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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521-530 订阅

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Research on 3D Model Classification and Retrieval Based on Graph Convolutional Neural Network 14th

Research on 3D Model Classification and Retrieval Based on G...

引用

14th international Workshop of Advanced Manufacturing and automation, IWAMA 2024

作者： Deng, Junqiang Zhou, Chuanhong Chen, Xi School of Automation and Mechanical Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200072 China

ISBN: (纸本)9789819626243

With the popularization of CAD technology in the manufacturing industry, the number of 3D models in the industry is rapidly expanding and becoming increasingly complex. These models carry the intelligence of designers and are highly reusable in the product development process. Manufacturing practitioners realize that they can accelerate product development by reusing the structure of previously designed models. Today, many open-source and private model databases have emerged, but efficient and accurate retrieval of 3D models is still an urgent problem. Current mainstream 3D model classification and retrieval algorithms commonly use deep learning techniques, which can usually be subdivided into voxel-based, point cloud-based and view-based methods. However, they all have their own shortcomings. Therefore, in this paper, we propose a 3D model classification and retrieval algorithm based on graph convolutional neural network for feature extraction of STEP files, which improves the efficiency and accuracy of 3D model classification and retrieval. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Minotaur - A Single Lane Navigation System 8

Minotaur - A Single Lane Navigation System

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8th international conference on automation, robotics and Applications (ICARA)

作者： Kaushal, Harsh Dhruv, Vinayak Sreedevi, Indu Delhi Technol Univ Elect & Commun Dept New Delhi India Delhi Technol Univ Elect Engn Dept New Delhi India

ISBN: (纸本)9781665483834

Autonomous driving has been a major field for research, with recent developments in self-driving vehicles, various studies are being done for building affordable autonomous rovers and bots. In this paper we propose Minotaur, an autonomous differential drive rover with perception and control abilities that have been designed on the ROS(Robot Operating System) platform. We have introduced a deep learning based solution for lane detection, replacing the previously used classical methods and sensors. For safeguarding, a rollover protection system and an M-Stop Button are incorporated, along with hand-curated hardware interfacing and High-Level PID tuning. We discuss our navigation stack that has been successfully deployed on a hardware system capable of Single Lane Navigation and obstacle avoidance along with waypoint Navigation. Improvements from traditional methods and results have also been presented.

关键词： Autonomous vehicles Deep learning methods Embedded systems Perception robotics ROS(Robot Operating System)

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Artificial Neural Network Guided Compensation of Nonlinear Payload and Wear Effects for Industrial Robots 19

Artificial Neural Network Guided Compensation of Nonlinear P...

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19th IEEE international conference on automation Science and Engineering, CASE 2023

作者： Raible, Julian Rettig, Oliver Alt, Benjamin Yaman, Alper Gauger, Isabelle Biasi, Lorenzo Müller, Silvan Katic, Darko Strand, Marcus Huber, Marco F. Institute of Industrial Manufacturing and Management Iff University of Stuttgart Stuttgart70569 Germany Baden-Württemberg Cooperative State University Karlsruhe76133 Germany ArtiMinds Robotics GmbH Karlsruhe76131 Germany Stuttgart70569 Germany

ISBN: (纸本)9798350320695

The absolute accuracy of industrial robots is influ-enced by numerous geometric and non-geometric errors. Most state-of-the-art calibration and compensation methods consider only the geometric errors and neglect the non-geometric ones. In this paper, a hybrid compensation approach is proposed that combines well-known kinematic models with a model-free data-driven component using a neural network. This allows the use of established calibration methods for the geometric influences captured in the kinematic model to improve the non-geometric error compensation by the neural network. The proposed approach is applied in two use cases: payload and wear compensation. Simulations and real experiments show the improved absolute accuracy of the hybrid compensation approach compared to classical calibration methods. © 2023 IEEE.

关键词： Error compensation

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Soft Sensors for Industrial Applications: Comparison of Variables Selection methods and Regression models

Soft Sensors for Industrial Applications: Comparison of Vari...

