Bio-inspired geotechnical engineering: principles, current work, opportunities and challenges

A broad diversity of biological organisms and systems interact with soil in ways that facilitate their growth and survival. These interactions are made possible by strategies that enable organisms to accomplish functions that can be analogous to those required in geotechnical engineering systems. Examples include anchorage in soft and weak ground, penetration into hard and stiff subsurface materials and movement in loose sand. Since the biological strategies have been ‘vetted’ by the process of natural selection, and the functions they accomplish are governed by the same physical laws in both the natural and engineered environments, they represent a unique source of principles and design ideas for addressing geotechnical challenges. Prior to implementation as engineering solutions, however, the differences in spatial and temporal scales and material properties between the biological environment and engineered system must be addressed. Current bio-inspired geotechnics research is addressing topics such as soil excavation and penetration, soil–structure interface shearing, load transfer between foundation and anchorage elements and soils, and mass and thermal transport, having gained inspiration from organisms such as worms, clams, ants, termites, fish, snakes and plant roots. This work highlights the potential benefits to both geotechnical engineering through new or improved solutions and biology through understanding of mechanisms as a result of cross-disciplinary interactions and collaborations

Bio-inspired geotechnical engineering: Principles, current work, opportunities and challenges

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Improving Soft Snake Robot Locomotion Through Targeted Environmental Interactions Using Artificial Snake Skin

This dissertation outlines the design and development of the �first fully-soft, snake robot and its snake-inspired skin. Soft robotics takes advantage of soft materials to, among other things, improve robot interactions with complex, unstructured environments. Due to the interplay between the soft material and the environment, minor tweaks to the morphological design of the robot can produce major changes in behavior when using the same control input. The research goal of this dissertation was to determine how the locomotion a soft snake robot, using a lateral undulation gait, can be improved by targeting a specific environmental interaction through the confluence of body design, gait design, and interfacial mechanism design. Understanding how these three areas of design can affect one another is key in developing robots that are adaptable in a range of environments. Each design area is addressed in a chapter of this dissertation to illustrate how changes to one area propagate to others, and how that can be an advantage to improving the locomotion of a soft robot. Chapter 3 examines how the body design of the robot changes its locomotion capabilities in granular media, focusing on interactions between the body and the ridges formed in the media. Chapter 4 illustrates how improvements to the gait can also be driven by interactions between the robot's body and the granular media. The design and implementation of an interfacial mechanism to further improve locomotion is described in Chapter 5. Kirigami, a Japanese art form involving the patterning of cuts in thin materials, is used to create a snake-inspired skin. The skin design targets directional friction, a morphological characteristic vital to snake locomotion in two axes. Most skins implemented for snake robots focus only on the longitudinal axis for creating directional friction. However, lateral undulation, the gait employed throughout this work, requires a significant lateral resistance to successfully create locomotion. This interfacial mechanism is designed speci�cally for the kinematics of the soft actuators as well as the production of directional friction in two axes, which required the creation of a new set of radial kirigami lattices. Each chapter demonstrates how improvements to locomotion can come from designing the morphological characteristics of the robot alongside the development of a gait and interfacial mechanisms by targeting specific, bioinspired interactions between the robot and the environment. The �final iteration of system resulted in a soft robot and it's snake-inspired skin with a 530% improvement in velocity over the original robot with no skin. The main contributions of this dissertation are: 1. The development of the �first fully-soft snake robot. 2. A skin for lateral undulation with two axes of directional friction 3. A set of new kirigami lattice structures that can be used for bending actuators 4. A framework in which to investigate bioinspired design of robots in three areas of design: morphology, gait, and interfacial mechanisms

Gait optimality for undulatory locomotion with applications to C. elegans phenotyping

This thesis focuses on the optimality and efficiency of organism locomotion strategies, specifically of microscopic undulators, in two distinct parts. Undulators loco- mote by propagating waves of bending deformation along their bodies, and at the microscale (ie low Reynolds number) interactions between undulators and their surroundings are well-described by biomechanical models due to high viscosity and negligible inertia. Frameworks such as resistive force theory enable the determination of optimal gaits for micro-undulators, often defined as the waveform maximising the ratio of swimming speed to energetic cost. Part I explores this avenue of research in a theoretical setting. Primary mathematical focus has been on finding optimal waveforms for straight-path forwards locomotion, but organisms do not move exclusively this way: turning and manoeuvring is key to survival. Here we establish a mathematical model, extend- ing previous approaches to modelling swimming micro-undulators, now introducing path curvature, to obtain optimal turning gaits. We obtain an analytical result demonstrating that high-curvature shapes minimise energetic cost when the penalty for bending is reduced. Imposing limitations on the curvature, and investigating multiple high-dimensional shape-spaces, we show that optimal turning results can be closely approximated as constant-curvature travelling waves. Part II adopts an experimental approach. Quantitative phenotyping tools can be used in behavioural screens of the model organism C. elegans to detect differences between wildtype and mutant strains. Expanding the current set of tools to include more orthogonal features could enable increased detection of deficiencies. Here we develop efficiency as a phenotyping lens for C. elegans, quantifying the gait optimality of rare human genetic disease model strains. Genetic diseases in humans are modelled in C. elegans with disease-associated orthologs. We find worm gait efficiency is found to correlate highly with percentage time paused. High efficiencies are exhibited during reversals and backing motions, due to suppressed head-swinging and increase in speed.Open Acces

