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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

共 1217 条记录，以下是281-290 订阅

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On Rapid Parallel Tuning of Controllers of a Swarm of MAVs – Distribution Strategies of the Updated Gains

On Rapid Parallel Tuning of Controllers of a Swarm of MAVs ...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Dariusz HorIa Wojciech Giernacki Vít Krátký Petr Štibinger Tomáš Báča Martin Saska Faculty of Automation Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Poznan Poland Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Prague Czechia

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In this paper, we present a reliable, scalable, time deterministic, model-free procedure to tune swarms of Micro Aerial Vehicles (MAVs) using basic sensory data. Two approaches to taking advantage of parallel tuning are presented. First, the tuning with averaging of the results on the basis of performance indices reported from the swarm with identical gains to decrease the negative effect of the noise in the measurements. Second, the tuning with parallel testing of varying set of gains across the swarm to reduce the tuning time. the presented methods were evaluated both in simulation and real-world experiments. the achieved results show the ability of the proposed approach to improve the results of the tuning while decreasing the tuning time, ensuring at the same time a reliable tuning mechanism.

关键词： automation Distribution strategy Noise Gain measurement Robot sensing systems Data models Reliability Noise measurement Tuning Testing

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Harvesting Information Estimation Algorithm for Occluded Fruits in Autonomous Open-Field Orchard Robotic Systems

Harvesting Information Estimation Algorithm for Occluded Fru...

引用

international Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Jeongil Seo Jaehyun Kang Pyo Dongbum KwangEun Ko Human-Centric Robotics R&D Department Korea Institute of Industrial Technology Republic of Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In this study, we propose an efficient approach for recognizing harvesting information to perform grasping manipulation on fruits partially obscured by obstacles such as leaves and other fruits in an open-field orchard environment. Our method utilizes predefined 3D fruit models to more accurately reflect the true size of the fruits compared to traditional superellipsoid estimation methods. the proposed pipeline involves generating point clouds from 3D scans of real fruits, distinguishing fruit targets from obstacles using instance segmentation with the YOLOv8 model, and efficiently generating position and orientation information for grasping manipulation, even for partially occluded fruits.

关键词： Point cloud compression Instance segmentation Deep learning Solid modeling three-dimensional displays Shape Pipelines Estimation Grasping Robots

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Structural Damage Identification of Low-Carbon Energy Infrastructure Using Convolutional Neural Networks: A Case Study of Wind Turbines 28

Structural Damage Identification of Low-Carbon Energy Infras...

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28th international conference on automation and Computing, ICAC 2023

作者： Dinmohammadi, Fateme Karama, Jackson Shafiee, Mahmood School of Computing and Engineering University of West London LondonW5 5RF United Kingdom School of Energy and Sustainability Cranfield University BedfordshireMK43 0AL United Kingdom School of Mechanical Engineering Sciences University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9798350335859

the development of low-carbon energy technologies, along with the modernization of energy infrastructure, plays a vital role in the transition towards a net-zero future. Energy infrastructures are susceptible to major disruptions caused by extreme weather events, natural disasters, technical failures, and man-made accidents. the structural monitoring of energy infrastructures is therefore necessary to ensure the health and safety of systems and secure a reliable power supply. In recent years, many remote-sensing technologies such as satellite and drone imagery have been introduced to monitor large geographical areas. these technologies can provide high resolution images from energy infrastructures such as power plants, pipelines, and wind turbines. Collecting and analyzing such data can help to identify any potential damage to the infrastructure before it turns into a major incident. this paper proposes an Artificial Intelligence (AI) enabled tool that can correlate image data from various sources to identify and locate faults as well as provide some useful details about the damage such as size, shape, and orientation. Our model utilizes a ResNet50 convolutional neural network (CNN) model to classify the faults and a Region CNN model to localize the faults such that the classified faults can be singled out, labelled and given a short description to aid with the maintenance process. the model is tested on a dataset containing hundreds of images taken by a drone during an inspection of wind turbine blades. the results show that the proposed methods improve the detectability of faults, reduce failure rates, and consequentially cut down on repair expenditures. the effect of cost reduction is derived in terms of levelized cost of energy (LCOE) for six wind farms across the UK and Europe, and an average reduction of 1.2% in LCOE was achieved. © 2023 IEEE.

