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A Novel Variable Resolution Torque Sensor Based on Variable Stiffness Principle

A Novel Variable Resolution Torque Sensor Based on Variable ...

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IEEE international conference on robotics and automation (ICRA)

作者： Sun, Xiantao Chen, Wenjie Zhang, Jianbin Wang, Jianhua Jiang, Jun Chen, Weihai Anhui Univ Sch Elect Engn & Automat Anhui Engn Lab Human Machine Cooperat Syst & Inte Hefei Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Beijing Inst Control Engn Beijing Peoples R China

ISBN: (纸本)9781728190778

High resolution and large range force/torque (F/T) measurements are usually required in many engineering tasks. However, most existing F/T sensors only have a fixed resolution over their whole ranges. The key lies in that it is difficult to well balance high resolution and large range in the sensor design. Taking the torque sensor for example, this paper presents a better compromise for this problem i.e., a novel variable resolution torque sensor based on variable stiffness principle. From the structural points of view, the sensor is constructed with multiple radial flexures to achieve a pure rotational motion with negligible parasitic center motions. Two resistive strain gauges (RSGs) are selected as the measuring units of the sensor to detect the applied external torque and meanwhile provide variable resolutions in the two different measuring ranges (each RSG for one range). Static and dynamic models of the sensor are established in details and validated through finite element analysis (FEA) to evaluate its characteristics. A principle prototype is finally fabricated and tested to verify the effectiveness of the presented design. RSGs are calibrated through a commercial six-axis F/T sensor from ATI Industrial automation, Inc. Experimental results show that the torque sensor can provide high and low resolutions in the small and large ranges respectively and possesses the first natural frequency of 673 Hz. In addition, the proposed variable resolution method can also be applied to the development of multi-axis F/T sensors.

关键词： Mechanical sensors Vibrations Analytical models Torque Sensor phenomena and characterization Strain measurement Shock absorbers

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Large Language Model for Intuitive Control of Robots in Micro-Assembly

Large Language Model for Intuitive Control of Robots in Micr...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Rolf Wiemann Niklas Terei Annika Raatz Institute of Assembly Technology and Robotics Leibniz University Hannover Garbsen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In an era of rapid technological advances in microdevices and photonics, the importance of efficient automation solutions is becoming increasingly evident. In particular, the automation of assembly processes is gaining importance as a significant portion of costs is incurred in this phase. Programming robots, especially in micro-assembly, requires a high level of expertise due to the complex assembly systems and processes. With the rapid development of increasingly powerful Large Language models (LLMs), their use for programming and controlling robots is becoming more and more prevalent. However, previous approaches have been limited to the field of service robots. In this paper, we present a framework that uses an LLM as an intuitive user interface for robot control in the field of micro-assembly. We integrate an LLM into a framework based on ROS2 (Robot Operation System 2), which enables skill-based control and programming of the micro-assembly robot. LLMs offer an intuitive access to the robot skills, thus facilitating robot control for users without knowledge of programming and robots. We demonstrate how an advanced LLM functions as an efficient interface, interpreting user instructions in context and initiating corresponding actions. Using a use case with exemplary user queries, we evaluate the performance of the implemented framework. Finally, we highlight further improvements and application possibilities.

关键词： Solid modeling automation Service robots Large language models Robot control Virtual reality User interfaces Planning Robots Robot programming

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Animation Anycolor: Enhancing Line Drawing Colorization with Keypoint Matching

Animation Anycolor: Enhancing Line Drawing Colorization with...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Liyao Lin, Zuzeng Wu, Danni Yu, Zihao Zhang, Suzhe Wu, Zixian Wang, Feng Tianjin University China Communication University of China China TuSimple United States

ISBN: (纸本)9798350368741

Colorization is a crucial but labor-intensive and time-consuming process of animation production. The automation of animation line-drawing colorization has become a prominent research topic. Recently, methods based on pre-trained text-to-image models have been explored for the task of line-drawing colorization. However, these approaches may result in colorization errors when dealing with complex situations such as positional changes and extensive motion commonly encountered in animated scenes. These issues are primarily attributed to the inadequate semantic correspondence between the reference images and line-drawings. To tackle this problem, we introduce the Animation Anycolor framework. This approach leverages spatial attention mechanisms to integrate the appearance features of reference images, ensuring consistent feature transmission. Furthermore, we present a novel technique that employs keypoint matching to explicitly direct the network to recognize the feature correspondence areas between reference and target images, thus effectively mitigating color confusion. Our method preserves the accuracy and naturalness of color results in scenes characterized by positional shifts and character movement. Comparative evaluations indicate that our method outperforms the baseline by an average of 11.3% on the FID metric. Notably, this method improves the efficiency of line-drawing colorization and reduces production costs. It also introduces new insights by combining in-context correspondences with knowledge from the pre-trained model. This approach has broad application prospects in the animation industry. © 2025 IEEE.

