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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171-180 订阅

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Attitude Balance Control of a Wheeled Quadruped Robot Utilizing an Inertial Measurement Unit

Attitude Balance Control of a Wheeled Quadruped Robot Utiliz...

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

作者： Xiyuan Li Yunsong Li Jihong Zhang Changshuai Sun School of Electromechanical and Automotive Engineering Yantai University Yantai China School of Information Science and Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

The Wheeled Quadruped Robot (WQR), a novel hybrid mobile platform that combines the advantages of wheeled and legged locomotion, has attracted significant research interest due to its enhanced mobility and stability in complex terrains. However, achieving efficient attitude stabilization during dynamic operations remains a critical technical challenge. This study initiates with a thorough kinematic modeling of the WQR mechanism. Next, we present a novel attitude stabilization methodology employing closed-loop control architecture driven by real-time Inertial Measurement Unit (IMU) data streams. Our approach integrates kinematic constraints and real-time IMU data with Proportional-Integral (PI) regulators to establish an inverse kinematics-guided adaptive adjustment mechanism. This mechanism dynamically adjusts leg movements in response to deviations in attitude. To assess the effectiveness of our control strategy under diverse terrain conditions and motion patterns, extensive simulations were conducted, comparing it with fixed joint angle control and traditional inverse kinematics-based control methods. The simulation results indicate that the WQR's attitude stability is markedly improved across various terrains and motion states after implementing our proposed control strategy. This research contributes to robotic control theory by innovatively integrating sensor fusion and adaptive kinematics, thereby significantly impacting the development of robust mobile platforms for unstructured environments.

关键词： Legged locomotion Adaptation models Measurement units Attitude control Kinematics Inertial navigation Robot sensing systems Stability analysis Real-time systems Quadrupedal robots

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Turbine Engine Health Assessment Based on Graph Neural Network

Turbine Engine Health Assessment Based on Graph Neural Netwo...

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

作者： Yuemei Xu Jin Chen Yu Peng Qiwen Hao Xingling Shao Ruixiang Deng Qiang Zhang School of Electrical and Control Engineering North University of China China Beijing Institute of Aerospace Control Beijing China Beijing SpaceNovo Technologies Company Limited Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Aiming at the research field of aero-engine health state assessment, for the problem of missing or unobtainable part of the monitoring data which leads to a big challenge in health assessment, this paper proposes a health state assessment method for complex systems based on Graph Neural Network(GNN), aiming to realize the health assessment of the system through partial inspection data. Firstly, the GNN node graph is drawn based on the structural mechanism among the components in the complex system, secondly, the health state of some components is obtained based on the monitoring data, and the node attributes and edge attributes are set to establish the Graph Convolution Network(GCN) health assessment model; finally, the effectiveness of the proposed method in the complex system is verified by the performance simulation data of an aviation turbofan engine.

关键词： Training Accuracy Inspection Graph neural networks Data models Complex systems Aircraft propulsion Monitoring Engines Turbines

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3rd international conference on Intelligent Systems and Applications, ICISA 2024

3rd International Conference on Intelligent Systems and Appl...

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3rd international conference on Intelligent Systems and Applications, ICISA 2024

ISBN: (纸本)9789819798384

The proceedings contain 24 papers. The special focus in this conference is on Intelligent Systems and Applications. The topics include: Analyzing Various CNN Architectures for Detection of Early Blight and Late Blight in Infected Potato Plants;GrainLog: An IoT-ML Based Food Grain Monitoring and Shelf-Life Prediction System for the Farmer;intelligent Systems for Digitalization and automation of Agriculture Equipment;machine Learning methods for Crop Yield Prediction Across Diverse Agricultural Environments;soil Health Analysis and Automatic Prediction of Macro and Micronutrient Using Machine Learning Algorithms;A Deep CNN Model for Analytical Prediction of Tomato Diseases Using Multiple Disease Dataset;synergistic Solutions: Imagen-Based Dataset Augmentation and Yolo-V8-Driven Disease Detection for Enhanced Cherry Tree Health;advanced Detection of Diseases in Tomato Plant Leaf Using Deep Learning;Application of CNN for Separation Between No Rain and Rain from Remote Sensing Data;Comparative Analysis of Ensemble-Based Nowcasting models for Precipitation Prediction in the UAE;AI-Driven Approach for Optimal Soil-Based Crop Recommendations;Employing Machine Learning methods and RFM Model for Customer Clustering: Case Study of an Agricultural Retailer;Enhanced Vegetation Cover Assessment Using Sentinel-2A: A Unified Perspective on NDVI, SAVI, and GNDVI;agroGenie: A Smart Approach to Agriculture Using Machine Learning;Implementation of DCNN Framework—Auto Identification and Categorization of Various Stress in Paddy Crop and Resource Management System;Optimized CNN Model to Develop a Decision-Making System for Spraying on Cotton Crop;a Robust Optimization Approach for Agricultural Waste Supply Chain Network Design: A Real Case Study;finding the Non-destructive Monitoring System of Papaya by Machine Learning Algorithm.