关键词： Neural network models

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Time-Varying Setpoint Tracking for Batch Process Control Using Reinforcement Learning

Time-Varying Setpoint Tracking for Batch Process Control Usi...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： M U Abuthahir Nabil Magbool Jan Department of Chemical Engineering Indian Institute of Technology Tirupati Andhra Pradesh India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Batch processes are indispensable for the production of low-volume and high-value products. However, control of a batch process is challenging due to the inherent nonlinearity, and time-varying characteristics. In this work, we aim to provide a comparative study of reinforcement learning approaches to perform tracking of a time-varying setpoint trajectory in a batch process. this can be accomplished by defining suitable state vectors, actions, and rewards for batch process control. Upon discretizing the state and action space, we utilize two model-free reinforcement learning methods. the efficacy of these two reinforcement learning methods is demonstrated in a non-isothermal batch reaction system. Further, we compare the performance of these reinforcement learning controllers with the nonlinear model predictive controller.

关键词： Training Monte Carlo methods Batch production systems Process control Reinforcement learning Aerospace electronics Predictive models Vectors Trajectory Robots

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Hough Transform for Detection of 3D Point Cloud Rotation

Hough Transform for Detection of 3D Point Cloud Rotation

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international conference on methods and models in automation and robotics (MMAR)

作者： Joanna Koszyk Łukasz Ambroziński Piotr Łabędź Department of Robotics and Mechatronics AGH University of Krakow Krakow Poland Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In the field of mobile robotics, point clouds are widely used as an environment representation merged into a map. However, point cloud registration can be challenging when rapid movement occurs. In indoor environments, planelike features such as walls, floors, and ceilings are dominant and can be used to aid cloud merging. these features need to be extracted from point clouds. In this paper, we propose an open-source Hough transform implementation in Python. We have adapted this method, conventionally used for detecting shapes in images, to be applied to 3D point clouds and to detect planar features. To illustrate the point-cloud Hough transform algorithm, we generated synthetic point clouds. Additionally, we collected LiDAR measurements in the corridor. Processing the experimental data proved the algorithm’s ability to handle noise and measurement uncertainties. the study included an evaluation of the Hough transform by making a comparison to the ICP method. the Hough Transform provided more accurate results of corridor width measured on a merged point cloud by 0.06 m.

关键词： Point cloud compression Performance evaluation Accuracy three-dimensional displays Simultaneous localization and mapping Shape Noise

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Servo system Cascade Linear Active Disturbance Rejection Control Based on Motor models

Servo system Cascade Linear Active Disturbance Rejection Con...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Yuang He Qijia Zheng Jialun Liu Yan Wang Qian Fan Beijing Institute of Precision Mechatronics and Controls Innovation Center for Control Actuators Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

the electromechanical servo system is usually implemented by three closed loops, with the inner loop being the current loop, the middle loop being the speed loop, and the outer loop being the position loop. the traditional system tuning method is the PI parameter trial and error method, which cannot achieve the optimal performance of the system. this article innovatively adopts the LADRC three closed-loop cascade control method. Compared with previous control methods, the control method applied in this article can ensure that the current loop, speed loop, and position loop control circuits all meet the requirements of the Bode theorem, which provides a basis for the control objectives. this article provides a detailed description of the selection methods for the control order, observer bandwidth, controller bandwidth, and loop gain of each control loop. this method was validated and ultimately achieved stable control of the electromechanical servo system.

关键词： Robust control Time-frequency analysis Service robots Bandwidth Production Observers Mathematical models Servomotors Time-domain analysis Tuning

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Enhanced PID Neural Network Control (EPIDNNC) - A Model Reference Approach

Enhanced PID Neural Network Control (EPIDNNC) - A Model Refe...

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international conference on robotics and automation Engineering (ICRAE)

作者： Dalim Wahby Guillaume Ducard Université Côte d’Azur I3S/CNRS Sophia Antipolis France

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

PID controllers are among the most popular control strategies used in industry. However, one of their main limitations is often the difficulty to tune them properly to achieve the best possible performance. therefore, adaptive PID methods have been developed, such as PID neural networks (PIDNN). the gains are often tuned by minimizing the error between the desired output and the system’s output, using a gradient descent-based algorithm. We propose an enhanced PIDNN controller (EPIDNNC) that introduces a reference model, to teach the controller a desired response. this work is successfully applied to a nonlinear system, an inverted pendulum, whose dynamics and parameters are unknown to the controller. Our EPIDNNC method is superior to the standard PIDNNC, due to 1) better performance, with respect to overshoot, steady-state error, settling time, and oscillatory behavior, and 2) a new versatile update law, allowing changes in the reference model dynamics.

关键词： Industries Adaptation models Adaptive systems Heuristic algorithms Neural networks Cost function Steady-state Nonlinear dynamical systems robotics and automation Standards

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Bayesian-Tuned Convolutional Neural Networks for Precise Bearing Fault Classification

Bayesian-Tuned Convolutional Neural Networks for Precise Bea...

