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Agile Actions with a Centaur-Type Humanoid: A Decoupled Approach

Agile Actions with a Centaur-Type Humanoid: A Decoupled Appr...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Polverini, Matteo Parigi Hoffman, Enrico Mingo Laurenzi, Arturo Tsagarakis, Nikos G. Ist Italiano Tecnol IIT Humanoids & Human Ctr Mechatron Res Line HHCM Genoa Italy

ISBN: (纸本)9781728190778

The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts. pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control planning of lower-body primitives, based on a template kinematic model, and online control of the upper-body motion to maintain balance. Three stabilizing strategies are presented, whose performance is compared in two types of simulated jumps, while experimental validation is performed on a half-squat jump using the CENTAURO robot.

关键词： automation Conferences Dynamics Humanoid robots Optimal control Kinematics Computer architecture

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Design And control of an Articulated Robotic Arm for Archery 5

Design And Control of an Articulated Robotic Arm for Archery

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5th ieee International symposium in Robotics and Manufacturing automation, ROMA 2022

作者： Khairudin, Ahmad Rafiq Mohd Baharuddin, Zafri Mohamed, Hassan Hafidz, Muhamad Hazwan Abdul Keith, Calvin Teh Jon Faudzi, Ahmad Athif Mohd Hamid, Wessam Universiti Tenaga Nasional College of Engineering Department of Engineering Foundation and Diploma Studies Selangor Malaysia Universiti Tenaga Nasional College of Engineering Department of Electrical and Electronics Selangor Malaysia Institute of Sustainable Energy Universiti Tenaga Nasional College of Engineering Department of Mechanical Engineering Selangor Malaysia Faculty of Engineering Universiti Teknologi Malaysia School of Electrical Engineering Johor Malaysia Kuala Lumpur Malaysia Faculty of Engineering Universiti Teknologi Malaysia School of Mechanical Engineering Johor Malaysia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

The significance of robots and artificial intelligence is gaining popularity, especially in education, industries and warehouse stores. This role can be further applied in sports and medical areas, where robots can imitate human movements and perspectives. An articulated robotic arm, motion controllers, and a vision-based targeting system are components of an archery robot's robotic system. This paper highlights the development of the targeting system by comparing the OpenCV Circle Hough Transform (CHT) algorithm with the colour and contour detection algorithm. Using these algorithms, the robot was able to find the centre of the target in a pre-determined x-y plane viewed from a webcam, draw the arrow, and finally release the arrow. This paper proposes using a Universal Robot UR5 robotic arm with a standard gripper to shoot an arrow at a target using a detection algorithm. The gripper was chosen as it can perform a thumb draw style of shooting, using a single motor to grip and release the string. This paper also provides a proof of concept algorithm that can detect a target using a webcam at 87.56% accuracy and then control a UR5 manipulator to shoot an arrow to hit the target. © 2022 ieee.

关键词： Robotic arms

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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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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

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

ISBN: (纸本)9798331508333

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. © 2025 ieee.

关键词： Model predictive control

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Machine learning and fuzzy cognitive maps in a hybrid approach toward freeway on-ramp traffic control

Machine learning and fuzzy cognitive maps in a hybrid approa...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mehran Amini Miklos F. Hatwagner Laszlo T. Koczy Department of Information Technology Szechenyi Istvan University Gyor Hungary

The infrequent emergence of traffic congestion on freeways can result in the decline of the transportation system over time. Without the implementation of appropriate countermeasures, congestion can escalate, leading to unfavorable impacts on other aspects of the traffic network. As a result, there is a greater need for reliable and optimal traffic control. The goal of this research is to manage the number of vehicles entering the main freeway from the ramp merging area, in order to balance the demand and capacity to satisfy the maximum utilization of the freeway capacity. Despite extensive research into different ramp metering techniques, this study aims to utilize the fuzzy cognitive map as a macroscopic traffic flow model in conjunction with the Q-learning algorithm. This combination prevents freeway congestion and maintains optimal performance by keeping freeway density below a key threshold. The inherent uncertainty of traffic conditions is addressed through the application of reinforcement learning, which is constructed on the principles of the Markov decision process. This approach represents an exploration-exploitation trade-off, as implemented through the Q-learning algorithm. The proposed technique was evaluated for its efficacy in the regulation of freeway ramp metering in both controlled and uncontrolled simulations. The findings demonstrate a significant improvement in the control of the mainstream traffic flow.

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The automatic control and monitoring system for the production management process of large-scale breeding farm

The automatic control and monitoring system for the producti...

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2021 International symposium on Artificial intelligence and its Application on Media, ISAIAM 2021

作者： Wang, Tenghui Zheng, Xiaohai Wang, Qilong Wang, Xujie Xijing University School of Science Shaanxi Xi'an China Xijing University Shaanxi Province Controlled Neutron Source Engineering Research Center School of Science Shaanxi Xi'an China

ISBN: (纸本)9781665432603

China is a large nation of breeding industry and has a big demand for poultry products. In terms of the level of automation and management of poultry farms, there is a certain gap compared with the developed countries. In this paper, based on the researches on application of automation equipment and Internet technologies, we design a large-scale breeding farm automation control monitoring system by using the upper computer software and the lower computer PLC, which can applied to the whole production management process. Through this system, the farm can greatly reduce the labor cost and improve the automation level of production management process. © 2021 ieee.

