Real‐time terrain traversability analysis and mapping for autonomous robotics in dynamic environments: fusing appearance‐ and geometry‐based approaches

This paper presents advanced methodologies for real‐time terrain analysis and mapping in autonomous robotic systems. The focus is on appearance‐based terrain traversability analysis and geometric‐based terrain traceability analysis. In the appearance‐based approach, an enhanced segmentation model using pixel‐based augmentation and 13 unique classes is proposed for reliable terrain classification. Semantic images are projected onto a 2.5D map by transforming two‐dimensional image data into a three‐dimensional coordinate system. The geometric‐based approach involves depth estimation from stereo cameras, employing three Zed‐2 cameras and the Depth Sensing application programming interface. The research contributes to improved perception and decision‐making capabilities of autonomous robots operating in complex and dynamic environments and also provides a new comprehensive data set named CranfieldTerra. Experimental results validate the effectiveness of the proposed methodologies, demonstrating their potential in various applications, such as search and rescue, agriculture, and exploration. This study establishes a foundation for further advancements in autonomous robotics, enhancing their ability to navigate safely and efficiently in challenging terrains.The first author acknowledges the Republic of Turkey, Ministry of National Education (YLYS), for supporting the studies under PhD scholarship ref:U9BYTAB2LDGA7LKJournal of Field Robotic

Techno-economic study for degraded gas turbine on pipeline application in the oil and gas industry.

Gas compression through pipelines is a capital intensive project. Therefore, it is imperative to investigate the viability of investing in such a project. Thus, the techno- economic and environmental risk assessment (TERA) tool to rapidly evaluate the entire natural gas pipeline project becomes vital. This research has investigated the impacts of gas turbine (GT) degradation in the application of TERA for a natural gas pipeline, taking into account the equipment selection, ambient conditions and periodic engine overhaul. Three scenarios (optimistic, medium and pessimistic) defining different levels of deterioration of the GT in comparison with the clean condition were examined in each season of the years (rainy, dry and hot season) based on the location of Trans-Saharan gas pipeline with 18 compression stations. The developed TERA model considered different modules such as the pipeline/gas compressor, performance, emission, a simplified lifing and economic module. The pipeline/gas compressor module evaluated the performance of the 4180km pipeline and gas compressor power across all compression stations in both isothermal and non- isothermal conditions. Aspen-Hysys/micro-soft excel and MATLAB were used to develop the model. The result showed that for every 1% increase in pipe exit pressure resulted in a 1.8% increase in the volume of the gas flow in the pipeline. Having evaluated the gas compressor (GC) power across the 18 compressor station, the investigation also revealed that for every 1% rise in the gas temperature resulted in a 3.4% rise in the power required by the gas compressor to move the gas. The GT performance was modelled using TURBOAMATCH at fixed power of the engine with respect to the different scenarios under investigation. The performance result was linked with the developed emission, lifing and economic model in MATLAB. The result revealed that for every 1% degradation (reduction in flow capacity and isentropic efficiency) at a constant power of engine operation, between an ambient temperature of 16.2ᴼC and 29ᴼC, CO₂ emission increases between 0.71% and 0.78% when compared with the clean condition. Also, at the same operating condition, the NOx emission increases between 1.66% and 1.8%. However, NOx emission at different compressor station varies from one station to another due to the influence of different ambient conditions, engine power settings and number of engines used. Lifing result showed that as the engine degrades, its creep life reduces at high TET to deliver the same power at a fixed number of engines Net present value (NPV) at different discount rates (DR) (0%, 5%, 10% and 15%) were used to evaluate the economic viability of the project, taking into account engine divestment and leasing for the redundant fleets after overhaul. The study further investigated how Rescheduling of GT Overhaul (ROH) from the baseline condition affects the economic viability of the pipeline project. The result showed that implementing the ROH reduces the number of GT used for the optimistic, medium and pessimistic scenarios by 8%, 2% and 4% respectively, for the same number of the compressor station and at the same operating conditions when compared with the baseline condition. The result also showed that running the engine on degraded mode increases the life cycle cost while the NPV reduces as the degradation increases. For instance, at 10% DR, the baseline NPV for the clean, optimistic, medium, and pessimistic scenarios were $21.5,$19.6, $18.4 and$17.1 billion, respectively showing that the NPV decreases with increase in degradation, unlike other studies that analysed the NPV on clean engine operation only. Remarkably, the NPV for engine divestment was 0.2% to 20.3% lower than the NPV for leasing depending on the different scenarios and DR, indicating that NPV leasing gives better benefits than that of engine divestment. Furthermore, the implementation of on-line compressor washing to investigate the impacts on the pipeline project and emission reduction using TURBOMATCH and MATLAB for the developed model revealed that the CO₂ emission and cost of CO₂ for the optimistic, medium and pessimistic scenarios had a reduction of 5.8%, 6.1% and 6.5% respectively when compared with the baseline condition. Also, at 5% DR, the NPV for the three scenarios after compressor washing increase by 6%, 5.2% and 4.8%, respectively when compared with the baseline case. The proposed methods and result in this research will offer a useful decision-making guide for all pipeline investors to invest in a natural gas pipeline business, taking into account different operating conditions and the impacts of engine degradation.PhD in Aerospac

