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共 24757 条记录，以下是481-490 订阅

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FEJ2: A Consistent Visual-Inertial State Estimator Design 39

FEJ2: A Consistent Visual-Inertial State Estimator Design

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chen, Chuchu Yang, Yulin Geneva, Patrick Huang, Guoquan Univ Delaware Robot Percept & Nav Grp RPNG Newark DE 19716 USA

ISBN: (纸本)9781728196817

In this paper, we propose a novel consistent state estimator design for visual-inertial systems. Motivated by first-estimates Jacobian (FEJ) based estimators - which uses the first-ever estimates as linearization points to preserve proper observability properties of the linearized estimator thereby improving the consistency - we carefully model measurement linearization errors due to its Jacobian evaluation and propose a methodology which still leverages FEJ to ensure the estimator's observability properties, but additionally explicitly compensate for linearization errors caused by poor first estimates. We term this estimator FEJ2, which directly addresses the discrepancy between the best Jacobian evaluated at the latest state estimate and the first-estimates Jacobian evaluated at the first-time-ever state estimate. We show that this process explicitly models that the FEJ used is imperfect and thus contributes additional error which, as in FEJ2, should be modeled and consistently increase the state covariance during update. The proposed FEJ2 is evaluated against state-of-the-art visual-inertial estimators in both Monte-Carlo simulations and real-world experiments, which has been shown to outperform existing methods and to robustly handle poor first estimates and high measurement noises.

关键词： Jacobian matrices Monte Carlo methods automation Measurement uncertainty Noise measurement Observability

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"Educational robotics integration into Storytelling activities to promote kindergarteners' communication, collaboration, creativity, and critical thinking. A scoping review: 2014-2024" 9

"Educational Robotics integration into Storytelling activiti...

引用

9th South-East Europe Design automation, Computer Engineering, Computer Networks and Social Media conference, SEEDA-CECNSM 2024

作者： Palioura, Maria Rapti, Sophia Sapounidis, Theodosios School of Philosophy and Education Department of Education Thessaloniki Greece

ISBN: (纸本)9798350342482

Communication, collaboration, creativity, and critical thinking (4Cs) are regarded as vital skills for well-being and success. Nowadays, education needs to promote them by using innovative tools and enriching traditional methods. So, educational robotics integrated into storytelling activities arise as contributors to that. Yet, there hasn't been any mapping of the research conducted in this field. Consequently, we present a scoping review of 18 papers out of 203 initially retrieved from 5 databases from 2014 to 2024 exploring kindergarteners' 4Cs via robotics combined with storytelling. Based on our findings, communication, and collaboration are examined the most and creativity the least. Most of the researchers used the mixed method focusing on qualitative tools, examining quite a small sample size, and utilizing humanoid robots most often. It is illustrated that robotics integrated into storytelling can promote kindergarteners' 4Cs but further research is needed related to children's expression of emotions while interacting with the robots and peers. Moreover, this scoping provides activities, included in the reviewed papers, combining robotics and storytelling to develop kindergarteners' 4Cs. Finally, it is recommended to design more quantitative research in the related field and form models of full activities utilizing robotics and storytelling to promote all 4Cs in kindergarten. © 2024 IEEE.

关键词： Anthropomorphic robots

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A Genetic-Firefly Algorithm Based CNN-LSTM for Lithium-Ion Battery Fault Diagnosis 6

A Genetic-Firefly Algorithm Based CNN-LSTM for Lithium-Ion B...

引用

6th international conference on robotics, Control and automation Engineering, RCAE 2023

作者： Liu, Xiaolu Jia, Li Li, Yang School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798350315301

Lithium-ion batteries are a crucial component of new energy. Studying the fault diagnosis method of the batteries can ensure the safety of the system during operation. In this paper, a diagnosis method is studied based on the efficient channel attention (ECA) mechanism. The ECA is combined with convolutional neural network (CNN) and long short-Term memory (LSTM) to design the diagnosis model. To determine the values of hyperparameters, a hyperparameter selection method based on genetic-firefly algorithm is implemented. The hyperparameter optimization methods are illustrated by the simulated battery fault data. The results show that the proposed method can make the classification accuracy reach 100%. © 2023 IEEE.

关键词： Failure analysis

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Ratio-of-Distance-Based Adaptive Formation Control of Wheeled Robot Networks with Unknown Parameters and External Disturbances 18

Ratio-of-Distance-Based Adaptive Formation Control of Wheele...

