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

共 24667 条记录，以下是181-190 订阅

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SPI2I: Structure-Preserved Image-to-Image Translation with Diffusion models 27th

SPI2I: Structure-Preserved Image-to-Image Translation with D...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Dong, Beibei Peng, Bo Dong, Jing Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031783043

Large-scale text-to-image generative models are already proficient at producing high-quality results that closely match the intended prompts. Nevertheless, the pivotal challenge in image editing tasks lies in the difficulty of confining alterations within the editing region while preserving the structure and details of the source image. In this paper, we propose a zero-shot structure-preserved image-to-image translation approach based on diffusion models. We combine the optimization of the latent code and the injection of the U-Net features to strengthen the structural preservation effect by alleviating the inconsistency between the information contained in the latent code and the injected features. Our method effectively preserves the structural and detailed information of the source image while enhancing the quality of the generated results. We exhibit comprehensive and high-quality experimental results showcasing that our approach surpasses state-of-the-art methods across various image-to-image translation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image editing Image-to-image translation Text-to-image generation

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Integrating Trajectory Optimization in Quality-Diversity for Kinodynamic Motion Planning

Integrating Trajectory Optimization in Quality-Diversity for...

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international conference on Autonomous Robots and Agents, ICARA

作者： Konstantinos A. Asimakopoulos Konstantinos I. Chatzilygeroudis Department of Electrical & Computer Engineering Laboratory of Automation & Robotics (LAR) Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Kinodynamic planning is a critical component of robotics, enabling robots to generate dynamically feasible trajectories while adhering to geometric constraints. Existing methods for kinodynamic planning primarily fall into two categories: (a) stochastic planners, such as Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), which excel in state-space exploration due to their directed randomness, and (b) optimization-based approaches, which are well-suited for structured environments, producing smooth and optimal solutions. In this work, we present Hybrid Trajectory Exploration for Kinodynamic Planning (HyTraX), a hybrid framework that integrates the strengths of these two paradigms. Specifically, HyTraX enhances the effectiveness of the Go-Explore algorithm by incorporating trajectory optimization with random target selection as its core exploration strategy. We evaluate HyTraX on two challenging kinodynamic motion planning tasks: a car-like agent and a planar quadrotor navigating through maze environments. Our results demonstrate significant performance improvements with minimal task-specific tuning, highlighting the robustness and versatility of the proposed approach.

关键词： Legged locomotion Scalability Performance gain Probabilistic logic Robustness Planning Trajectory optimization Robots Tuning Quadrotors

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Human-Robot Interaction in Extreme and Challenging Environments 20

Human-Robot Interaction in Extreme and Challenging Environme...

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20th Annual ACM/IEEE international conference on Human-Robot Interaction, HRI 2025

作者： Tian, Leimin Carreno-Medrano, Pamela Giuliani, Manuel Hawes, Nick Bhattacharyya, Raunak Kulić, Dana Csiro Robotics Clayton Australia Monash University Clayton Australia Kempten University of Applied Sciences Kempten Germany University of Oxford Oxford United Kingdom Indian Institute of Technology Delhi Delhi India

ISBN: (纸本)9798350378931

The first workshop on human-robot interaction in extreme and challenging environments (exactingHRI: https://***/***/exactinghril) focuses on the forefront of HRI research in applications where robots are working with diverse users in uncertain, unknown, or risky environments to deliver reliable outcomes in repeated sessions. In these scenarios, a robot's autonomous and interactive functions are put to the test, with errors likely to arise. Such HRI systems require design and evaluation in-situ with target users, i.e., 'exacting' HRI. The exactingHRI 2025 workshop aims to bring together researchers that investigate the diverse human, robot, task, environment, and interaction factors that are challenging for state-of-the-art HRI systems, as well as innovative designs, theories, models, and methods that equip people and robots with the ability to address these challenges. Workshop presenters will share lessons they have learned from successful or failed attempts in testing their work in such difficult settings, in a bid to encourage and guide the necessary efforts that progress our field to solve real-world problems. © 2025 IEEE.

关键词： Social robots

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Left/Right Brain, Human Motor Control and the Implications for robotics 10th

Left/Right Brain, Human Motor Control and the Implications...