Numerical analysis of crack path effects on the vibration behaviour of aluminium alloy beams and its identification via artificial neural networks

Understanding and predicting the behaviour of fatigue cracks are essential for ensuring safety, optimising maintenance strategies, and extending the lifespan of critical components in industries such as aerospace, automotive, civil engineering and energy. Traditional methods using vibration-based dynamic responses have provided effective tools for crack detection but often fail to predict crack propagation paths accurately. This study focuses on identifying crack propagation paths in an aluminium alloy 2024-T42 cantilever beam using dynamic response through numerical simulations and artificial neural networks (ANNs). A unified damping ratio of the specimens was measured using an ICP® accelerometer vibration sensor for the numerical simulation. Through systematic investigation of 46 crack paths of varying depths and orientations, it was observed that the crack propagation path significantly influenced the beam’s natural frequencies and resonance amplitudes. The results indicated a decreasing frequency trend and an increasing amplitude trend as the propagation angle changed from vertical to inclined. A similar trend was observed when the crack path changed from a predominantly vertical orientation to a more complex path with varying angles. Using ANNs, a model was developed to predict natural frequencies and amplitudes from the given crack paths, achieving a high accuracy with a mean absolute percentage error of 1.564%.Sensor

AI-assisted advanced propellant development for electric propulsion

Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean relative error of 6.87% and 7.99%, respectively, and a predicted ion mass with a 23.89% relative error. In the cases of full mass spectra due to electron ionisation, the predictions have a cosine similarity of 0.6395 and align with the top 10 most similar mass spectra in 78% of instances within a 30 Da range.Journal of Electric Propulsio

Visual servoing MPC framework for shipboard autonomous landing

Autonomous shipboard landing under harsh sea state conditions is a challenging task, with platform oscillations being one of the critical challenges to expand the flight envelope. Conventional GNC systems usually rely on deck-mounted equipment to supply relative pose data, which limits interoperability within a naval fleet. Image Based Vision Servoing (IBVS) controllers provide a complementary solution that only relies on feature detections and image plane processing. However, current approaches are limited to mild sea state conditions and mainly consider current ship states within the landing criteria. This can increase the risk of unsafe conditions under harsh sea states, such as rollover. This paper introduces a framework that integrates visual servoing with a nonlinear model predictive control (NMPC) to address the attitude oscillations of the landing pad by introducing a custom cost barrier function. Vessel states are forecasted in real-time using a Fast Fourier Transform (FFT) combined with a Discrete Kalman Filter (DKF), enabling short-term prediction without requiring prior knowledge of vessel dynamics. Simulation results and flight test validation demonstrate that the proposed approach achieves a higher success rate under high sea state condition compared to both conventional IBVS based on current or predicted states.This work was supported and sponsored by MBDA UK and the Brazilian Air Force through sponsorship P20227.AIAA Aviation Forum and Ascend 202

Estimation and visualisation of brain functional and effective connectivity

Functional and effective connectivity are two important concepts in the field of neuroscience that describe how different regions of the brain communicate and work together to support various cognitive and behavioural functions. Despite the many advances in functional and effective connectivity research, there are still several important research gaps that need to be addressed. This thesis explores the novel estimation and visualisation of brain functional and effective connectivity using electroencephalography recordings, with a particular focus on its potential impact on the diagnosis and monitoring of neurological disorders. This thesis proposes two novel methods for estimating brain functional connectivity and effective connectivity. The first method, Revised Hilbert-Huang Transformation, outperforms wavelet-based methods in terms of promising features and time-frequency resolution, providing a potential biomarker and diagnostic tool for Alzheimer's disease. The second method, causality detection attention-based convolutional neural networks, effectively estimates effective connectivity networks and identifies disrupted connectivity in Alzheimer’s disease patients. These methods contribute to the growing literature on connectivity estimation and offer valuable insights into the neural mechanisms underlying cognitive processes and neurodegenerative diseases, providing potential diagnostic and monitoring tools for healthcare professionals. This thesis also introduces a novel directed structure learning GNN (DSL-GNN) to leverage several EBC estimations to extract discriminative biomarkers for dementia classification. In studies of Alzheimer's disease, epilepsy, Parkinson's disease, and workload classification, the thesis demonstrates that the proposed brain connectivity methods have better performance compared with traditional methods based on individual channel. It suggests that functional and effective connectivity may track more changes from healthy people to patients to a certain extent, providing the possibility for earlier and more accurate diagnoses. Specifically, the thesis finds that specific regions of the brain can contribute to the diagnosis of epilepsy and dementia disease as well as workload classification based on brain connectivity. By advising the appropriate placement of electroencephalography sensors based on these identified regions, doctors and researchers can more efficiently and accurately diagnose and classify these neurological disorders, reducing the burden on healthcare systems.PhD in Manufacturin