引用

IEEE 18th international conference on Control and automation (ICCA)

作者： Wang, Yujie Liu, Shuai Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

ISBN: (纸本)9798350354416;9798350354409

In this note, the adaptive formation control for wheeled robot networks with unknown parameters and external disturbances is discussed. Unlike most existing research which utilize distance or bearing measurements to realize formation targets, ratio-of-distance (RoD) measurements are introduced into the wheeled robot networks to reach the desired formation shapes. In reality, in lieu of ideal mass-point models popularly adopted in the study of formation control, wheeled robots are described by nonholonomic kinematics which is nonlinear. To handle it better, we consider transforming the system model into an Euler-Lagrange-like system model. After system transformation, system parameters can be linearized. To simulate extensive situations, we consider these parameters are unknown, and we also consider there exist external disturbances in system. Then, we propose an adaptive distributed control law for each wheeled robot to drive the whole robotic network to acquire the desired formation shapes through RoD measurements and backstepping approach. The Lyapunov method is employed to prove the system stability.

关键词： Lyapunov methods

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Humanoid robotics Powered by Vision-Based LLM Zero Shot Interactions

Humanoid Robotics Powered by Vision-Based LLM Zero Shot Inte...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Smith, Gabriel David Debrah-Pinamang, Kwasi Voicu, Razvan Cristian Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Advancements in artificial intelligence (AI) have transformed robotics by enabling systems to autonomously execute complex tasks with minimal human involvement. Traditional methods, however, often depend on costly hardware, continuous monitoring, and intricate software integration, which constrain scalability and widespread implementation. This research presents an innovative approach that combines vision-based large language models (LLMs) with zero-shot prompting to autonomously program robotic systems, including a humanoid robot equipped with dual manipulators. The proposed system harnesses contextual image data to efficiently generate task-specific code, eliminating the need for iterative corrections. Training is conducted through OpenAI's Assistant feature, utilizing documents predominantly comprising images, while continuous operation is facilitated by a self-looping mechanism. Experimental results highlight the system's capability to perform manipulator tasks with notable accuracy, paving the way for scalable, adaptive, and dynamic automation. This study addresses both practical and theoretical challenges in automation, providing a cost-effective framework for next-generation robotic systems. © 2025 IEEE.

关键词： Anthropomorphic robots

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Application of the Polynomial Maximization Method for Estimating Nonlinear Regression Parameters with Non-gaussian Asymmetric Errors 28th

Application of the Polynomial Maximization Method for Estima...

引用

2024 international conference automation

作者： Zabolotnii, Serhii Tkachenko, Oleksandr Nowakowski, Waldemar Warsza, Zygmunt L. Cherkasy State Business Coll Cherkassy Ukraine Uzhgorod Natl Univ Uzhgorod Ukraine Ivan Franko Natl Univ Lviv Lvov Ukraine Casimir Pulaski Radom Univ Radom Poland Polish Metrol Soc Radom Poland

ISBN: (纸本)9783031782657;9783031782664

In the article, an alternative approach to estimating parameters in nonlinear regression models under asymmetric error distributions is examined. A novel approach for adaptive estimation is proposed, which is based on the use of second-order polynomial functions. This enables a straightforward implementation to account for deviations from Gaussian idealization in the form of moments up to the fourth order. It is demonstrated that the overall problem can algorithmically be reduced to the numerical solution of a system of nonlinear stochastic equations. Analytical expressions are obtained, which facilitate the estimation of parameters and the analysis of their asymptotic variance. Statistical modeling using the Monte Carlo method was conducted, and the results indicate that the accuracy of PMM2 estimates is comparable to SLS estimates and significantly so exceeds the accuracy of OLS estimates.

关键词： Polynomial Maximization Method non-linear regression models asymmetric error distributions Monte Carlo method higher order statistics

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An Empirical Evaluation of Out-of-Distribution Detection Using Pretrained Language models 5

An Empirical Evaluation of Out-of-Distribution Detection Usi...