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10th international conference on Machine Learning, Optimization, and Data Science, LOD 2024

作者： Rinaldo, Jarrad Friedman, Jason Kuhlmann, Levin Kowadlo, Gideon Department of Data Science and AI Monash University Melbourne Australia Department of Physical Therapy Tel Aviv University Tel Aviv Israel Cerenaut Melbourne Australia

ISBN: (纸本)9783031824869

Neural Network movement controllers promise a variety of advantages over conventional control methods, however, they are not widely adopted due to their inability to produce reliably precise movements. This research explores a bilateral neural network architecture as a control system for motor tasks. We aimed to achieve hemispheric specialisation similar to what is observed in humans across different tasks;the dominant system (usually the right hand, left hemisphere) excels at tasks involving coordination and efficiency of movement, and the non-dominant system performs better at tasks requiring positional stability. Specialisation was achieved by training the hemispheres with different loss functions tailored to the expected behaviour of the respective hemispheres. We compared bilateral models with and without specialised hemispheres, with and without inter-hemispheric connectivity (representing the biological Corpus Callosum), and unilateral models with and without specialisation. The models were trained and tested on two tasks common in the human motor control literature: the random reach task, suited to the dominant system, a model with better coordination, and the hold position task, suited to the non-dominant system, a model with more stable movement. Each system outperformed the non-preferred system in its preferred task. For both tasks, a bilateral model outperformed the non-preferred hand and was as good or better than the preferred hand. The results suggest that the hemispheres could collaborate on tasks or work independently to their strengths. This study provides ideas for how a biologically inspired bilateral architecture could be exploited for industrial motor control. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Spatial Alignment Method for Medical Ear Canal Based on Simplified Transformer Module and MultiScale Iterative Structure

A Spatial Alignment Method for Medical Ear Canal Based on Si...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Chaokun Luan Qinglei Zhang Ying Zhou Weiping Liu Biao Shu Zhijian Li Xiaoyao Liu Haopeng Yang Logistics Engineering College Shanghai Maritime University Shanghai China Shanghai Smart Surgery Center Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China Key Accounts Department Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Alignment of deformed medical images is a challenging task in which the images are subject to large deformations due to some constraints in the image acquisition process. In the field of deformation image alignment, convolutional neural network-based methods have a local acceptance domain and cannot handle deformed images. In this regard, we propose a new multi-scale parallel architecture model of Transformer-CNN, which takes advantage of Transformer's goodness at modeling remote dependencies, while simplifying the module and reducing the number of model parameters. The multiscale iterative structure of CNN can acquire local information and connect feature information at different scales. Experimental results on the medical ear canal dataset show that the method has excellent performance in terms of alignment accuracy, outperforming existing deep learning and some traditional alignment methods, while reducing the number of model parameters to some extent, making it lightweight.

关键词： Irrigation Accuracy Deformation Ear Parallel processing Transformers Iterative methods Convolutional neural networks Robots Biomedical imaging

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Anomaly Detection for Escalators Based on Feature Optimization and Improved GMM Algorithm

Anomaly Detection for Escalators Based on Feature Optimizati...

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robotics, Control and automation (ICRCA), international conference on

作者： Long Qiang Ningyun Lu Jianfei Chen Jianhua Lyu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing Kangni Mechanical & Electrical Co. Ltd. Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Escalators, as vital components of modern urban transportation and commercial buildings, necessitate stable operation. However, due to prolonged usage and environmental influences, key components of escalators are prone to failures, leading to operational anomalies. Traditional periodic inspections and manual maintenance methods suffer from low efficiency, high cost, and poor real-time performance. To address these issues, this paper proposes an unsupervised escalator anomaly detection method based on feature optimization and an improved Gaussian Mixture Model (GMM) *** method initially employs the Pareto principle for feature selection, enhancing computational efficiency and generalization capabilities. Subsequently, to overcome the limitations of traditional GMMs in determining the number of clusters and their application to anomaly detection, a bidirectional Fuzzy C-Means-Expectation Maximization (FCM-EM) algorithm is proposed. This algorithm determines the optimal number of clusters and optimizes the model’s fit to the data, thereby enabling accurate identification of anomalous states in *** results demonstrate that the proposed method achieves high diagnostic accuracy across four escalator fault modes. Furthermore, comparative experiments show that the proposed method outperforms the comparison methods, providing reliable technical support for intelligent maintenance and safe operation of escalators.

关键词： Accuracy Heuristic algorithms Clustering algorithms Manuals Feature extraction Maintenance Anomaly detection Optimization Gaussian mixture model Escalators

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A Kinematic Simulation Strategy of a Compliant 4-Bar Linkage with Flexure Hinge Joint

A Kinematic Simulation Strategy of a Compliant 4-Bar Linkage...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Pengfei Wang Lailu Li Rui Wang Xin Guo Dongyu Li Shanghai Radio Equipment Research Institute Shanghai China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Compliant mechanisms have drawn a lot of attention in recent years. Discrete elastic rod theory has paved the way in designing and analyzing linkage mechanisms with flexure hinges (compliant linkage mechanism). In order to achieve a same kinematic as the rigid linkage mechanism, this paper gives a general model for the kinematic of a compliant linkage mechanism: crank and rocker mechanism: Firstly, modeling of a compliant linkage mechanism based on discrete elastic rod theory, taking a typical example of a cranker and rod mechanism; Secondly, kinematic modeling of the rigid cranker and rod mechansim; Thirdly, optimization flow is introduced, including BFGS optimization method, cubic hermix spline method and other relevant settings; Lastly, the simulation results demonstrate that this model could be used in the analysis and design of the compliant linkage mechanism.