关键词： conditional generation diffusion models line-drawing colorization

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A Fuzzy Inference System-Based Hybrid Assignment Method for Cobot Assignment Problem

A Fuzzy Inference System-Based Hybrid Assignment Method for ...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Marrisa Kimaporn Wuttinan Nunkaew Department of Industrial Engineering Faculty of Engineering Thammasat School of Engineering (TSE) Thammasat University Pathum Thani Thailand

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

To address the complexity of a cobot assignment problem in smart manufacturing, this research has pioneered a new hybrid assignment method integrating the fuzzy inference system (FIS) into both multi-objective assignment and linear assignment models. It focuses on the critical tasks of assigning robots, jobs, and workers to create efficient cobot workstations, covering three core activities: robot-to-job, job-to-robot, and worker-to-robot-job assignments. An FIS-based multi-objective assignment model is utilized in the initial step to form effective sets of a single robot and jobs. The model also allows users to adjust the level of negative deviation from the target to enhance the satisfaction of the decision-maker. In the final step, workers are assigned and the cobot workstations are concurrently created using a FIS-based linear assignment model. To illustrate the method's efficacy, we provided a practical example that demonstrates its performance in action for industrial applications.

关键词： Fuzzy logic automation Collaborative robots Programming Data models Workstations Complexity theory

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methods and Tools for Robotization of Gear Shifting Mechanism for Highly Automated Vehicles

Methods and Tools for Robotization of Gear Shifting Mechanis...

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international Russian automation conference (RusAutoCon)

作者： Ivan A. Rubtsov Department of Innovative Technologies in Mechanical Engineering Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This paper describes models, methods, and tools to support the process of converting a vehicle's mechanical gearbox to robotize it in unmanned driving tasks. The described computer-aided design system (CAD) allows to determine the parameters of the gearbox shifting algorithm used in the control system of a highly automated vehicle (HAVs), depending on the vehicle load, road conditions and parameters of the vehicle itself. The methodology of modeling of the main processes occurring with HAVsduring driving is considered in detail. The developed models allow to determine the parameters of the algorithm of gear shifting for two driving profiles: with minimum fuel consumption and with maximum permissible speed of technological operation. The proposed methods and tools can be useful for designers of HAVscontrol systems, as well as for mechanizers in agriculture.

关键词： Solid modeling Torque Design automation Gears Roads Computational modeling Maintenance Fuels Robots Load modeling

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LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent

LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large...

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IEEE international conference on robotics and automation (ICRA)

作者： Jianing Yang Xuweiyi Chen Shengyi Qian Nikhil Madaan Madhavan Iyengar David F. Fouhey Joyce Chai Computer Science and Engineering University of Michigan Ann Arbor MI USA Independent Researcher New York University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics.

关键词： Visualization Three-dimensional displays Grounding Large language models Semantics Pipelines Training data

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Determination of the Strain Energy and Stress Intensity Factors with the Crack's Inclination under biaxial loading

Determination of the Strain Energy and Stress Intensity Fact...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Salman Khan Massab Junaid Fahd Nawaz Khan Tauheed Shehbaz Faculty of Mechanical Engineering (FME) Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology Swabi Pakistan

All components have certain flaws either interior or exterior on the surface which causes stress intensity under loading. The stress intensity exceeds the fracture toughness of the materials and causes failure of the component. A finite element analysis (FEA) was performed on Inconel 718 plate with a central crack under biaxial loading to compute the stress intensity factor (SIF) and the strain energy released rate (SERR) with the crack inclination ranging (ß) 0 °-150° and loading factor (B). The applied load is 10 MPa in the Y-direction (αy) while a range of -30 - +30 MPa in the X -direction (σ x ) with B of -3 to +3 was studied using COMSOL Multiphysics. The ratio of height to width of the plate is 1.25. The results show that the opening displacement was maximum at negative B (compression) while the minimum for positive B (tension) was at 90°. For mode I, the SIFs increase in compression and maximum at 90° while decreasing in tension and minimum at 90°. Beyond 90°, the SIFs increase under tensile while decreasing in compression load. For mode II, the SIFs were maximum at 45° for negative B while minimum at positive B.

关键词： Industries Loading Market research Mathematical models Distance measurement Finite element analysis Numerical models

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Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language models 41

Connecting the Dots: Collaborative Fine-tuning for Black-Box...