引用

5th international conference on Control and robotics (ICCR)

作者： Yoon, Byungmu Kim, Jaeyoung Gachon Univ Seongnam Si Gyeonggi Do South Korea

ISBN: (纸本)9798350307627

In Natural Language Processing (NLP) tasks, detecting out-of-distribution (OOD) samples is essential to safely deploy a language model in real-world problems. Recently, several studies report that pre-trained language models (PLMs) accurately detect OOD data compared to LSTM, but we empirically find that PLMs show sub-par OOD detection performance when (1) OOD samples have similar semantic representation to in-distribution (IND) samples and (2) PLMs are finetuned under data scarcity settings. To alleviate above issues, state-of-the-art uncertainty quantification (UQ) methods can be used, but the comprehensive analysis of UQ methods with PLMs has received little consideration. In this work, we investigate seven UQ methods with PLMs and show their effectiveness in the text classification task.

关键词： OOD Detection Pre-trained Language models(PLMs) Uncertainty Quantification(UQ)methods

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Enhanced Parameter Identification of Fractional-Order PMSM in Robotic Joints Using Lévy Crayfish Optimization Algorithm

Enhanced Parameter Identification of Fractional-Order PMSM i...

引用

2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Fan, Hongjie Wei, Hongxing Xu, Dong School of Mechanical Engineering and Automation Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665481090

The accuracy and response speed of torque in robotic joint models are crucial for enhancing the compliance of robot interactions. Consequently, parameter identification of the permanent magnet synchronous motor (PMSM) model in robotic joints has garnered significant attention from researchers world-wide. To further improve model accuracy, this study focuses on the parameter identification of a fractional-order model of PMSM. This paper introduces the Lévy Crayfish Optimization Algorithm (LCOA), an enhancement of the crayfish optimization algorithm (COA), designed to overcome COA's tendency to become trapped in local optima. The proposed algorithm employs the Lévy flights (LF) strategy to optimize the foraging stage, improving global search capabilities. As a result, the new algorithm exhibits superior stability and computational accuracy. Simulations were conducted to discuss the accuracy of parameter identification for the fractional-order PMSM. © 2024 IEEE.

关键词： Permanent magnets

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EAPNet: Enhanced Attention-Guided Network for Fine-Grained Semantic Segmentation 7

EAPNet: Enhanced Attention-Guided Network for Fine-Grained S...

引用

7th international conference on robotics, Control and automation Engineering, RCAE 2024

作者： Huang, Minyu Hou, Beiping Pan, Feng Dong, Jianwei Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou China China Telecom Corporation Ltd. Quzhou Hangzhou China

ISBN: (纸本)9798350355642

Ground-based cloud observation is essential for meteorological research and climate monitoring. Existing methods for cloud detection and classification in whole-sky images (WSIs) often focus solely on binary segmentation (sky and cloud) or image-level classification, which perform poorly in tasks requiring the classification and segmentation of different cloud types. This paper presents an enhanced attention-guided pyramid network (EAPNet) designed to enhance the accuracy and robustness of fine-grained cloud classification in whole-sky images through deep learning techniques. The key innovations include: 1) the utilization of the pyramid spatial pooling (PSP) module to capture multi-scale contextual information;2) the introduction of the convolutional block attention module (CBAM), which leverages an attention mechanism to enhance feature representation;and 3) an improved decoder module that enhances localization accuracy. Experimental results demonstrate that EAPNet outperforms existing mainstream methods in both cloud classification and segmentation tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Learning-Guided Exploration for Efficient Sampling-Based Motion Planning in High Dimensions 39

Learning-Guided Exploration for Efficient Sampling-Based Mot...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Schramm, Liam Boularias, Abdeslam Rutgers State Univ Comp Sci Dept New Brunswick NJ 08901 USA

ISBN: (纸本)9781728196817

Optimal motion planning is a long-studied problem with a wide range of applications in robotics, from grasping to navigation. While sampling-based motion planning methods have made solving such problems significantly more feasible, these methods still often struggle in high-dimensional spaces wherein exploration is computationally costly. In this paper, we propose a new motion planning algorithm that reduces the computational burden of the exploration process. The proposed algorithm utilizes a guidance policy acquired offline through model-free reinforcement learning. The guidance policy is used to bias the exploration process in motion planning and to guide it toward promising regions of the state space. Moreover, we show that the gradients of the corresponding learned value function can be used to locally fine-tune the sampled states. We empirically demonstrate that the proposed approach can significantly reduce planning time and improve success rate and path quality.

关键词： automation Navigation Computational modeling Reinforcement learning Grasping Manipulators Planning

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