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ieee International symposium on computational intelligence in Robotics and automation, CIRA 2001

ISBN: (纸本)0780372034

The proceedings contain 96 papers. The topics discussed include: design of a novel compact dexterous hand for teleoperation;design of energy efficient joint profiles for a planar five-link biped robot;vibration control of flexible-link manipulators using generalized output redefinition;humanoid walk control with feedforward dynamic pattern and feedback sensory reflection;computation of grasp internal forces for stably grasping multiple objects;on computing immobilizing grasps of 3-D curved objects;a new method for grasp stability evaluation;development of a creeping snake-robot;are parallel manipulators more energy efficient?;a study of the indefinite shaped mobile mechanism composed of multi-unit;multiple sensor data fusion in robotic prosthetic eye system;improved path following of USU ODIS by learning feedforward controller using dilated B-spline network;torque control of sinusoidal PMAC motors for direct-drive robots;and dynamic control of curve-constrained hyper-redundant manipulators.

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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ieee Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sarathkumar D Anil. A. R J Sivadasan T. Jayakumar Murugesan Manivel Anandhakumar C Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Computer Science &Engineering Sree Buddha College of Engineering Pattoor Alappuzha Kerala India Department of Electronics and Communication Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronic Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem.

关键词： Fuzzy logic automation Machine learning algorithms Neural networks Prediction algorithms Real-time systems Optimization Intelligent control Industrial Internet of Things Predictive control

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FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfaces for Robotic Exploration

FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfac...

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computational intelligence on Engineering/Cyber Physical Systems Companion (CIES Companion), ieee symposium on

作者： Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (Air Lab) Aarhus University Aarhus C Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798331508494

ISBN: (纸本)9798331508500

Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for reconstructing icy surfaces using various sensors. A preliminary study based on this dataset demonstrates the feasibility of combining the geometric surface obtained from consecutive LiDAR scans with reconstructed meshes from visual cameras. The integration of distinct data sources has the potential to improve the robustness of reconstruction algorithms in diverse scenarios. The dataset can be downloaded at https://***/records/13862009.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Navigation Ice Robustness Laser beams Image reconstruction Surface treatment

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Adaptive Bayesian Optimization for High-Precision Motion Systems

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ieee Transactions on automation Science and Engineering 2025年 22卷 15627-15637页

作者： Konig, Christopher Krishnadas, Raamadaas Balta, Efe C. Rupenyan, Alisa Control and Automation Group Inspire AG Zürich Switzerland Zürich University for Applied Sciences ZHAW Centre for Artificial Intelligence Zürich Switzerland

controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free controller tuning and adaptation method. However, Bayesian optimization methods are computationally expensive and therefore difficult to use in real-time critical scenarios. In this work, we propose a real-time purely data-driven, model-free approach for adaptive control, by online tuning low-level controller parameters. We base our algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization, for handling performance and stability criteria. We introduce multiple computational and algorithmic modifications for computational efficiency and parallelization of optimization steps. We further evaluate the algorithm’s performance on a real precision-motion system utilized in the semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach. © 2004-2012 ieee.

关键词： Stability criteria

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Proceedings of ieee International symposium on computational intelligence in Robotics and automation, CIRA

Proceedings of IEEE International Symposium on Computational...

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2003 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2003

ISBN: (纸本)0780378660

The proceedings contain 266 papers. The topics discussed include: on using discretized cohen-Grossberg node dynamics for model-free actor-critic neural learning in non-Markovian domains;a parameter control method inspired from neuromodulators in reinforcement learning;manipulation of hidden units activities for fault tolerant multi-layer neural networks;state estimation for flexible joint manipulators using stable neural networks;a text processing kohonen neural network;software tool development for marker making operations in textile industry;genetic design of decentralized controllers for 5dof robotic manipulator;design of sliding mode power system stabilizer via genetic algorithm;an operation planning method for a demand-bus system based on local search of autonomous agents;and an analysis of U-mart experiments by machine and human agents.