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41st international conference on Machine Learning, ICML 2024

作者： Wang, Zhengbo Liang, Jian He, Ran Wang, Zilei Tan, Tieniu University of Science and Technology of China China NLPR MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Nanjing University China

With the emergence of pretrained vision-language models (VLMs), considerable efforts have been devoted to fine-tuning them for downstream tasks. Despite the progress made in designing efficient fine-tuning methods, such methods require access to the model's parameters, which can be challenging as model owners often opt to provide their models as a black box to safeguard model ownership. This paper proposes a Collaborative Fine-Tuning (CraFT) approach for fine-tuning black-box VLMs to downstream tasks, where one only has access to the input prompts and the output predictions of the model. CraFT comprises two modules, a prompt generation module for learning text prompts and a prediction refinement module for enhancing output predictions in residual style. Additionally, we introduce an auxiliary prediction-consistent loss to promote consistent optimization across these modules. These modules are optimized by a novel collaborative training algorithm. Extensive experiments on few-shot classification over 15 datasets demonstrate the superiority of CraFT. The results show that CraFT achieves a decent gain of about 12% with 16-shot datasets and only 8,000 queries. Moreover, CraFT trains faster and uses only about 1/80 of the memory footprint for deployment, while sacrificing only 1.62% compared to the white-box method. Our code is publicly available at https://***/mrflogs/CraFT. Copyright 2024 by the author(s)

关键词： Visual languages

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A Comparison of Machine Learning Algorithms for Parkinson's Disease Detection 12

A Comparison of Machine Learning Algorithms for Parkinson's ...

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12th IEEE international conference on Control, automation and Information Sciences, ICCAIS 2023

作者： Vu, Tran Anh Ha, Ngo Thi Thu Duc, Le Minh Huy, Hoang Quang Dung, Nguyen Viet Huong, Pham Thi Viet Thanh, Nguyen Tien Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Vietnam National University International School Hanoi Viet Nam 108 Military Central Hospital Hanoi Viet Nam

ISBN: (纸本)9798350328783

Parkinson's disease is a progressive condition that impacts the neurological system and the areas of the body that are under the control of the nerves. Detecting and diagnosing PD early and accurately is essential for effective treatment and care. Biomedical speech analysis has emerged as a promising noninvasive approach for detecting and monitoring PD. We collected a data set from the University of California at Irvine (UCI) machine learning repository. The study investigates the utilization of a dataset to assess the suitability of machine learning (ML) algorithms for diagnosing Parkinson's disease. The research employs classification algorithms coupled with feature selection based on Pearson correlation. The data set is comprised of 195 voice recordings obtained from 31 patients during their examinations. Among them, 23 individuals in the study were diagnosed with Parkinson's disease. The age at the time of diagnosis ranged from 46 to 85 years. To handle this data, three methodologies are employed: a train/test split, k-fold cross-validation, and stratified k-fold cross-validation to ensure robust evaluation. Additionally, we use the F1 score, accuracy, and balanced accuracy measures to evaluate our models. The highly correlated selection data combined with data processing methods helps achieve positive results for all machine learning algorithms. Especially, Random Forest consistently outperformed other methods in our project. Random Forest with an accuracy of 95.42%, balanced accuracy of 93.98%, and an f1-score of 0.98. This demonstrates the capability of algorithms to diagnose Parkinson's disease effectively, even when confronted with imbalanced data. In our future research, we aim to integrate additional data on Parkinson's Disease in order to conduct a comprehensive evaluation of this condition. © 2023 IEEE.

关键词： Feature extraction

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A Multi-modal Hybrid Robot with Enhanced Traversal Performance*

A Multi-modal Hybrid Robot with Enhanced Traversal Performan...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhipeng He Na Zhao Yudong Luo Sian Long Xi Luo Hongbin Deng School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Computer Science and Technology Morphing Robotics Lab Dalian Maritime University Dalian China Yichang Testing Tech. Research Institution Yichang China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Current multi-modal hybrid robots with flight and wheeled modes have fallen into the dilemma that they can only avoid obstacles by re-taking off when encountering obstacles due to the poor performance of wheeled obstacle-crossing. To tackle this problem, this paper presents a novel multi-modal hybrid robot with the ability to actively adjust the wheel’s size, which is inspired by the behavior of the turtle’s legs when it encounters obstacles, to enhance the traversal performance. In detail, we describe the hardware design that allows the robot to achieve a modal switch between flight and wheeled modes through foldable structures and variable wheel diameters; then, we present the architecture to control these two morphing mechanisms. After that, we establish the theoretical kinematic models for both the foldable arm and variable wheel and carry out extensive experiments to test the performance of the foldable arm, the variable-diameter wheel, as well as the traversal performance of the robot. Experimental results show that the proposed multimodal robot can realize the function of a quadrotor, respond quickly with full-scale folding within 0.9 s, climb a maximum slope of 36°, and traverse narrow passageways, which exhibit superior mobility and environmental adaptability.

关键词： Legged locomotion Wheels Prototypes Manipulators Sensors Mobile robots Collision avoidance

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