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

共 24757 条记录，以下是281-290 订阅

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methods for Gastrointestinal Examination and Motion Simulation of a Roundworm-like Robot for Gastrointestinal Inspection 9

Methods for Gastrointestinal Examination and Motion Simulati...

引用

9th international conference on robotics and automation Engineering, ICRAE 2024

作者： Xu, Chang Hebei University of Technology Tianjin China

ISBN: (纸本)9798331518301

This article investigates the application of robotic systems, particularly a novel roundworm-like robot for gastrointestinal examinations. Unlike traditional endoscopic methods, this biomimetic approach leverages the movement patterns of roundworm-like to develop a non-invasive, efficient robotic system for navigating the human gastrointestinal tract. The proposed robot aims to enhance diagnostic accuracy and patient comfort by minimizing physical trauma and eliminating the need for anesthesia. This article utilizes a robotic model consisting of six rods and five joints to simulate the movement of a roundworm. The roundworm-like robot performs linear motion within a simulated model of the small intestine. By altering the length of each segment, the study aims to determine an appropriate size for the roundworm robot and analyze its feasibility. Initial simulations demonstrate the robot's potential to adapt its movement through the simple environment of the gastrointestinal tract, offering a promising alternative to current diagnostic techniques. © 2024 IEEE.

关键词： Endoscopy

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Condensed semi-implicit dynamics for trajectory optimization in soft robotics 7

Condensed semi-implicit dynamics for trajectory optimization...

引用

7th international conference on Soft robotics (ROBOSOFT)

作者： Menager, Etienne Bilger, Alexandre Jallet, Wilson Carpentier, Justin Duriez, Christian Univ Lille CNRS Cent Lille UMR 9189CRIStALINRIA F-59000 Lille France PSL Res Univ CNRS Dept Informat ENS Ecole Normale SuperInria Paris France LAAS CNRS 7 Ave Colonel Roche F-31400 Toulouse France

ISBN: (纸本)9798350381818

Over the past decades, trajectory optimization (TO) has become an effective solution for solving complex motion generation problems in robotics, ranging from autonomous driving to humanoids. Yet, TO methods remain limited to robots with tens of degrees of freedom (DoFs), limiting their usage in soft robotics, where kinematic models may require hundreds of DoFs in general. In this work, we introduce a generic method to perform trajectory optimization based on continuum mechanics to describe the behavior of soft robots. The core idea is to condense the dynamics of the soft robot in the constraint space in order to obtain a reduced dynamics formulation, which can then be plugged into numerical TO methods. In particular, we show that these condensed dynamics can be easily coupled with differential dynamic programming methods for solving TO problems involving soft robots. This method is evaluated on three different soft robots with different geometries and actuation.

关键词： Optimization and Optimal Control. Modeling Control and Learning for Soft Robots. Soft Robot Applications

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KoopNet: Joint Learning of Koopman Bilinear models and Function Dictionaries with Application to Quadrotor Trajectory Tracking 39

KoopNet: Joint Learning of Koopman Bilinear Models and Funct...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Folkestad, Carl Wei, Skylar X. Burdick, Joel W. CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

ISBN: (纸本)9781728196817

Nonlinear dynamical effects are crucial to the operation of many agile robotic systems. Koopman-based model learning methods can capture these nonlinear dynamical system effects in higher dimensional lifted bilinear models that are amenable to optimal control. However, standard methods that lift the system state using a fixed function dictionary before model learning result in high dimensional models that are intractable for real time control. This paper presents a novel method that jointly learns a function dictionary and lifted bilinear model purely from data by incorporating the Koopman model in a neural network architecture. Nonlinear MPC design utilizing the learned model can be performed readily. We experimentally realized this method on a multirotor drone for agile trajectory tracking at low altitudes where the aerodynamic ground effect influences the system's behavior. Experimental results demonstrate that the learning-based controller achieves similar performance as a nonlinear MPC based on a nominal dynamics model in medium altitude. However, our learning-based system can reliably track trajectories in near-ground flight regimes while the nominal controller crashes due to unmodeled dynamical effects that are captured by our method.

关键词： Dictionaries Trajectory tracking Optimal control Predictive models Aerodynamics Data models Real-time systems

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Learning Stable Dynamical Systems for Visual Servoing 39

Learning Stable Dynamical Systems for Visual Servoing

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IEEE international conference on robotics and automation (ICRA)

作者： Paolillo, Antonio Saveriano, Matteo Dalle Molle Inst Artificial Intelligence IDSIA USI SUPSI Lugano Switzerland Univ Trento Dept Ind Engn DII Trento Italy

ISBN: (纸本)9781728196817

This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dynamical systems allow to program additional skills without explicitly coding them in the visual servoing law, but leveraging few demonstrations of the full desired behavior. On the other, visual servoing allows to consider exteroception into the dynamical system architecture and be able to adapt to unexpected environment changes. The beneficial combination of the two concepts is proven by applying three existing dynamical systems methods to the visual servoing case. Simulations validate and compare the methods;experiments with a robot manipulator show the validity of the approach in a real-world scenario.

