This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the availab...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.
Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improv...
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This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines (SVM)) in classification pro...
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ISBN:
(数字)9798331515799
ISBN:
(纸本)9798331515805
This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines (SVM)) in classification problems. The analyses emphasize the impact of data preprocessing steps and hyperparameter optimization on model performance. As part of the data preprocessing, missing values were imputed, categorical data were transformed into numerical data, and normalization procedures were applied. It was observed that normalization significantly enhanced the performance of the MLP and SVM algorithms in particular. Furthermore, additional improvements in accuracy rates were achieved through hyperparameter optimization. Naive Bayes and Decision Trees were found to exhibit stable performance regardless of data scaling. This study demonstrates that proper data preprocessing and model selection can significantly enhance algorithm performance in classification problems.
The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has ...
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The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.
This study aims to develop a virtual reality (VR) application to improve the fault detection and troubleshooting processes of autonomous industrial mobile robots. The complex systems and operating environments of auto...
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ISBN:
(数字)9798331515799
ISBN:
(纸本)9798331515805
This study aims to develop a virtual reality (VR) application to improve the fault detection and troubleshooting processes of autonomous industrial mobile robots. The complex systems and operating environments of autonomous robots make fault detection challenging for users, requiring effective and user-friendly solutions to overcome these difficulties. In this context, the Robot Operating System (ROS) was utilized to detect errors in robots and record these errors in a database. Thanks to the modular structure and robust communication infrastructure offered by ROS, faults in the robots were monitored and analyzed through ROS *** study demonstrates the effective use of VR technology in the maintenance and repair processes of autonomous industrial mobile robots. By integrating ROS-based data collection and fault detection into a VR environment for user training, robot operators are enabled to troubleshoot faults more quickly and effectively. In this way, it is aimed to achieve time and cost savings in industrial processes.
This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair pr...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection.
Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. ...
Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of hislher joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.
With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework o...
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In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect ...
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Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller ...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.
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