamics model. Experiments demonstrate that this robot can perform rapid climbing at a speed of 1.09m/s in load-free scenarios, and it can move at a maximum speed of 0.828m/s with 500g load. Reconstruction of a flexible pipe using the robot is also demonstrated.