Shape sensing of miniature snake-like robots using optical fibers

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 CO 2 -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°

Rolling-joint design optimization for tendon driven snake-like surgical robots

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

Accessible digital ophthalmoscopy based on liquid-lens technology

Ophthalmoscopes have yet to capitalise on novel low-cost miniature optomechatronics, which could disrupt ophthalmic monitoring in rural areas. This paper demonstrates a new design integrating modern components for ophthalmoscopy. Simulations show that the optical elements can be reduced to just two lenses: an aspheric ophthalmoscopic lens and a commodity liquid-lens, leading to a compact prototype. Circularly polarised transpupilary illumination, with limited use so far for ophthalmoscopy, suppresses reflections, while autofocusing preserves image sharpness. Experiments with a human-eye model and cadaver porcine eyes demonstrate our prototype’s clinical value and its potential for accessible imaging when cost is a limiting factor

Model-free tuning of laguerre network for impedance matching in bilateral teleoperation system

This paper addresses the tuning method to attain symmetry between the master and slave manipulators of a bilateral teleoperation system. In the proposed structure, an equalizer based on the Laguerre network connected in-feedback loop to the master manipulator has been introduced. A set of input-output data were first generated and recorded which later be used in two-steps tuning procedure. A fictitious reference signal was formulated based on these data. In addition, a metaheuristic optimization algorithm namely the Particle Swarm Optimization has been employed in seeking the optimal controller’s parameters. Numerical analyses utilizing Matlab software has been performed. The results exhibited that the dynamic of the master manipulator with the added controller is almost identical to the dynamic of the slave systems. Hence, it is verified that the proposed tuning technique is feasible to achieve symmetry between both sides of the manipulators

Hubot: a three state human-robot collaborative framework for bimanual surgical tasks based on learned models

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

Endoscopic bi-manual robotic instrument design using a genetic algorithm

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 i 2 Snake 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

A framework for sensorless and autonomous probe-tissue contact management in robotic endomicroscopic scanning

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

Inverse kinematics control methods for redundant snakelike robot teleoperation during minimally invasive surgery

The real-time teleoperation or telemanipulation of redundant snakelike robots for minimally invasive surgery in a master-slave configuration is a complex problem. There are many possible mappings between a master's standard 6 degrees of freedom (DOF) and a redundant slave robot, typically with n ≫ 6 DOF. This letter introduces a snakelike robot for ear, nose, and throat surgery. The robot's architecture is comprised of n=26 joint variables. Six different control methods were investigated. The methods are compared through simulation with a user study. Each participant performed the same task using each of the six different control methods. Based on the metrics selected, the sparse pseudo-L 0 and our proposed approach performed better in terms of intuitiveness, real-time capabilities, and overall occupied volume

The i2Snake robotic platform for endoscopic surgery

Endoscopic procedures have transformed minimally invasive surgery as they allow the examination and intervention on a patient's anatomy through natural orifices, without the need for external incisions. However, the complexity of anatomical pathways and the limited dexterity of existing instruments, limit such procedures mainly to diagnosis and biopsies. This paper proposes a new robotic platform: the Intuitive imaging sensing navigated and kinematically enhanced ([Formula: see text]) robot that aims to improve the field of endoscopic surgery. The proposed robotic platform includes a snake-like robotic endoscope equipped with a camera, a light-source and two robotic instruments, supported with a robotic arm for global positioning and for insertion of the [Formula: see text] and a master interface for master-slave teleoperation. The proposed robotic platform design focuses on ergonomics and intuitive control. The control workflow was first validated in simulation and then implemented on the robotic platform. The results are consistent with the simulation and show the clear clinical potential of the system. Limitations such as tendon backlash and elongation over time will be further investigated by means of combined hardware and software solutions. In conclusion, the proposed system contributes to the field of endoscopic surgical robots and could allow to perform more complex endoscopic surgical procedures while reducing patient trauma and recovery time