关键词： Adaptation models automation Systems architecture Visual servoing Encoding Behavioral sciences Dynamical systems

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Physics-Informed Neural Network for Transient Modeling of Large-Scale Wind Farm 10

Physics-Informed Neural Network for Transient Modeling of La...

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10th international conference on automation, robotics, and Applications, ICARA 2024

作者： Wang, Hongyi Sun, Pingyang Konstantinou, Georgios Chen, Zhe Aalborg University AAU energy Aalborg Denmark University of New South Wales EE and Telecommunications Sydney Australia

ISBN: (纸本)9798350394245

The complexity of transient characteristics in large-scale wind farms (WF) hinders the application of machine learning algorithms. This paper proposes a neural network-based learning method to model the transient characteristics of large-scale WF. The complexity of the physical information library is simplified through an iterative algorithm. The dynamic library obtained based on neural networks can induce the machine learning framework to rapidly learn the transient characteristics of large-scale WF. Moreover, there is no need for excessive mechanistic analysis and speculation regarding the transient behavior of WF. The effectiveness of the proposed method is verified in the simulation model of a WF. © 2024 IEEE.

关键词： Iterative methods

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Virtual Reality robotics with Internet-of-Things for Student Learning on Industrial robotics and automation in Manufacturing

Virtual Reality Robotics with Internet-of-Things for Student...

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2024 ASEE Annual conference and Exposition

作者： Chiou, Richard Tseng, Tzu-Liang Bill Rahman, Md Fashiar Drexel University United States University of Texas El Paso United States

This paper explores the experience of implementing virtual reality (VR) laboratory activities with Internet-of-Things (IoT) for students to learn industrial robotics and automation in manufacturing. This work provides an innovative solution for optimizing learning effectiveness and improving educational outcomes through the development of VR models that can be used and integrated into the existing robotics laboratory. We explore methods of using ABB RobotStudio to allow students to program traditional industrial robots using the project-based learning approach. Key features of how they allow the user to move a virtual robot end effector and generate a tool path are described. A comparison between the conventional approach of robot programming using the teach pedant and the VR-based approach is then presented. The project provides students with opportunities to work with industrial robots. Students complete structured laboratory activities that introduce them to different aspects of applied robotics, including the design of end-effector tooling and fixtures for different tasks. The goal is to apply these VR simulators to train undergraduate engineering, engineering technology students, and professionals in robotics and automation education;and to offer experiential learning opportunities in 3D modeling, simulation, and visualization for robotics and automation. The students were given weekly robotics laboratory experiments in the course on robotics and mechatronics. VR robotics integrated with Internet-of-Things based mechatronics enables students to explore innovative approaches to integrate theoretical knowledge with practical applications, enhancing information retention, and promoting critical thinking. © American Society for Engineering Education, 2024.

关键词： End effectors

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The Role of robotics: automation in Shoe Manufacturing 20

The Role of Robotics: Automation in Shoe Manufacturing

引用

20th IEEE/ASME international conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Dias, Pedro A. Petry, Marcelo R. Rocha, Luis F. INESC TEC Inst Engn Sistemas & Computadores Tecno Porto Portugal Inst Politecn Porto Inst Super Engn Porto Porto Portugal

ISBN: (纸本)9798331516246;9798331516239

Emerging from a rich heritage, the shoe manufacturing industry stands as one of the world's most enduring and tradition-bound sectors. While renowned for their high-quality craftsmanship, countries like Portugal and Italy share the spotlight with those who focus on mass production methods. Regardless of their manufacturing model, both must adapt to the evolving competitive landscape by embracing innovative manufacturing techniques. robotics has emerged as a transformative force within the shoe industry, offering a path towards enhanced working conditions for employees while simultaneously reducing reliance on manual labor and bolstering productivity. The main focus of this paper is the comprehensive literature review, which examines the advancements made by researchers in various stages of shoe production, including roughing, gluing, finishing, and lasting. This article sheds light on the industry's response to modernization and efficiency imperatives, providing a thorough understanding of robotics in shoe manufacturing automation. A case study on the real implementation and simulation of a robotic cell for sole roughing is also presented. The results revealed that the robotic cell maintains the production cadence.