关键词： Couplings Analytical models Interpolation Manufacturing processes Simulation Optimization methods Kinematics Fasteners Splines (mathematics) Robots

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LSTM-CNN Ensemble methods for Predicting Malignant Brain Tumors 3

LSTM-CNN Ensemble Methods for Predicting Malignant Brain Tum...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Faiz, Ayaan Gopalakrishna, K. Agarwal, Ankita Suchithra, R. Saini, Pavas Ayus, Ishan Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

The LSTM-CNN ensemble method performs well in predicting malignant brain tumors. The model above combines the best of LSTM and CNN to enhance accuracy and consistency in forecasts. An LSTM model is an RNN architecture that allows the collecting and processing of repeatable data, e.g., time series or language text. It is one of the best tools for medical data analysis, such as brain tumor image scans. It will allow the LSTM model to learn patterns and correlations in your data regarding precision when predicting future observations. Meanwhile, the CNN model is best for interpreting high-dimensional data and then amalgamating them to make predictions. It renders it a beneficial tool for analyzing brain tumor imaging scans and predicting features indicative of a malignant nature. It unites the best of abilities in both models and helps to make up for their shortcomings, respectively. It leads to a more reliable and precise prognosis of brain cancers, both for early detection as well as treatment planning. Additionally, employing deep learning methods such as LSTM-CNN ensemble may greatly help enhance brain tumor diagnosis and therapy efficiency. © 2025 IEEE.

关键词： Data reduction

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Research on Gas Turbine Speed Control Based on PSO-MPC Rolling Optimization of PI Parameters

Research on Gas Turbine Speed Control Based on PSO-MPC Rolli...

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robotics, Control and automation (ICRCA), international conference on

作者： Zeng Boyang Wang Zinan Zhang Yuhao Tian Zhen Advanced Gas Turbine Laboratory Institute of Engineering Thermophysics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

To address the control challenges arising from the highly nonlinear characteristics of gas turbines and overcome issues related to tuning PI controller parameters, this study presents an innovative speed control methodology specifically designed for gas turbines. By integrating Particle Swarm Optimization with Model Predictive Control in real-time, our proposed approach effectively optimizes PI controller parameters. To serve as a predictive model, we employ a state-space model obtained through system identification techniques. Through rolling optimization using the PSO algorithm, dynamic adjustments can be made to fine-tune the PI controller parameters accordingly. Our digital simulations demonstrate that this approach yields faster response times compared to conventional PI controllers while significantly enhancing system adaptability. Consequently, our research provides valuable insights into optimizing control strategies for nonlinear systems.

关键词： PI control Heuristic algorithms Velocity control Predictive models Prediction algorithms Real-time systems Particle swarm optimization Turbines Optimization Predictive control

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Partial Multi-Label Learning via Dual Subspace Collaboration 16

Partial Multi-Label Learning via Dual Subspace Collaboration

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Hu, Xi Chen, Zhengtao Chen, Yu School of Computer Guangdong University of Technology Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

In image classification, an image often has multiple labels, but it is expensive to obtain accurate label information. The main task of partial multi-label learning (PML) is to learn in weakly supervised conditions where only a subset of the provided labels is correct and make correct predictions. Different from traditional multi-label learning, PML needs to train multi-label classification models in an imperfect environment, which belongs to weakly supervised learning. In order to reduce the false guidance of noise labels to the classifier and obtain correct prediction results, this paper proposes a novel PML method via dual subspace collaboration (PML-DSC). Specifically, it first use samples and labels to learn two common sub-spaces, then use fuzzy clustering to learn label class centers and calculate the confidence that the sample belongs to each label class center so that the first subspace can approximate the true label space to reduce the influence of noisy labels. The second subspace is then used to learn the potential semantic information, and the second subspace splits the classifier into two parts to improve the generalization ability of the model. Finally, the first subspace is used to guide the classifier learning, and the two sub-spaces work together to fully exploit the hidden information and improve the prediction accuracy of the model. Extensive experiments and analyses on both real-world and synthetic datasets demonstrate that the proposed PML-DSC is superior to the state-of-the-art methods. © 2025 SPIE.

关键词： Self-supervised learning

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