引用

international Carpathian Control conference (ICCC)

作者： Pawel Knap Urszula Jachymczyk Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

this paper presents a solution for the detection of bearing outer and inner race faults using a Convolutional Neural Network (CNN) that directly analyzes raw data. Our proposed approach takes advantage of deep learning to capture intricate patterns indicative of fault conditions. To further optimize the performance of the CNN, we integrate Bayesian optimization to systematically search for the most effective network hyperpa-rameters. the study includes a comprehensive comparison with a baseline solution and an alternative CNN configuration that analyzes spectrograms. through rigorous experimentation and evaluation on benchmark datasets, we demonstrate the efficacy of the proposed Bayesian optimized CNN to achieve superior accuracy and robustness in detecting bearing faults. the results highlight the potential of integrating Bayesian optimization techniques to fine-tune CNN hyperparameters, enhancing the model's ability to generalize and adapt to diverse fault scenarios. this research contributes to the advancement of fault detection methodologies, particularly in the domain of bearing health monitoring, showcasing the effectiveness of deep learning coupled with hyperparameter optimization.

关键词： Deep learning Adaptation models Fault detection Mathematical models Bayes methods Classification algorithms Convolutional neural networks

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Adaptive Obstacle Avoidance of UAV Leader/Followers with Nonlinear Logarithm Quaternion Control

Adaptive Obstacle Avoidance of UAV Leader/Followers with Non...

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international conference on methods and models in automation and robotics (MMAR)

作者： Huda Naji Al-Sudany Béla Lantos Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

this paper addresses a significant challenge within the realm of fixed wing Unmanned Aerial Vehicles (UAVs), namely obstacle avoidance of the Leader-Follower system. the proposed approach involves formulating the Leader-Follower problem, consisting of one leader and three followers, with control strategies utilizing the logarithm exponential Quaternion method. Subsequently, the designed control law employing quaternions is applied to both the leader and the follower UAVs. Moreover, a notable contribution of this research lies in the incorporation of adaptive obstacle avoidance. the idea based on potential field as obstacle avoidance algorithm, with adaptive techniques for determining obstacle avoidance parameters. these parameters are dynamically adjusted based on the distance between the UAV and the obstacle through a non-linear adaptation function. the comprehensive implementation of these methods is rigorously evaluated by employing a predefined reference path for the leader and conducting numerous scenario-based tests. Random directions of followers were used in case of stuck in local minimum to avoid it; this was applied in case when potential filed force is less than small value. Experiments to test some scenarios of obstacle avoidance were conducted. the final results conclusively demonstrate the stability of the control law even in the presence of obstacles. the effectiveness of the adaptive obstacle avoidance approach is clearly evident from the trajectories of the UAV followers.

关键词： Adaptation models automation Quaternions Heuristic algorithms Force Autonomous aerial vehicles Stability analysis

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Design and Implementation of a QR-Enabled Autonomous Bartender Using a User-Friendly Interface

Design and Implementation of a QR-Enabled Autonomous Bartend...

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international conference on methods and models in automation and robotics (MMAR)

作者： Joseph El Chartouni Elio El Ghaoui Gaby Abou Haidar Roy Abi Zeid Daou Department of Mechatronics Engineering La Sagesse University Beyrouth Lebanon merican University of Science and Technology Faculty of Engineering Zahle Lebanon Department of Mechatronics and Biomedical Engineering La Sagesse University Beyrouth Lebanon

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

the QR-Enabled Autonomous Bartender system introduces a pioneering approach to revolutionize beverage service within the hospitality sector. the Makr Shakr is a notable example of robotic bartending systems, renowned for its sleek design and advanced automation capabilities. However, existing works in this domain are often characterized by their high cost and lack of customization options, making them inaccessible to many establishments. the objective of this paper is to present the design, construction, and implementation of a stationary robotic bartender system enabled with QR code interaction, highlighting its potential to enhance customer experience and operational efficiency in bars and restaurants. the proposed system objectives encompass the design and construction of a stationary robotic bartender, the development of a user-friendly mobile website, the implementation of a personalized ordering system, and the optimization of beverage mixing and dispensing efficiency. Utilizing chrome and wood construction, this system balances durability with cost-effectiveness. the implementation of this initiative promises to advance automation and QR technology in the food and beverage industry, offering valuable insights into robotics, automation, and web development while enhancing customer experience and operational efficiency in bars and restaurants. As for validation, the system underwent rigorous testing, with a total of 210 trials resulting in 35 trials for each drink over a period of approximately 7 hours. Impressively, the system demonstrated an overall accuracy of $96.1 \%$ with an average response time of 0.4 seconds.

关键词： automation Costs Accuracy Service robots QR codes Beverage industry Time factors

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