关键词： Footwear automation Shoe production Sole roughing Shoe industry

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Active Learning for Testing and Evaluation in Field robotics: A Case Study in Autonomous, Off-Road Navigation 39

Active Learning for Testing and Evaluation in Field Robotics...

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IEEE international conference on robotics and automation (ICRA)

作者： Gregory, Jason M. Sahu, Daniel Lancaster, Eli Sanchez, Felix Rocks, Trevor Kaukeinen, Brian Fink, Jonathan Gupta, Satyandra K. DEVCOM Army Res Lab Adelphi MD 20783 USA Booz Allen Hamilton Washington DC USA Univ Southern Calif Viterbi Sch Engn Los Angeles CA USA

ISBN: (纸本)9781728196817

Testing and evaluation of field robotic systems requires both experimentation in representative conditions and human supervision to effectively assess components, manage risk, and interpret results. Due to the complexity of robotic systems, we argue this experimentation should be done adaptively by using insights gained from previous trials. Furthermore, we envision an advisory system that could assist experimenters with selecting trial configurations by learning and accounting for human preferences and risk tolerances;however, formal methods for human decision making in the context of field robotic experimentation remains an open question. In this work, we present and analyze a case study for how decisions were made during the testing and evaluation of an off-road, autonomous navigation system. From the perspective of active learning, we find that Bayesian Optimization is a promising mathematical framework for modeling human decision making in adaptive experimental design of field robotics and that a combination of the EI, KG, and PES acquisition functions would likely be useful for realizing an advisory system.

关键词： Adaptation models automation Navigation Decision making Mathematical models Bayes methods Complexity theory

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Numerical Analysis of Internal Defect Detection in Aluminum Alloy Pipes Aims to Provid Data Support for AI models

Numerical Analysis of Internal Defect Detection in Aluminum ...

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2024 lEEE international conference on Advanced Information, Mechanical Engineering, robotics and automation, AIMERA 2024

作者： Bai, Pengshihan Wang, Shang Long, Jianqiao Zheng, Kexin Ma, Jing Li, Nandu Bruce, Steve University of Science and Technology Beijing School of Metallurgical and Ecological Engineering Beijing China Beijing Polytechnic School of Automotive Engineering Beijing China Newcastle University School of Computing United Kingdom Dezhou Natural Resources Bureau Shandong China Information China University of Geosciences School of Mechanical Engineering and Electronic Wuhan China SEH Automation Pte Ltd Department of Research & Development Singapore

ISBN: (纸本)9798350343335

The online effective detection of internal defects in metals has long faced many challenges. The rapid development of Artificial Intelligence (AI) technology has provided possibilities for improving defect detection techniques. However, AI algorithms rely heavily on large amounts of data, which are difficult to obtain through traditional experimental methods. To address this issue, this study proposes a method for the internal defect detection of aluminum alloy pipes and provides data support for AI models and algorithms. We established a "pinch roller-pressure roller-aluminum alloy pipes"model and obtained the variation rules of Mises stress, constraint reaction force caused by the change of internal defect position through numerical calculation. The results show that the position change of the internal defect has a clear functional relationship with the constraint reaction force of roller-A. When the defect is located near the lower part of roller-B, the constraint reaction force of roller-A reaches the maximum value (978.8 N). This study provides empirical evidence for establishing an online defectdetection AI models. It clarifies the evolution pattern of constraint forces related to crack location. These findings provide data support for developing efficient and accurate defect online detection systems based on AI to address the limitations of traditional methods. © 2024 IEEE.

关键词： Aluminum pipe

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Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty 39

Bayesian Optimisation for Robust Model Predictive Control un...

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IEEE international conference on robotics and automation (ICRA)

作者： Guzman, Rel Oliveira, Rafael Ramos, Fabio Univ Sydney Sch Comp Sci Sydney NSW Australia Australian Res Councils Ctr Data Analyt Resources & Environm DARE Canberra ACT Australia Univ Sydney Brain & Mind Ctr Sydney NSW Australia NVIDIA Santa Clara CA USA

ISBN: (纸本)9781728196817

We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards. In particular, we develop a Bayesian optimisation (BO) algorithm with a heteroscedastic noise model to deal with varying noise across the MPC hyper-parameter and dynamics model parameter spaces. Typical homoscedastic noise models are unrealistic for tuning MPC since stochastic controllers are inherently noisy, and the level of noise is affected by their hyper-parameter settings. We evaluate the proposed optimisation algorithm in simulated control and robotics tasks where we jointly infer control and dynamics parameters. Experimental results demonstrate that our approach leads to higher cumulative rewards and more stable controllers.

关键词： Adaptation models Heuristic algorithms Stochastic processes Predictive models Bayes methods Task analysis Optimization

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