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Brick by Brick - Findings of an of-the-Shelf automation System Application for Building Block Component Group Disassembly

Brick by Brick - Findings of an of-the-Shelf Automation Syst...

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Mechanical Engineering and robotics Research (ICMERR), international conference on

作者： Dominique Briechle Tobias Geger Lena Schartow Andreas Rausch Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

automation is one of the key drivers in today's global economy. It ensures the conduction of a manifold of standardized processes which helps tackle the decreasing amount of skilled workers in certain areas as well as reducing cost. Although automation is broadly used nowadays, the overall applicability of automated processes is often limited to use cases with predefined process steps of minor flexibility. However, new and upcoming sustainable industries that are focusing on product life-cycle prolonging (e.g. repair, refurbish, and remanufacture (3Rs)) options to enable Circular Economy-friendly treatment, require a high amount of adaptability in automated systems to tackle the high variability of product compositions in disassembly scenarios. The goal of the conducted case study is therefore to investigate the possibilities of a nowadays available of-the-shelf automation system regarding its functions and mechanisms towards enabling automated adaptability by using disassembly scenarios of component groups consisting of different building blocks as an example scenario and the concluding future research areas. The paper, therefore, investigates the different methods offered by the default system and applies them to various levels of disassembly scenarios. The conducted study helps to clarify the capabilities of the system's functionalities in the overall goal to enhance adaptability in disassembly processes.

关键词： Manifolds Industries Computer vision automation Costs Focusing Collaborative robots Maintenance engineering Mechanical engineering

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Formalizing Swarm Intelligence: Event-B Verification in robotics 15th

Formalizing Swarm Intelligence: Event-B Verification in Robo...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： El Mimouni, Sanae Bakhbakh, Adil Faculty of Sciences Mohammed V University Rabat Morocco National School of Applied Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031810855

Swarm robotics, comprising decentralized collectives of robots with emergent behaviors, holds promise for tasks like exploration and construction. However, verifying safety and liveness properties in these systems proves challenging due to their nature. This paper proposes an approach using the Event-B formal method for modeling and verifying swarm robotics systems. Event-B offers a framework to specify models and prove properties mathematically. Demonstrating the method’s efficacy, we model swarm behaviors and environments using Event-B, formulate key safety and liveness properties, and discharge proof obligations for verification. Applying this approach to case studies involving swarm construction and navigation, we successfully verify critical properties, including collision avoidance, localization, and goal reaching. This rigorous verification allows for the validation of swarm system correctness before deployment, offering certified guarantees that enhance trust and facilitate real-world deployment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Formal methods Liveness properties Safety properties Swarm robotics Theorem proving

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Development of a Soft Gripper Using Kirigami-Inspired Techniques

Development of a Soft Gripper Using Kirigami-Inspired Techni...

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Mechatronics, Control and robotics (ICMCR), international conference on

作者： Efrem Olivio Gomes Shyang-Jye Chang Ilham Saputra Hsu-Feng Chang Department of Mechanical Engineering National Yunlin University of Science and Technology Douliu Taiwan Department of Mechanical Engineering Vocational School Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This study introduces a soft robotic gripper inspired by Kirigami, designed to improve flexibility and adaptability for handling delicate or irregular objects. The gripper, fabricated using silicone elastomers with strategic Kirigami cuts, allows significant deformation while maintaining structural integrity. Two models were tested on various objects. Model 1 showed superior performance, while Model 2, designed for larger objects, struggled with smaller and heavier items. The gripper demonstrated excellent adaptability, securely gripping diverse objects by conforming to complex geometries. Its potential for gentle handling makes it suitable for biomedical and agricultural applications. Durability tests revealed that the 1.5 mm thickness gripper maintained performance after over 100 cycles, outperforming the 1.0 mm version, which exhibited loosening after fewer uses. Compared to conventional grippers, the Kirigamiinspired design showed enhanced flexibility, precision, and durability, making it ideal for automation tasks. Future refinements in materials and design will further improve its performance.

