Lakmal D. Seneviratne

State-of-the-Art in Force and Tactile Sensing for Minimally Invasive Surgery

Haptic perception plays a very important role in surgery. It enables the surgeon to feel organic tissue hardness, measure tissue properties, evaluate anatomical structures, and allows him/her to commit appropriate force control actions for safe tissue manipulation. However, in minimally invasive surgery, the surgeons ability of perceiving valuable haptic information through surgical instruments is severely impaired. Performing the surgery without such sensory information could lead to increase of tissue trauma and vital organic tissue damage. In order to restore the surgeons perceptual capability, methods of force and tactile sensing have been applied with attempts to develop instruments that can be used to detect tissue contact forces and generate haptic feedback to the surgeon. This paper reviews the state-of-the-art in force and tactile sensing technologies applied in minimally invasive surgery. Several sensing strategies including displacement-based, current-based, pressure-based, resistive-based, capacitive-based, piezoelectric-based, vibration-based, and optical-based sensing are discussed.

Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems

This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

State of the art in sensor technologies for sewer inspection

This paper reviews the state of the art in sensors and automated inspection devices for enhanced sewer inspection. Efficiency, safety, environmental, and legislative concerns have made inspection and assessment of communal sewers a central issue to water and sewerage companies. Nowadays, the standard sewer inspection system is based on a wheeled platform on which a closed circuit television (CCTV) camera is mounted. One of the disadvantages of camera inspection systems is that they can only detect a small proportion of all possible damage in a sewer. The inspection outcome of such systems relies not only on the quality of the acquired images, but also on the off-line. recognition and classification conducted by human operators. In consequence, CCTV-based platforms are frequently not effective. Infrared, microwave, optical, and ultrasonic-based sensors have been proposed to complement the existing CCTV-based approach and to improve inspection results. New inspection devices employing multiple sensors and being capable of carrying out remote sewer inspection tasks are under research.

A three-term backpropagation algorithm

Abstract The standard backpropagation algorithm for training artificial neural networks utilizes two terms, a learning rate and a momentum factor. The major limitations of this algorithm are the existence of temporary, local minima resulting from the saturation behaviour of the activation function, and the slow rates of convergence. In this paper, the addition of an extra term, a proportional factor, is proposed in order to speed-up the weight adjusting process. This new three-term backpropagation algorithm is tested on three example problems and the convergence behaviour of the three-term and the standard two-term backpropagation algorithm are compared. The results show that the proposed algorithm generally out-performs the conventional algorithm in terms of convergence speed and the ability to escape from local minima.

Rolling Mechanical Imaging for Tissue Abnormality Localization During Minimally Invasive Surgery

We describe a novel approach for the localization of tissue abnormalities during minimally invasive surgery using a force-sensitive wheeled probe. The concept is to fuse the kinaesthetic information from the wheel-tissue rolling interaction into a pseudocolor rolling mechanical image (RMI) to visualize the spatial variation of stiffness within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. Initially, a testing facility for validating the concept in an ex vivo setting was developed and used to investigate rolling ¿wheel-tissue¿ interaction. A silicone soft-tissue phantom with embedded hard nodules was constructed to allow for experimental comparison between an RMI and a known soft-tissue structure. Tests have also been performed on excised porcine organs to show the efficacy of the method when applied to biological soft tissues. Results indicate that the RMI technique is particularly suited to identifying the stiffness distribution within a tissue sample, as the continuous force measurement along a given rolling trajectory provides repeatable information regarding relative variations in the normal tissue response. When compared to multiple discrete uniaxial indentations, the continuous measurement approach of RMI is shown to be more sensitive and facilitates coverage of a large area in a short period of time. Furthermore, if parametric classification of tissue properties based on a uniaxial tissue indentation model is desirable, the rolling indentation probe can be easily employed as a uniaxial indenter.

A sensor guided autonomous parking system for nonholonomic mobile robots

An automated parallel parking strategy for a car-like mobile robot is presented. The study considers general cases of parallel parking for a rectangular robot within a rectangular space. The system works in three phases. In scanning phase, the parking environment is detected by ultrasonic sensors mounted on the robot and a parking position and manoeuvring path is produced if the space is sufficient. Then in the positioning phase, the robot reverses to the edge of the parking space avoiding potential collisions. Finally, in manoeuvring phase, the robot moves to the parking position in the parking space in a unified pattern, which may requires backward and forward manoeuvres depending on the dimensions of the parking space. Motion characteristics of this kind of robots are modeled, taking into account the nonholonomic constraints acting on the car-like robot. A collision-free path is planned in reference to the surroundings. The strategy has been integrated into an automated parking system, and implemented in a modified B12 mobile robot, showing capable of safe parking in tight situations.

