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检索条件"机构=Mechatronics and Robotics"

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CoaxHaptics-3RRR: A Novel Mechanically Overdetermined Haptic Interaction Device Based on a Spherical Parallel Mechanism 1

CoaxHaptics-3RRR: A Novel Mechanically Overdetermined Haptic...

引用

1st IEEE Conference on Telepresence, Telepresence 2024

作者： Dinc, Huseyin Tugcan Hulin, Thomas Rothammer, Michael Seong, HyeonSeok Willberg, Bertram Pleintinger, Benedikt Ryu, Jee-Hwan Ott, Christian IRiS Lab. Daejeon34141 Korea Republic of Institute of Robotics and Mechatronics Wessling82234 Germany United States Hapticslabs GmbH Dresden01067 Germany Tu Wien Automation and Control Institute Vienna1040 Austria

ISBN: (纸本)9798331528249

This paper presents the CoaxHaptics-3RRR, a novel concept for haptic interaction devices that is based on a 3-RRR spherical parallel mechanism (SPM). The novelty lies in its mechanical overdetermination through a central hollow shaft, which brings two advantages. First, the device can be built with high rigidity with regard to translational degrees of freedom (DoF). Second, it enables a lighter design with less rotational inertia, as the moving links do not need to withstand translational or gravitational forces. In order to fully exploit these advantages, an optimization process has been conducted that simultaneously optimized for workspace, manipulability, inertia, and structural stiffness. The resulting functional demonstrator provides an unlimited workspace around the shaft axis and +/- 55 degrees in the other two rotational DoF, thus covering a large portion of the human wrist's rotational range. Tests confirm the validity and superiority of the concept over existing devices, making it a promising solution for the category of mechanically overdetermined haptic devices. © 2024 IEEE.

关键词： Haptic interfaces

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Studying of Copying Control System with Nonlinear Measurer 16th

Studying of Copying Control System with Nonlinear Measurer

引用

16th International Conference on Electromechanics and robotics "Zavalishin’s Readings", ERZR 2021

作者： Jatsun, Sergey Malchikov, Andrei Yatsun, Andrey Saveleva, Ekaterina Department of Mechanics Mechatronics and Robotics South-West State University 94 50 Let Oktyabrya Str Kursk305040 Russia

ISBN: (纸本)9789811628139

The article is devoted to the development and mathematical modeling of the exoskeleton suit femoral link copying control system. The material discusses various approaches to ensuring the master and slave links movement synchronization in a human–machine system and minimizing the efforts of interaction between them. To control the force between the links, an original design measurer with nonlinear mechanical properties is used. On the proposed criterion for assessing the regulation quality basis, an analysis of the applicability of various methods for the copying control task is carried out. To compensate the feedback loop nonlinear effects, it is proposed to use a corrective action block, the parameters of which are determined experimentally. As the numerical simulations results shown, the control system with a correcting device usage makes it possible to obtain the exoskeleton suit drive system movement required parameters executive link. Integral error values less than 0.7% of the amplitude for the system studied parameters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Control systems

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Deep Learning-Based Approach for Frost Quantification in Domestic Refrigerators

Deep Learning-Based Approach for Frost Quantification in Dom...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Hassan Akbar Huzzam Hassan Anjum Naeem Malik Tahir Habib Nawaz National Centre of Robotics and Automation Department of Mechatronics Engg. CEME NUST Islamabad Pakistan Department of Mechatronics Engg. CEME NUST Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Frost formation in refrigerators disrupts heat ex-change, lowering system efficiency and causing operational is-sues, making defrosting essential for maintaining performance. However, modern defrost systems rely on sensors for frost detection. The sensory approach is reliable but it could degrade over time in changing environmental conditions. To address this limitation, the paper proposes a real-time frost quantification method using digital image processing and deep learning. A custom Convolutional Neural Network (CNN) model is designed to classify frost thickness in RGB images captured inside a refrigerator. We have used a custom dataset, collected from a real refrigeration environment and made a comparison with other methods previously used for frost thickness estimation. The dataset is collected from several experiments to ensure its diversity such as illumination and environmental changes. This method enables precise frost detection, targeted for optimal defrost control for improved efficiency. Results indicate that the CNN model achieves a high test accuracy of 98.10%. The model's performance surpasses traditional methods, demonstrating its robustness in distinguishing varying frost levels from 0 to 4 (mm). This method enables precise frost thickness estimation and is a suitable choice for defrost control to improve the energy efficiency of the refrigerator.

关键词： Deep learning Accuracy Thresholding (Imaging) System performance Estimation Lighting Sensor systems Sensors Refrigeration Convolutional neural networks

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FinFix: A Soft Gripper With Contact-Reactive Reflex for High-Speed Pick and Place of Fragile Objects

FinFix: A Soft Gripper With Contact-Reactive Reflex for High...

引用

IEEE International Conference on Soft robotics (RoboSoft)

作者： Willem Heeringa Cosimo Della Santina Gerwin Smit BioMechanical Engineering department Delft University of Technology Delft Netherlands Cognitive Robotics department Delft University of Technology Institute of Robotics and Mechatronics Delft Netherlands German Aerospace Center (DLR) Institute of Robotics and Mechatronics Weßling Germany

Industrial automation calls for precise tasks with cycle times reduced to the minimum. At the same time, when handling delicate products such as fruits and vegetables, accelerations must be kept low to keep interaction forces under a certain threshold to avoid damage. This trade-off hinders the penetration of automation in many relevant application fields. This paper investigates using soft technology to solve this challenge. We propose the FinFix gripper, a non-anthropomorphic soft gripper capable of handling delicate objects at high acceleration using a contact-reactive grasping approach. This gripper has two entirely passive sensorized fingers that establish contact and two active fingers that are actuated pneumatically through a rigid mechanism allowing for rapid closure. We provide exhaustive experimental validation by connecting the gripper to a delta robot. The system can reliably execute pick-and-place cycles in $\sim 1 \ \mathrm{s}$ when the distance between the pick and the place locations is 400 mm, resulting in a peak speed of $\sim 10\frac{\mathrm{m}}{\mathrm{s}}$ . None of the fragile objects used during the experiments showed any damage. The only information needed is a rough estimation of the object's position to be grasped and a contact event to trigger the reflex. The test results show that the gripper can hold fragile objects during lateral accelerations of 10g.