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2023 international conference on Control, automation and Diagnosis, ICCAD 2023

作者： Leiria, Joana Salles, Rodrigo Mendes, Jerome Sousa, Pedro University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Rua Cidade Poitiers Coimbra3000-108 Portugal

ISBN: (纸本)9798350347074

The digitalization of industrial environments has enabled the development of tools that make the production process more efficient and safer. In this sense, the Soft Sensor (SS) plays a fundamental role. Through historical data and indirect measurements, it is possible to estimate the value of important variables that are difficult to measure. This paper presents the SS development process: data collection and pre-processing, variable selection, model selection for SS implementation, model training and testing, and performance evaluation. The selection of variables was made with the help of Pearson Correlation, Mutual Information, and fastTracker algorithm techniques. For the implementation of SS have been tested several models: Multiple Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator, Elastic Net, Support Vector Regression and Gaussian Mixture models. Four datasets were used to test the development of the SS. © 2023 IEEE.

关键词： Correlation methods

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Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning 39

Visuotactile-RL: Learning Multimodal Manipulation Policies w...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Hansen, Johanna Hogan, Francois Rivkin, Dmitriy Meger, David Jenkin, Michael Dudek, Gregory Samsung AI Res Ctr Montreal 1000 Sherbrooke St WSuite 1100 Montreal PQ Canada

ISBN: (纸本)9781728196817

Manipulating objects with dexterity requires timely feedback that simultaneously leverages the senses of vision and touch. In this paper, we focus on the problem setting where both visual and tactile sensors provide pixel-level feedback for Visuotactile reinforcement learning agents. We investigate the challenges associated with multimodal learning and propose several improvements to existing RL methods;including tactile gating, tactile data augmentation, and visual degradation. When compared with visual-only and tactile-only baselines, our Visuotactile-RL agents showcase (1) significant improvements in contact-rich tasks;(2) improved robustness to visual changes (lighting/camera view) in the workspace;and (3) resilience to physical changes in the task environment (weight/friction of objects).

关键词： Visualization Perturbation methods Optical feedback Tactile sensors Reinforcement learning Robustness Optical sensors

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A Study of the Magnetically Active Objects Motion in a Closed Channel Under the Influence of Magnetic Force

A Study of the Magnetically Active Objects Motion in a Close...

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2024 international Russian automation conference, RusAutoCon 2024

作者： Malchikov, Andrei Ryapolov, Petr South-West State University Department of Mechanics Mechatronics and Robotics Kursk Russia South-West State University Department of Nanotechnologies Microelectronics General and Applied Physics Kursk Russia

The paper examines various models of the motion of magnetically active micro objects under the influence of a magnetic field generated by a moving permanent magnet. A mathematical model is proposed that describes the controlled motion of a spherical microrobot within a closed channel that simulates a vessel cavity. A key feature of the model is its ability to simulate the complex magnetic interactions between the permanent magnet and the investigated object, leading to intricate motions of the microrobot. The results of studying various motion modes are presented, and a comparative analysis is performed with the results of full-scale experiments conducted on a laboratory bench. © 2024 IEEE.

关键词： Microrobots

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AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language models 24

AutoM3L: An Automated Multimodal Machine Learning Framework ...

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32nd ACM international conference on Multimedia, MM 2024

作者： Luo, Daqin Feng, Chengjian Nong, Yuxuan Shen, Yiqing Dataa Robotics Shenzhen China Meituan Inc. Shenzhen China Johns Hopkins University Baltimore United States

ISBN: (纸本)9798400706868

Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unimodal scenarios and require extensive manual configuration. Recent advancements in Large Language models (LLMs) have showcased their exceptional abilities in reasoning, interaction, and code generation, presenting an opportunity to develop a more automated and user-friendly framework. To this end, we introduce AutoM3L, an innovative Automated Multimodal Machine Learning framework that leverages LLMs as controllers to automatically construct multimodal training pipelines. AutoM3L comprehends data modalities and selects appropriate models based on user requirements, providing automation and interactivity. By eliminating the need for manual feature engineering and hyperparameter optimization, our framework simplifies user engagement and enables customization through directives, addressing the limitations of previous rule-based AutoML approaches. We evaluate the performance of AutoM3L on six diverse multimodal datasets spanning classification, regression, and retrieval tasks, as well as a comprehensive set of unimodal datasets. The results demonstrate that AutoM3L achieves competitive or superior performance compared to traditional rule-based AutoML methods. Furthermore, a user study highlights the user-friendliness and usability of our framework, compared to the rule-based AutoML methods. © 2024 Owner/Author.