Self-healing mechanism in polymer composite materials

The current self-healing mechanisms are still a long way from being fully implemented, and most published studies have only shown successful damage repair at the laboratory level. The complex nature of these mechanisms makes it difficult to implement them in real-life situations where the component or structure must continue to function. For complete healing, a molecular-level chemical reaction is required with the aid of external stimuli such as heating, light, and temperature change. Existing self-healing mechanisms are almost impossible to implement in critical applications such as 3D-printed products due to the requirements of external stimulations and reactions. The objective of this research is to investigate the strain release behaviour during crack growth of polymeric beams under elastic loads for self-healing. The mechanical behaviour of polymer components has been studied for many years, and their basic features are well understood. In this study, the elastic and plastic responses of 3D-printed beams made of Acrylonitrile butadiene styrene (ABS), thermoplastic polyurethane (TPU), and thermoplastic elastomers (TPE) were investigated under different bending loads. Two types of 3D-printed beams were designed to test their elastic and plastic responses under different bending loads. These responses were used to develop an innovative self-healing mechanism based on origami capsules that can be triggered by crack propagation due to strain release in a structure. The origami capsules, made of TPU in the form of a cross with four small beams either folded or elastically deformed, were embedded in a simple ABS beam. When crack propagation occurred in the ABS beam, the strain was released, causing the TPU capsule to unfold with the arms of the cross in the direction of the crack path. This increased the crack resistance of the ABS beam, which was validated in a delamination test of a double cantilever specimen under quasi-static load conditions. The results showed the potential of the proposed self-healing mechanism as a novel contribution to existing practises primarily based on external healing agents. The self-healing mechanism of TPU and TPE origami capsules has been demonstrated and reported for the first time. These materials achieved a good balance of mechanical strength and self-healing ability. A thicker beam structure tends to yield higher strain energy than do low thickness values for the beam. Since the strain energy release is dependent on how much cracking has propagated, so the higher strain release from the DCB TPU star and roll contributes to the rate at which crack propagation extends.PhD in Manufacturin

Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions

Due to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.This work was supported by the National Natural Science Foundation of China (Grants No. 22176075, 22406068), Natural Science Foundation of Jiangsu Province (BK20240884).Chemical Engineering Journa

Chip away everything that doesn't look like an elephant

This paper addresses the question of how conceptual models are created in a simulation modelling activity. Assuming an entity-based approach to simulation, some techniques for discovering good entity classes are considered, including personation. Also considered are the notations by which a conceptual model can be represented, and the modes of thought required for good conceptual modelling. Specifically excluded from consideration is the idea of applying a cut-and-dried method. The shortcomings of computers for conceptual modelling are remarked upon.12th Simulation Workshop (SW25

Integrating corporate identity, social responsibility, and reputation: a triadic framework for sustainable branding in hospitality & tourism

Previous studies have explored the impact of corporate identity (CI), corporate social responsibility (CSR), and corporate reputation (CR), but they have largely overlooked the effects of inconsistent CSR strategies on unexpected outcomes among hospitality employees. To address this gap, this study examines the interplay among CI, CSR, and CR within the hospitality industry. Adopting a multidisciplinary approach, the research reviews the literature from marketing, design, organizational studies, and management. It then employs qualitative methods, including interviews with managers and focus groups with employees, supplemented by a survey conducted among hospitality and tourism employees in the UK, Malaysia, and Iran. The findings reveal 20 critical CI factors across corporate communication, visual identity, and management behavior, demonstrating that CI influences CSR and CR. This study introduces a triadic framework that integrates CI, CSR, and CR, offering a holistic perspective essential for sustainable branding in hospitality.International Journal of Hospitality Managemen