关键词： automation

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Investigation of reward functions for controlling blood glucose level using reinforcement learning

Investigation of reward functions for controlling blood gluc...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Dénes-Fazakas Lehel Máté Siket László Szilágyi Gyorgy Eigner Levente Kovács Physiological Controls Research Center Óbuda University Applied Informatics and Applied Mathematics Doctoral School Óbuda University Budapest Bécsi street 96/B Computational Intelligence Research Group Sapientia University Tg. Mureş Romania Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Óbuda University

In the present study, we investigated the effect of different reward functions in insulin regulation using reinforcement learning. An artificial pancreas system is able to deliver insulin into the body in an automated way. The control algorithm of an automated insulin delivery system is a key player in achieving personalized therapy. Neural networks provide an approach to customize insulin administration by learning the patient’s habits and administering insulin accordingly. Therefore, we conducted experiments with neural networks based on reinforcement learning. Our goal was to find a neural network-based model and reward function that could learn the patient’s behavior and administers insulin with the best time in ranges. We evaluated the method on simulated virtual patients when sensor noise occurs. The results show that the bump functions were the most efficient in providing acceptable time in ranges.

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EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization

EffLoc: Lightweight Vision Transformer for Efficient 6-DOF C...

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ieee International Conference on Robotics and automation (ICRA)

作者： Zhendong Xiao Changhao Chen Shan Yang Wu Wei Control Science and Engineering School of Automation Science and Engineering South China University of Technology Guangzhou China Colloge of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving. It estimates 3D camera position and orientation (6-DoF) from images. Unlike traditional methods like SLAM, recent strides use deep learning for direct end-to-end pose estimation. We propose EffLoc, a novel efficient Vision Transformer for single-image camera relocalization. EffLoc’s hierarchical layout, memory-bound self-attention, and feed-forward layers boost memory efficiency and inter-channel communication. Our introduced sequential group attention (SGA) module enhances computational efficiency by diversifying input features, reducing redundancy, and expanding model capacity. EffLoc excels in efficiency and accuracy, outperforming prior methods, such as AtLoc and MapNet. It thrives on large-scale outdoor car-driving scenario, ensuring simplicity, end-to-end trainability, and eliminating handcrafted loss functions.

关键词： Computer vision Accuracy Three-dimensional displays Simultaneous localization and mapping computational modeling Robot vision systems Cameras

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Analysis of social responsibility and consumer activity through primary research

Analysis of social responsibility and consumer activity thro...

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International symposium on computational intelligence and Informatics

作者： Anett Popovics Valéria Szekeres Keleti Károly Faculty of Business and Management Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

Today, successful companies are all placing a strong emphasis on corporate social responsibility: they are involved in activities and support causes that serve the interests of a community. The emergence of a group of responsible consumers’ is not new, who place great emphasis on buying environmentally responsible and ethical products and who also value transparent communication from companies. In our quantitative methodological research, we investigated whether there is a correlation between social activism and the purchase of ethical products, and which consumer groups are active buyers of ethical products. Our results confirmed both our hypotheses: we have shown that this attitude is reflected in consumer behaviour and could contribute to increasing social responsibility activity in the future.

关键词： Computer science Ethics Consumer behavior Mechatronics Correlation automation Companies

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Intergenerational cooperation and generational differences at work

Intergenerational cooperation and generational differences a...

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International symposium on computational intelligence and Informatics

作者： Katalin Jäckel Monika Garai-Fodor Faculty of International Management and Business Budapest Business School Budapest Hungary Keleti Karoly Faculty of Business and Management Obuda University Budapest Hungary

ISBN: (纸本)9798350398830

A significant challenge for human resources professionals is that there are currently four different generations working in the labour market. The youngest one, Generation Z, is the most different from the previous generations and therefore, managing them requires a different approach. It is crucial for employers to be aware of the mentality, values and expectations of Generation Z in order to create a generation-specific employer branding strategy. The objective has three components. To attract and retain the most valuable employees for the corporate culture in the long term, and to achieve the most effective cooperation between the 4 generations, taking into account both rational and emotional aspects. In our research we interviewed 540 people. The sample is not representative, but it allows us to formulate causal *** believe that the results of our research could help employers and managers to pay attention to the differences, values, benefits and sometimes conflicts between the generations and to deal with them in a more differentiated approach.

关键词： Computer science Mechatronics automation Brand management Informatics Robots computational intelligence

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Resource Constrained, Fast Convergence Training for Violence Detection in Video Streams

Resource Constrained, Fast Convergence Training for Violence...

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International symposium on computational intelligence and Informatics

作者： Catalin Vladu Lucian Prodan Alexandru Iovanovici Department of Computer and Information Technology Politehnica University Timisoara Romania

ISBN: (纸本)9798350398830

This paper addresses the automated identification of violent acts from CCTV video streams using a Deep Learning model under constrained resources. While this process typically involves a powerful setup, it is useful to accelerate the training and get accurate results using more modest computational resources that would bring automatic recognition of violent acts closer to common surveillance resources. Our results provide 94.98% accuracy, on par with the state-of-the-art, but at a fraction of the training time. This translates into lower energy requirements and allows a broader deployment on large scale (urban) autonomous surveillance networks while providing an increased privacy towards citizens and lower chances of abuse from authorities.

关键词： Training Adaptation models Privacy Surveillance computational modeling Neural networks Streaming media

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