关键词： Adversarial machine learning

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Convex strategies for trajectory optimisation: application to the Polytope Traversal Problem 39

Convex strategies for trajectory optimisation: application t...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Tonneau, Steve Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781728196817

Non-linear trajectory optimisation methods require good initial guesses to converge to a locally optimal solution. A feasible guess can often be obtained by allocating a large amount of time for the trajectory to be complete. However for unstable dynamical systems such as humanoid robots, this quasi-static assumption does not always hold. We propose a conservative formulation of the trajectory problem that simultaneously computes a feasible path and its time allocation. The problem is solved as a convex optimisation problem guaranteed to converge to a feasible local optimum. The approach is evaluated with the computation of feasible trajectories that traverse sequentially a sequence of polytopes. We demonstrate that on instances of the problem where quasi static solutions are not admissible, our approach is able to find a feasible solution with a success rate above 80% in all the scenarios considered, in less than 10ms for problems involving traversing less than 5 polytopes and less than 1s for problems involving 20 polytopes, thus demonstrating its ability to reliably provide initial guesses to advanced non linear solvers.

关键词： automation Optimization methods Humanoid robots Trajectory Resource management Reliability Dynamical systems

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Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns 39

Targeted Attack on Deep RL-based Autonomous Driving with Lea...

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IEEE international conference on robotics and automation (ICRA)

作者： Buddareddygari, Prasanth Zhang, Travis Yang, Yezhou Ren, Yi Arizona State Univ Sch Comp & Augmented Intelligence Act Percept Grp Tempe AZ 85287 USA Cornell Univ Coll Engn Ithaca NY 14853 USA Arizona State Univ Act Percept Grp Tempe AZ USA Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

ISBN: (纸本)9781728196817

Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks, raising concerns about the application of such models to risk-sensitive tasks such as autonomous driving. Threat models for these demonstrations are limited to (1) targeted attacks through real-time manipulation of the agent's observation, and (2) untargeted attacks through manipulation of the physical environment. The former assumes full access to the agent's states/observations at all times, while the latter has no control over attack outcomes. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical objects in the environment, a threat model that combines the practicality and effectiveness of the existing ones. Through analysis, we demonstrate that a pre-trained policy can be hijacked within a time window, e.g., performing an unintended self-parking, when an adversarial object is present. To enable the attack, we adopt an assumption that the dynamics of both the environment and the agent can be learned by the attacker. Lastly, we empirically show the effectiveness of the proposed attack on different driving scenarios, perform a location robustness test, and study the tradeoff between the attack strength and its effectiveness. Code is available at https://***/ASU-APG/ Targeted- Physical- Adversarial- Attacks-on-AD

关键词： Visualization Three-dimensional displays Perturbation methods Heuristic algorithms Reinforcement learning Robustness Real-time systems

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Exploration with Global Consistency Using Real-Time Re-integration and Active Loop Closure 39

Exploration with Global Consistency Using Real-Time Re-integ...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhang, Yichen Zhou, Boyu Wang, Luqi Shen, Shaojie Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

ISBN: (纸本)9781728196817

Despite recent progress of robotic exploration, most methods assume that drift-free localization is available, which is problematic in reality and causes severe distortion of the reconstructed map. In this work, we present a systematic exploration mapping and planning framework that deals with drifted localization, allowing efficient and globally consistent reconstruction. A real-time re-integration-based mapping approach along with a frame pruning mechanism is proposed, which rectifies map distortion effectively when drifted localization is corrected upon detecting loop-closure. Besides, an exploration planning method considering historical viewpoints is presented to enable active loop closing, which promotes a higher opportunity to correct localization errors and further improves the mapping quality. We evaluate both the mapping and planning methods as well as the entire system comprehensively in simulation and real-world experiments, showing their effectiveness in practice. The implementation of the proposed method will be made open-source for the benefit of the robotics community.

关键词： Location awareness Systematics Image analysis automation Benchmark testing Distortion Real-time systems

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