关键词： Fabrication Adaptation models Shape Biological system modeling Elastomers Soft robotics Object recognition Grippers Optimization Testing

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Towards Formal Specification of Agent-based Transport models 16

Towards Formal Specification of Agent-based Transport Models

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kübler, Jelle Vortisch, Peter Institute for Transport Studies Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 Karlsruhe 76131 Germany

Agent-Based Transport models (ABTMs) are powerful tools for simulating complex transport systems, yet they often lack formal specifications, limiting their transparency, reproducibility, and automation. While behavioral (sub-) models such as discrete choice models have a formal foundation, their integration into broader simulation systems is usually informally documented or solely defined in source code, making it difficult to replicate findings, reuse components, or enforce best practices. To address this, this paper conducts a comprehensive analysis of various aspects observed within existing different ABTM frameworks, highlighting the variation of their purposes, resolution, agent types, and modeling methods. Building on this analysis, we propose a metamodel tailored for ABTM specification, defining common concepts in an abstract syntax for defining agent attributes, behaviors, and interactions. Our proposed metamodel emphasizes a method-agnostic design, enabling integration and reuse of already existing behavioral models. This paves the way for automatic model validation, code generation, and sensitivity analysis. Additionally, we outline a conceptual mapping to Discrete Event System Specification (DEVS) to formalize the dynamic semantics, along with a graphical syntax for intuitive model creation by transport engineers. © 2025 Elsevier B.V.. All rights reserved.

关键词： ABM DEVS DSL formal specification simulation travel demand

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An Adaptive Varying-Parameter Projection Neural Network for Redundant Manipulator with Unknown Physical Parameters 17th

An Adaptive Varying-Parameter Projection Neural Network for...

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17th international conference on Intelligent robotics and Applications, ICIRA 2024

作者： Huang, Pan Hou, Che Chen, Wenjie Zheng, Lunan College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of High-end Heavy-Load Robots Midea Group Foshan528300 China Midea Corporate Research Center Foshan528311 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789819607761

Quadratic programming methods have been widely used to solve the redundancy problem of control manipulators due to their ability to optimize performance indicators under physical constraints. However, conventional quadratic programming methods require known parameters. In practical work environments, the physical parameters of the manipulator can be uncertain. Therefore, an adaptive control method that can synchronize parameter identification and network control is necessary. We propose an adaptive varying parameter projection neural network method that begins from the perspective of joint velocity space. This method decouples the unknown parameters and nonlinear parts that require identification in the Jacobian matrix of the manipulator. For the first time, a projection network with varying parameters is introduced for redundant decomposition, greatly improving the accuracy of trajectory tracking. Additionally, we design an iterative identification equation based on neural dynamics and introduce position feedback, making Jacobian matrix identification more accurate. The proposed method can solve the problem of redundant resolution of robotic arms under actual physical constraints and unknown physical parameters and can improve performance indicators. Theoretical analysis and simulation results demonstrate the feasibility and performance of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th international conference on Social robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Design and Analysis of Data Synthesis Pipeline for Assembly Detection with Domain Randomization

Design and Analysis of Data Synthesis Pipeline for Assembly ...

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international conference on Autonomous Robots and Agents, ICARA

作者： Cong Sun Peter Kinnell Claire Guo Mohammed M. Mabkhot Niels Lohse Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough UK Department of Mechenical Engineering University of Birmingham Birmingham UK

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

We propose a framework that leverages product CAD models and domain randomization techniques to generate training data via simulators, enabling the training and fine-tuning of object detection networks entirely on synthetic data for real-world deployment. This framework is tailored for product assembly and intelligent manufacturing, where automation and time efficiency are critical. Focusing on assembly state detection and object pose estimation tasks, we also analyze the impact of multiple Domain Randomization (DR) variables and measure the time consumption across configurations. Experimental results demonstrate the feasibility and necessity of using mature simulators to address current challenges associated with the resource-intensive nature of data collection and annotation.

关键词： Training Solid modeling Three-dimensional displays Training data Object detection Data collection Robot sensing systems Data models Time measurement Assembly

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