Dragline excavation simulation, real-time terrain recognition and object detection

The contribution of coal to global energy is expected to remain above 30% through 2030. Draglines are the preferred excavation equipment in most surface coal mines. Recently, studies toward dragline excavation efficiency have focused on two specific areas. The first area is dragline bucket studies, where the goal is to develop new designs which perform better than conventional buckets. Drawbacks in the current approach include operator inconsistencies and the inability to physically test every proposed design. Previous simulation models used Distinct Element Methods (DEM) but they over-predict excavation forces by 300% to 500%. In this study, a DEM-based simulation model has been developed to predict bucket payloads within a 16.55% error. The excavation model includes a novel method for calibrating formation parameters. The method combines DEM-based tri-axial material testing with the XGBoost machine learning algorithm to achieve prediction accuracies of between 80.6% and 95.54%. The second area is dragline vision studies towards efficient dragline operation. Current dragline vision models use image segmentation methods that are neither scalable nor multi-purpose. In this study, a scalable and multi-purpose vision model has been developed for draglines using Convolutional Neural Networks. This vision system achieves an 87.32% detection rate, 80.9% precision and 91.3% recall performance across multiple operation tasks. The main novelty of this research includes the bucket payload prediction accuracy, formation parameter calibration and the vision system accuracy, precision and recall performance toward improving dragline operating efficiencies --Abstract, page iii

Theory and Practice of Tunnel Engineering

Tunnel construction is expensive when compared to the construction of other engineering structures. As such, there is always the need to develop more sophisticated and effective methods of construction. There are many long and large tunnels with various purposes in the world, especially for highways, railways, water conveyance, and energy production. Tunnels can be designed effectively by means of two and three-dimensional numerical models. Ground–structure interaction is one of the significant factors acting on economic and safe design. This book presents recent data on tunnel engineering to improve the theory and practice of the construction of underground structures. It provides an overview of tunneling technology and includes chapters that address analytical and numerical methods for rock load estimation and design support systems and advances in measurement systems for underground structures. The book discusses the empirical, analytical, and numerical methods of tunneling practice worldwide

Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation

Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation

Internet of Things for Sustainable Mining

The sustainable mining Internet of Things deals with the applications of IoT technology to the coupled needs of sustainable recovery of metals and a healthy environment for a thriving planet. In this chapter, the IoT architecture and technology is presented to support development of a digital mining platform emphasizing the exploration of rock–fluid–environment interactions to develop extraction methods with maximum economic benefit, while maintaining and preserving both water quantity and quality, soil, and, ultimately, human health. New perspectives are provided for IoT applications in developing new mineral resources, improved management of tailings, monitoring and mitigating contamination from mining. Moreover, tools to assess the environmental and social impacts of mining including the demands on dwindling freshwater resources. The cutting-edge technologies that could be leveraged to develop the state-of-the-art sustainable mining IoT paradigm are also discussed

SIMULATION TECHNOLOGY OF SUGAR BEET

Технологія виробництва коренеплодів цукрових буряків є поєднання агротехнічних прийомів і технічнихзасобів, покликаних їх виконувати. Моделі таких технологічних процесів пропонується розглядати як складнісистеми, які характеризуються великою кількістю взаємопов’язаних між собою підсистем та елементів,різноманітністю вимог до окремих ланок технологічного процесу, випадковим характером, інваріантністюструктури, неоднорідність фізичної природи і т.п. Тому моделювання таких технологічних процесів повиннобазуватися на певному наборі принципів, що забезпечує методику побудови моделей шляхом уніфікаціїпроцедур моделювання, класифікації критеріїв оцінювання та узагальнення технологічних змінних, колиосновними принципами моделювання технології вирощування і збирання цукрових буряків визначаються:– доцільність моделювання;– наявність достатньої інформації;– множинність;– агрегативність;– координованість.Технология производства корнеплодов сахарной свеклы объединяет несколько агротехнических приемов итехнических средств, которые нужны для их работы. Модели таких технологических процессов предлагаетсярассмотреть как сложные системы, которые характеризируются большим количеством взаимосвязанных междусобою подсистем та элементов, разнообразием требований к отдельным звеньям технологического процесса,случайным характером,, инвариантностью структуры, неоднородностью физической природы и т.п. Поэтомумоделирование таких технологических процессов должно базироваться на определенном наборе принципов,что в свою очередь дает возможность методике построения моделей путем унификации процедурмоделирования, класификации критериев оценивания и обьединения технологических значений, кордаосновними принципами моделирования технологи выращивания и сборки сахарной свеклы определяются :– оптимальность моделирования;– наявность нужной информации;– агрегативность;– множественность;– координованость.The Article highlights specific features of process simulation and technical facilities related to sugar beetproduction. It reviews the literature sources and specifies distinctive properties of the created models . Basing upon theanalysis conducted the main principles of process simulation of sugar beet growing and cropping are proposed

Automatic estimation of excavator actual and relative cycle times in loading operations

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator\u27s actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine\u27s performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites

A Robotic System for In-Situ Measurement of Soil Total Carbon and Nitrogen

Surges in the cost of fertilizer in recent times coupled with the environmental effects of their over-application have driven the need for farmers to optimize the amount of fertilizer they apply on the farm. One of the key steps in determining the right amount of fertilizer to apply in a given field is measuring the amount of nutrients present in the soil. To ascertain nutrient deficiencies, most farmers perform wet chemistry analysis of soil samples which requires a lot of time and is expensive. In this research project, a robotic system was designed and developed that could autonomously move to predetermined GPS waypoints and estimate total carbon (TC) and total nitrogen (TN) content in the soil in-situ using visible and near-infrared reflectance spectroscopy - a faster and cheaper method to determine soil nutrients in real-time. For the locomotion of the robotic system, a Husky robotic platform by Clearpath Robotics was used. A Gen2 robotic arm by Kinova Robotics was used for the precise positioning of the probe in taking soil spectral measurement. The probe was custom designed and built to be used in conjunction with the robotic arm as an end-effector. Two lightweight and inexpensive spectrometers by OceanInsight, namely, Flame VisNIR and Flame NIR+, were used to capture the spectral signatures of soil. The prediction was done with a spectroscopic calibration model and External Parameter Orthogonalization (EPO) was applied to remove the moisture effect from the soil spectra. The robotic system was tested at University of Nebraska-Lincoln (UNL) NU-Spidercam phenotyping facility. Two sets of spectra were obtained from the field campaign: in-situ and dry-ground spectra. The dry-ground spectra were used as library scans and Partial Least Square Regression (PLSR) was used for the modeling. The in-situ spectra were randomly divided into EPO calibration and validation sets. Satisfactory results were obtained from the initial prediction on dry-ground validation set, with R2 (coefficient of determination) of 0.77 and RMSE (Root Mean Squared Error) of 0.15% for TC and R2 of 0.64 and RMSE of 171 ppm for TN. There was a reduction in R2 and an increase in RMSE values for both TC and TN when prediction was done directly on the in-situ validation set. For TC, the R2 dropped and RMSE increased to 0.25 and 0.29% respectively, and for TN, the R2 dropped and RMSE increased to 0.19 and 259 ppm respectively. This was primarily due to the presence of moisture in the field samples. The R2 increased to 0.62 and RMSE decreased to 0.2% for TC, and the R2 increased to 0.51 and RMSE decreased to 200 ppm for TN, when EPO was applied on both the in-situ validation and dry-ground sets. These findings highlight the importance of accounting for moisture effects in the prediction of soil properties using the robotic system and demonstrate the potential of the system in enabling soil monitoring and analysis in-situ. Advisor: Yufeng G

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed