Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Bacteria classification with an electronic nose employing artificial neural networks

This PhD thesis describes research for a medical application of electronic nose technology. There is a need at present for early detection of bacterial infection in order to improve treatment. At present, the clinical methods used to detect and classify bacteria types (usually using samples of infected matter taken from patients) can take up to two or three days. Many experienced medical staff, who treat bacterial infections, are able to recognise some types of bacteria from their odours. Identification of pathogens (i.e. bacteria responsible for disease) from their odours using an electronic nose could provide a rapid measurement and therefore early treatment. This research project used existing sensor technology in the form of an electronic nose in conjunction with data pre-processing and classification methods to classify up to four bacteria types from their odours. Research was performed mostly in the area of signal conditioning, data pre-processing and classification. A major area of interest was the use of artificial neural networks classifiers. There were three main objectives. First, to classify successfully a small range of bacteria types. Second, to identify issues relating to bacteria odour that affect the ability of an artificially intelligent system to classify bacteria from odour alone. And third, to establish optimal signal conditioning, data pre-processing and classification methods. The Electronic Nose consisted of a gas sensor array with temperature and humidity sensors, signal conditioning circuits, and gas flow apparatus. The bacteria odour was analysed using an automated sampling system, which used computer software to direct gas flow through one of several vessels (which were used to contain the odour samples, into the Electronic Nose. The electrical resistance of the odour sensors were monitored and output as electronic signals to a computer. The purpose of the automated sampling system was to improve repeatability and reduce human error. Further improvement of the Electronic Nose were implemented as a temperature control system which controlled the ambient gas temperature, and a new gas sensor chamber which incorporated improved gas flow. The odour data were collected and stored as numerical values within data files in the computer system. Once the data were stored in a non-volatile manner various classification experiments were performed. Comparisons were made and conclusions were drawn from the performance of various data pre-processing and classification methods. Classification methods employed included artificial neural networks, discriminant function analysis and multi-variate linear regression. For classifying one from four types, the best accuracy achieved was 92.78%. This was achieved using a growth phase compensated multiple layer perceptron. For identifying a single bacteria type from a mixture of two different types, the best accuracy was 96.30%. This was achieved using a standard multiple layer perceptron. Classification of bacteria odours is a typical `real world' application of the kind that electronic noses will have to be applied to if this technology is to be successful. The methods and principles researched here are one step towards the goal of introducing artificially intelligent sensor systems into everyday use. The results are promising and showed that it is feasible to used Electronic Nose technology in this application and that with further development useful products could be developed. The conclusion from this thesis is that an electronic nose can detect and classify different types of bacteria

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Visually-guided timing and its neural representation

Stimulus-driven timing is a fundamental aspect of human and animal behavior. This type of timing can be subdivided into three principal axes: interval generation, storage, and evaluation. In this thesis, we present results related to each of these axes and describe their implications for how we understand timed behavior. In Chapter 2, we address interval generation, which is the process of creating an internal representation of an ongoing temporal interval. While several studies have found evidence for neural oscillators which may subserve this function, it has remained an open question whether such a mechanism can be useful for timing at even the lowest level of cortex. To address this question, we analyze electrophysiological data collected from rats performing a timing task and find evidence that, indeed, timed reward-seeking behavior tracks oscillatory states in primary visual cortex. This kind of finding raises an important question: how is this temporal information stored after the interval has been generated? This process is called interval storage, and we address the sources of noise that might corrupt it in Chapter 3. Specifically, we devise a novel timing task for humans (BiCaP) to address whether memory biases can account for performance on a classification task, in which a subject must decide whether a test interval is more similar to one or another reference interval. We find that they do, and argue that these sources of noise must be accounted for in theories of timing. In Chapter 4, we deal with interval evaluation which is the process of using this stored temporal information to make valuation decisions. We study this process through the lens of foraging behavior. Specifically, we develop and test a framework that rationalizes observed spatial search patterns of wild animals and humans by accounting for the temporal information they gather about their environment, and how they discount delayed rewards (temporal discounting). Lastly, in Chapter 5, we discuss how these processes are integrated and the implications of these findings for theories of timing

37th Rocky Mountain Conference on Analytical Chemistry

Final program, abstracts, and information about the 37th annual meeting of the Rocky Mountain Conference on Analytical Chemistry, co-sponsored by the Colorado Section of the American Chemical Society and the Rocky Mountain Section of the Society for Applied Spectroscopy. Held in Denver, Colorado, July 23-27, 1995