MRI-Compatible Fiber-Optic Force Sensors for Catheterization Procedures

Cardiac catheterization is an interventional procedure that is usually carried out without the use of force sensors. During such procedures the physician mainly relies on visual feedback provided by an imaging modality, like X-ray fluoroscopy, Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). Hence, the physician it is not always able to predict the forces between the catheter and blood vessel walls. Sometimes, tasks such as moving a catheter through delicate blood vessel networks and through the heart chambers become difficult. This paper provides an overview of fiber-optic pressure and force sensors for cardiac catheters with potential for providing haptic feedback. In conjunction with an MRI scanner the overall cardiac catheterization procedure could be enhanced. The paper focuses on fiber-optic sensors due to their very good MRI compatibility. Background information on manual and robotic catheterization approaches is provided together with an analytic discussion of the current state-of-the-art in fiber-optic force and pressure sensors for catheters, which can provide haptic information.

Rolling Indentation Probe for Tissue Abnormality Identification During Minimally Invasive Surgery

This paper presents a novel optical fiber-based rolling indentation probe designed to measure the stiffness distribution of a soft tissue while rolling over the tissue surface during minimally invasive surgery. By fusing the measurements along rolling paths, the probe can generalize a mechanical image to visualize the stiffness distribution within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. The performance of the developed probe was validated using simulated soft tissues. Results show that the probe can measure both force and indentation depth accurately with different orientations when the probe approached and rolled on the tissue surface. In addition, experiments for tumor, identification through rolling indentation were conducted. The size and embedded depth of the tumor, as well as the stiffness ratio between the tumor and tissue, were varied during tests. Results demonstrate that the probe can effectively and accurately identify the embedded tumors.

Pipe inspection using a laser-based transducer and automated analysis techniques

This paper presents a new sensing methodology for the automated inspection of pipes. Standard inspection systems, as they are for example used in waste pipes and drains, are based on closed-circuit television cameras which are mounted on remotely controlled platforms and connected to remote video recording facilities. Two of the main disadvantages of such camera-based inspection systems are: 1) the poor quality of the acquired images due to difficult lighting conditions and 2) the susceptibility to error during the offline video assessment conducted by human operators. The objective of this research is to overcome these disadvantages and to create an intelligent sensing approach for improved and automated pipe-condition assessment. This approach makes use of a low-cost lighting profiler and a camera which acquires images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the acquired images is introduced. The image data analysis is based on differential processing leading to highly-noise tolerant algorithms, particularly well suited for the detection of small faults in harsh environments. With the subsequent application of artificial neural networks, the system is capable of recognizing defective areas with a high success rate. Experiments in a range of waste pipes with different diameters and material properties have been conducted and test results are presented.

Automated Pipe Defect Detection and Categorization Using Camera/Laser-Based Profiler and Artificial Neural Network

Closed-circuit television (CCTV) is currently used in many inspection applications, such as the inspection of nonaccessible pipe surfaces. This human-oriented approach based on offline analysis of the raw images is highly subjective and prone to error because of the exorbitant amount of data to be assessed. Laser profilers have been recently proposed to project well-defined light patterns, improving the illumination of standard CCTV systems as well as enhancing the capability of automating the assessment process. This research shows that positional (geometrical) as well as intensity information, related to potential defects, can be extracted from the acquired laser projections. While most researchers focus on the analysis of positional information obtained from the acquired profiler signals, here the intensity information contained within the reflected light is also exploited for the purpose of defect classification and visualization. This paper describes novel strategies created for the automation of defect classification in tubular structures and explores new methods to fuse intensity and positional information, achieving improved multivariable defect classification. The acquired camera/laser images are processed in order to extract signal information for the purpose of visualization and map creation for further assessment. Then, a two-stage approach based on image processing and artificial neural networks is used to classify the images. First, a binary classifier identifies defective pipe sections, and then in a second stage, the defects are classified into different types, such as holes, cracks, and protruding obstacles. Experimental results are provided. Note to Practitioners-The method presented in this paper aims to automate the inspection of nonaccessible pipe surfaces. The method was thought to be employed in the inspection of sewers; however, it could be used in many other industrial applications and could also be extended to other shapes rather than tubular structures. A laser ring profiler, consisting, for instance, of a laser diode and a ring projector, can be easily integrated into existing closed-circuit television systems. The proposed algorithm identifies defective areas and categorizes the types of defects, analyzing the successive recorded camera images that will contain the reflected ring of light. The algorithm, that can be used online, makes use of the deformation of the reflected laser ring together with its changes in intensity. The fact of combining the two kinds of data using artificial-intelligent algorithms makes the method robust enough to work in harsh environments