Recent advances in probe-based Confocal Laser Endomicroscopy (pCLE) enable real-time, in situ and in vivo tissue assessment at the micro scale. The limited field-of-view offered by pCLE necessitates the use of mosaicking to allow for accurate tissue characterization from the incoming image stream. However, mosaicking requires a series of contiguous good-quality images, which is particularly challenging because probe-tissue distance must be maintained within a very narrow working range at all times and probe-tissue contact force must be kept to a minimum so that tissue deformation is avoided. Robotic manipulation of the endomicroscopy probe has provided partial solution to these challenges, but sensorless approaches have not been thoroughly investigated up to date. This paper proposes a novel sensorless framework that uses a single non-reference image-quality metric to learn an approximation of tissue motion and subsequently track it. Moreover, a pCLE robotic tool for autonomous endomicroscopy scanning is designed and used for testing and validation purposes. Experiments on lens paper and ex vivo porcine tissue validate the philosophy of the framework.

Traditional robotic surgical systems rely entirely on robotic arms to triangulate articulated instruments inside the human anatomy. This configuration can be ill-suited for working in tight spaces or during single access approaches, where little to no triangulation between the instrument shafts is possible. The control of these instruments is further obstructed by ergonomic issues: The presence of motion scaling imposes the use of clutching mechanics to avoid the workspace limitations of master devices, and forces the user to choose between slow, precise movements, or fast, less accurate ones. This paper presents a bi-manual system using novel self-triangulating 6-degrees-of-freedom (DoF) tools through a flexible elbow, which are mounted on robotic arms. The control scheme for the resulting 9-DoF system is detailed, with particular emphasis placed on retaining maximum dexterity close to joint limits. Furthermore, this paper introduces the concept of gaze-assisted adaptive motion scaling. By combining eye tracking with hand motion and instrument information, the system is capable of inferring the user's destination and modifying the motion scaling accordingly. This safe, novel approach allows the user to quickly reach distant locations while retaining full precision for delicate manoeuvres. The performance and usability of this adaptive motion scaling is evaluated in a user study, showing a clear improvement in task completion speed and in the reduction of the need for clutching.

Effective in situ, in vivo tumour margin assessment is an important, yet unmet, clinical demand in surgical oncology. Recent advances in probe-based optical imaging tools such as confocal endomicroscopy is making inroads in clinical applications. In practice, maintaining consistent tissue contact whilst ensuring large area surveillance is crucial for its practical adoption and for this reason there is a great demand for robotic assistance so that high-speed endomicroscopes can be combined with autonomous scanning, thus simplifying its incorporation in routine surgical workflows. In this paper, a cooperatively controlled robotic manipulator is developed, which provides a stable mechatronically-enhanced platform for micro-scanning tools to perform local high resolution mosaics over 3D undulating moving surfaces. Detailed kinematic and overall system performance analyses are provided and the results demonstrate the adaptability in terms of both contact force and orientation control of the system, and thus its simplicity in practical deployment and value for clinical adoption.

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°.

Suturing is an essential requirement for surgical robots. Current designs of needle drivers require significant forces to be applied so that the needle will not flip or slip between the jaws when inserted into the tissue. The required force implies that most designs are based on rigid instruments with a straight shaft that induces less dexterity. Flexible robotics provides great dexterity to achieve the task, although often it lacks the capability to apply large forces and therefore hold the surgical needle firmly. Solutions can be found in how the needle driver's jaws are designed. This paper presents improved designs for needle grasping tools for flexible robotic instruments, to be produced directly with selective laser melting. Features on the gripping surface were embedded to increase the gripping effectiveness even in the presence of limited grasping force. A detailed characterisation has been performed for each design showing improvements with respect to the state of the art.

The performance of a master-slave robotic system depends significantly on the ergonomics and the capability of its master device to correctly interface the user with the slave robot. Master manipulators generating commands in task space represent a commonly adopted solution for controlling a range of slave robots while retaining an ergonomic design. However, these devices present several drawbacks, such as requiring the use of clutching mechanics to compensate for the mismatch between slave and master workspaces, and the lack of capability to intuitively transmit important information such as specific joint limits to the user. In this paper, a novel joint-space master manipulator is presented. This manipulator emulates the kinematic structure of highly flexible surgical instruments which it is designed to control. This system uses 6 active degrees of freedom to compensate for its own weight, as well as to provide force feedback corresponding to the slave robot's joint limits. A force/torque sensor integrated at the end effector is used to relay user-generated forces and torques directly to specific joints. This is performed to counteract the friction stemming from structural constraints imposed by the kinematic design of the instruments. Finally, a usability study is carried out to test the validity of the system, proving that the instruments can be intuitively controlled even at the extremities of the workspace.

This paper introduces the design and characterization of a robotic surgical instrument produced mainly with rapid prototyping techniques. Surgical robots have generally complex structures and have therefore an elevated cost. The proposed instrument was designed to incorporate minimal number of components to simplify the assembly process by leveraging the unique strength of rapid prototyping for producing complex, assemble-free components. The modularity, cost-effectiveness and fast manufacturing and assembly features offer the possibility of producing patient or task specific instruments. The proposed robot incorporates an integrated force measurement system, thus allowing the determination of the force exchanged between the instrument and the environment. Detailed experiments were performed to validate the functionality and force sensing capability of the instrument.

The use of conventional surgical tool holders requires an assistant during positioning and adjustment due to the lack of weight compensation. In this paper, we introduce a robotic arm system with hands-on control approach. The robot incorporates a force sensor at the end effector which realises tool weight compensation as well as hands-on manipulation. On the operating table, the required workspace can be tight due to a number of instruments required. There are situations where the surgical tool is at the desired location but the holder arm pose is not ideal due to space constraints or obstacles. Although the arm is a non-redundant robot because of the limited degrees of freedom, the pseudo-null-space inverse kinematics can be used to constrain a particular joint of the robot to a specific angle while the other joints compensate in order to minimise the tool movement. This allows operator to adjust the arm configuration conveniently together with the weight compensation. Experimental results demonstrated that our robotic arm can maintain the tool position during reconfiguration significantly more stably than a conventional one.

This paper presents a novel blind collision detection and material characterisation scheme for a compliant robotic arm. By the incorporation of a simple MEMS accelerometer at each joint, the robot is able to detect collision, identify the material of an obstacle, and create a map of the environment. Detailed hardware design is provided, illustrating its value for building a compact and economical robot platform. The proposed method does not require the additional use of vision sensor for mapping the environment, and hence is termed as `blind' collision detection and environment mapping. Based on the shock wave and vibration signals, the proposed algorithm is able to classify a range of materials encountered. Detailed laboratory evaluation was performed with controlled obstacle collision from different orientation and locations with varying force and materials. The proposed method has achieved 98% detection sensitivity while maintaining 77% specificity. Furthermore, by using sound feature extraction and machine learning techniques, the classifier produces an accuracy of 98% for classifying four different impact materials. In this paper, we also demonstrate its use for detailed environment mapping by using the proposed method.