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Proceedings of ieee International symposium on computational intelligence in Robotics and automation, CIRA

Proceedings of IEEE International Symposium on Computational...

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2003 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2003

ISBN: (纸本)0780378660

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Proceedings of ieee International symposium on computational intelligence in Robotics and automation, CIRA

Proceedings of IEEE International Symposium on Computational...

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2003 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2003

ISBN: (纸本)0780378660

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

Lyapunov-Inspired Deep Reinforcement Learning for Robot Navi...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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Non-Linear Friction Force Estimation for Ball and Beam Mechanism Using R-PINN

Non-Linear Friction Force Estimation for Ball and Beam Mecha...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Ozan Kaya Seniz Ertugrul Masoud Abedinifar Olav Egeland Dept. of Mechanical and Industrial Eng. Norwegian University of Science and Technology (NTNU) Trondheim Norway Dept. of Mechatronics Engineering Izmir University of Economics (IUE) Izmir Turkey Dept. of Neurology University Hospital Schleswig-Holstein (USKH) Kiel Germany Dept of Mechanical and Industrial Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Different friction forces or torques are affecting the system's performance and control. Friction forces occur due to bearings, gearboxes, or any other contacts in the system. Researchers have reported different forms of friction, such as stiction, viscous and Stribeck effects, pre-sliding displacement, stick-slip effects, hysteresis (or frictional lag), etc. Developing a mathematical model to describe the underlying dynamics of a complex system may become necessary to design either a modelbased controller or at least compensate for the non-linear effects of friction forces. For this reason, either test set-ups or datadriven techniques might be used. In this study, the RecurrentPhysics Informed Neural Network is studied to determine the friction forces and model the Ball and beam system. While PINN provides faster results to model non-linear systems with noisy and small data sizes, Recurrent Neural Network architecture is fruitful for modeling time-dependent systems. Thus, R-PINN is trained with noisy signals for system response and friction model of the ball and beam system. Despite noisy signals and nonlinearity in the system, R-PINN is promising in modeling the system response and estimating the friction model.

关键词： Recurrent neural networks Friction computational modeling System performance Dynamics Training data Predictive models Data models Mathematical models Noise measurement

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Application of Fuzzy-Based Uncertainty in Cardiac MRI Segmentation

Application of Fuzzy-Based Uncertainty in Cardiac MRI Segmen...

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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Qiao Lin Xin Chen Chao Chen Nikesh Jathanna Peter P Swoboda Shahnaz Jamil-Copley Jonathan M. Garibaldi School of Computer Science University of Nottingham Ningbo China School of Computer Science University of Nottingham UK Nottingham University Hospitals NHS Trust UK Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds UK

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Cardiac magnetic resonance imaging (MRI) is pivotal in diagnosing cardiac-related diseases. Tissue segmentation from cardiac MRI images is the initial and most crucial step in downstream analyses. However, most current research focuses on designing segmentation models on cardiac MRI to produce segmentation results, without considering the reliability of these outputs. This paper novelty applied fuzzy-based uncertainty to assess the reliability of cardiac MRI segmentation results without access to ground truth images. Experimental results show that class-level uncertainty has a strong linear negative relationship (PE=-0.92) with the true segmentation quality (measured by Dice). This suggests that when ground truth images are unavailable, uncertainty can effectively estimate the cardiac MRI segmentation quality. Furthermore, qualitative analysis with clinicians is conducted to explore the clinical application of fuzzy-based uncertainty. Based on their feedback and comments, class-level uncertainty provides more detailed information and is more suitable to infer the segmentation quality in comparison to other uncertainties. As for the representation of uncertainty, the predicted Dice is the best option compared to other representation methods and is straightforward for clinicians to understand.

关键词： Image segmentation Visualization Uncertainty Hospitals Magnetic resonance imaging Measurement uncertainty Quality control Prediction algorithms Reliability engineering Diseases

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