The effects of upstream Natural Flood Management on urban surface drainage performance

The evaluation of rural Natural Flood Management (NFM) has traditionally focused on the ability of interventions to mitigate downstream fluvial flooding by attenuating catchment response. This research expands this focus – investigating whether these same interventions could also mitigate surface flood risk in downstream urban areas. By moderating water levels in receiving watercourses, upstream NFM could promote free discharge from urban drainage outfalls and thereby improve local surface drainage performance. A novel modelling methodology has been developed to characterise the response of three separate catchments – the Bin Brook in Cambridgeshire, the Asker in Dorset and a sub-catchment of the upper Calder in Yorkshire. The upstream, rural response is simulated by coupling Dynamic TOPMODEL (a semi-distributed hydrological model) and HEC-RAS (which solves the shallow flow equations). This offers a freely-available, spatially-informed approach for the evaluation of a range of upstream NFM interventions (located both within and beyond the riparian zone) at a catchment-scale. This modelled rural response then provides the input for a downstream, integrated urban drainage model (Infoworks ICM). This is then be used to examine how any consequent changes in outfall inundation by the urban watercourse alter local drainage performance. Each case study is examined separately before a comparative study of all three is undertaken to identify broader trends. These trends suggest that during more frequent events (e.g. 1 in 10 year), upstream NFM interventions create the greatest reductions for the time low-lying outfalls are submerged by local watercourses. As storm severity increases (heightening risk of drainage surcharge or failure) these reductions diminish. Despite this, the slight delay in rural response continues to allow more water to escape surface systems before outfall inundation occurs, increasing the effective capacity of networks and reducing surface flood volumes. While any improvements to outfall discharge would not, in themselves, justify NFM implementation, these interventions have the potential to contribute to a downstream water level management strategy in certain locations and therefore will be of interest to urban flood risk practitioners.Engineering and Physical Sciences Research Counci

Time domain reflectometry imaging - A new moisture measurement technique for industry and soil science

This thesis describes the theoretical and practical aspects of a new technique for quantitative, non-invasive and non-destructive imaging of the near-surface moisture content distribution of composite materials. The technique relies on the alteration by a nearby composite material, of the propagation velocity of an electromagnetic pulse along a parallel transmission line, through distortion of the evanescent field. A set of measurements taken at different relative positions of the transmission line and composite material are, in conjunction with a forward model describing propagation velocity on the line, inverted to provide the image of moisture content distribution. Development of the technique, called 'Time Domain Reflectometry Imaging' (TDRI), involved four steps: 1. Instrumentation to obtain a set of measurements of propagation times; 2. A forward model; 3. An inverse procedure; and 4. Conversion of a calculated permittivity distribution to a moisture distribution. Critical to the success of the inverse method is a set of measurements of propagation velocity that provide pico-second propagation time accuracy, and are sufficiently linearly independent to enable discrimination of the permittivity of each discretised cell within the composite material. Using commercial time domain reflectometry (TDR) instruments, a switched reference measurement, waveform subtraction and intersecting waveform tangents, sufficient timing accuracy has been achieved. The forward model was developed using the moment method. The advantage of such an integral equation method is that recalculation is not required when changing the impressed field. Hence for a particular model of the composite material's moisture distribution, just one execution of the forward model provides predicted propagation velocities for all positions of the transmission line. A new pseudo 3-D variant of the volume integral equation approach was developed to suit the 2-D transmission line, and resulted in a 100 fold reduction in memory use, and a greater than 10 fold reduction in execution time. The forward solution uses the telegrapher's equation to predict propagation velocity from an arbitrary permittivity distribution surrounding the line. Inversion of the measured data was accelerated by the use of three novel tactics: a rapid electric field surrogate for the Jacobian; a dynamic method of determining the conjugate gradient weighting factor; and a new blocking technique that accelerated the convergence of buried cells that have only a small influence on propagation velocity. The final TDRI step is a numerical model to translate both the a priori moisture distribution data to a permittivity distribution, and conversely the solution permittivity distribution to moisture content. A dielectric model based on an earlier model of Looyenga was adapted to include both the different characteristic of tightly held water, and the Debye relaxation of free water. The intention was a model with applicability to a range of composite materials. It was tested with data for soil, bentonite clay and wood, and except for one free parameter, model parameters were set by measurable physical properties of the host material