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon

Detecting cells and cellular activity from two-photon calcium imaging data

To understand how networks of neurons process information, it is essential to monitor their activity in living tissue. Information is transmitted between neurons by electrochemical impulses called action potentials or spikes. Calcium-sensitive fluorescent probes, which emit a characteristic pulse of fluorescence in response to a spike, are used to visualise spiking activity. Combined with two-photon microscopy, they enable the spiking activity of thousands of neurons to be monitored simultaneously at single-cell and single-spike resolution. In this thesis, we develop signal processing tools for detecting cells and cellular activity from two-photon calcium imaging data. Firstly, we present a method to detect the locations of cells within a video. In our framework, an active contour evolves guided by a model-based cost function to identify a cell boundary. We demonstrate that this method, which includes no assumptions about typical cell shape or temporal activity, is able to detect cells with varied properties from real imaging data. Once the location of a cell has been identified, its spiking activity must be inferred from the fluorescence signal. We present a metric that quantifies the similarity between inferred spikes and the ground truth. The proposed metric assesses the similarity of pulse trains obtained from convolution of the spike trains with a smoothing pulse, whose width is derived from the statistics of the data. We demonstrate that the proposed metric is more sensitive than existing metrics to the temporal and rate precision of inferred spike trains. Finally, we extend an existing framework for spike inference to accommodate a wider class of fluorescence signals. Our method, which is based on finite rate of innovation theory, exploits the known parametric structure of the signal to infer the unknown spike times. On in vitro imaging data, we demonstrate that the updated algorithm outperforms a state of the art approach.Open Acces

Fast and Robust Automatic Segmentation Methods for MR Images of Injured and Cancerous Tissues

Magnetic Resonance Imaging: MRI) is a key medical imaging technology. Through in vivo soft tissue imaging, MRI allows clinicians and researchers to make diagnoses and evaluations that were previously possible only through biopsy or autopsy. However, analysis of MR images by domain experts can be time-consuming, complex, and subject to bias. The development of automatic segmentation techniques that make use of robust statistical methods allows for fast and unbiased analysis of MR images. In this dissertation, I propose segmentation methods that fall into two classes---(a) segmentation via optimization of a parametric boundary, and: b) segmentation via multistep, spatially constrained intensity classification. These two approaches are applicable in different segmentation scenarios. Parametric boundary segmentation is useful and necessary for segmentation of noisy images where the tissue of interest has predictable shape but poor boundary delineation, as in the case of lung with heavy or diffuse tumor. Spatially constrained intensity classification is appropriate for segmentation of noisy images with moderate contrast between tissue regions, where the areas of interest have unpredictable shapes, as is the case in spinal injury and brain tumor. The proposed automated segmentation techniques address the need for MR image analysis in three specific applications:: 1) preclinical rodent studies of primary and metastatic lung cancer: approach: a)),: 2) preclinical rodent studies of spinal cord lesion: approach: b)), and: 3) postclinical analysis of human brain cancer: approach: b)). In preclinical rodent studies of primary and metastatic lung cancer, respiratory-gated MRI is used to quantitatively measure lung-tumor burden and monitor the time-course progression of individual tumors. I validate a method for measuring tumor burden based upon average lung-image intensity. The method requires accurate lung segmentation; toward this end, I propose an automated lung segmentation method that works for varying tumor burden levels. The method includes development of a novel, two-dimensional parametric model of the mouse lungs and a multifaceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation: 0.93), comparable with that of fully manual expert segmentation, between the automated method\u27s tumor-burden metric and the tumor burden measured by lung weight. In preclinical rodent studies of spinal cord lesion, MRI is used to quantify tissues in control and injured mouse spinal cords. For this application, I propose a novel, multistep, multidimensional approach, utilizing the Classification Expectation Maximization: CEM) algorithm, for automatic segmentation of spinal cord tissues. In contrast to previous methods, my proposed method incorporates prior knowledge of cord geometry and the distinct information contained in the different MR images gathered. Unlike previous approaches, the algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation, even in the presence of significant injury. The results of the method are shown to be on par with expert manual segmentation. In postclinical analysis of human brain cancer, access to large collections of MRI data enables scientifically rigorous study of cancers like glioblastoma multiforme, the most common form of malignant primary brain tumor. For this application, I propose an efficient and effective automated segmentation method, the Enhanced Classification Expectation Maximization: ECEM) algorithm. The ECEM algorithm is novel in that it introduces spatial information directly into the classical CEM algorithm, which is otherwise spatially unaware, with low additional computational complexity. I compare the ECEM\u27s performance on simulated data to the standard finite Gaussian mixture EM algorithm, which is not spatially aware, and to the hidden-Markov random field EM algorithm, a commonly-used spatially aware automated segmentation method for MR brain images. I also show sample results demonstrating the ECEM algorithm\u27s ability to segment MR images of glioblastoma