关键词： Automation Grasping Soft robotics Robot sensing systems Sensors Reliability Grippers

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Beyond Words: Integrating Personality Traits and Context-Driven Gestures in Human-Robot Interactions 25

Beyond Words: Integrating Personality Traits and Context-Dri...

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Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems

作者： Tahsin Tariq Banna Sejuti Rahman Mohammad Tareq Robotics & Mechatronics Engineering University of Dhaka Dhaka Bangladesh Accounting & Information Systems University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798400714269

As robots become increasingly integrated into human life, personalizing human-robot interactions (HRI) is crucial for improving user acceptance, engagement, and interaction quality. However, personalizing HRI poses a unique challenge due to the diversity of human personality traits. This paper proposes a method that leverages large language models (LLMs) to dynamically tailor robot conversations according to the Big Five (OCEAN) personality traits. Our novelty lies in using user personality traits to shape robots' verbal responses and implementing contextual action generation for gestures. This study addresses two primary research questions: (1) Does adapting robots' verbal responses based on user personality traits improve communication satisfaction? (2) How does the addition of context-appropriate gestures further enhance user satisfaction? We used Goldberg's personality trait measurement scale (1992) to assess 26 participants who engaged in conversations with an LLM-powered Pepper robot on various topics. The quality of these interactions was self-reported using a revised version of Hecht's (1978) conversation satisfaction scale. Three experimental conditions were conducted: (i) Baseline: Standard LLM conversation, (ii) Personality-congruent: LLM-adjusted dialogue based on personality of participants, and (iii) Enhanced interaction: Personality adaptation plus dynamic gestures. For the third condition, we implemented contextually appropriate pre-defined animations and generated novel gestures by computing joint angle values in real time. Statistical analysis using ANOVA revealed significant differences in communication satisfaction across the three conditions (F=13.41, p<.001). Post-hoc analyses using Šidák's multiple comparison test showed significant pairwise differences: Condition 2 vs. 1: Δ Δmean 4.42, p = 0.02; Condition 3 vs. 1: Δ Δmean 8.23, p < 0.01; Condition 3 vs. 2: Δ Δmean 3.80, p = 0.05. These results demonstrate that both personality-congruent inter

关键词： HRI

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Deep Learning-Based Hash Function Cryptanalysis

Deep Learning-Based Hash Function Cryptanalysis

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Ongee Jeong Inkyu Moon Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper analyzes the strength of Message Digest Algorithm (MD5) by performing deep learning-based Encryption Emulation (EE) and Plaintext Recovery (PR) attacks. We convert randomly generated S12-bit arrays, messages, into 128-bit arrays, digests, with MD5 in different numbers of steps. Furthermore, two different structures of deep learning models, fully-connected neural network and Bidirectional Long Short-Term Memory (BiLSTM), are used in attacks and trained to analyze MD5 automatically. As a result, the BiLSTM shows better prediction accuracy than the fully-connected neural network. Moreover, the PR attack is more challenging than the EE attack.

关键词： Deep learning Analytical models Accuracy Neural networks Emulation Bidirectional long short term memory Cryptographic hash function Information and communication technology Encryption Convergence

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Preliminary Work on Compliant Horseshoe Crab Artificial Intelligence Robot (CHAIR)

Preliminary Work on Compliant Horseshoe Crab Artificial Inte...

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IEEE Southeastcon

作者： Kate Johnson Jamie Assaf Rafael Juarez Razvan Voicu Ayse Tekes Robotics and Mechatronics Kennesaw State University Atlanta GA Mechanical Engineering Kennesaw State University Atlanta GA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This paper introduces a compliant robot inspired from horseshoe crab that harnesses the advantages of bendable links to achieve locomotion reminiscent of its natural counterpart. Our robot design features multiple motors for dynamic limb motion and a selectively positioned tail mechanism, which, in future work, will play a pivotal role in swimming. In this initial phase, we focus on establishing fundamental terrestrial locomotion by conducting separate Q-learning experiments for forward movement, left turning, and right turning. Each experiment utilizes gyroscopic feedback to reinforce proper tilt and balance, ensuring stable movement across diverse surface conditions. The success of these individual Q-learning procedures demonstrates how a soft robotic platform can systematically learn essential gait patterns while minimizing disturbances to local ecosystems, owing to the gentle, adaptive interactions afforded by compliant materials. By utilizing bendable links, the robot can conform more closely to uneven terrain, resulting in increased stability and reduced motor strain compared to rigid-body designs. The gyroscope-driven reward structure guides the robot's posture, helping it make timely corrective adjustments for each action. Overall, these results highlight that soft robotics, coupled with reinforcement learning and orientation-based rewards, is a viable approach for developing multi-terrain robots. This work establishes a foundation for more comprehensive action discovery, eventually incorporating additional sensing modalities, continuous tail motions, and further environmental-awareness capabilities to enable fully autonomous amphibious navigation.

关键词： Q-learning Torque Tail Soft robotics Robot sensing systems Motors Turning Gyroscopes Robots Strain

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Development of Ceiling Board Opening Robot for Installing Electric Light and Air Conditioning Equipment

Development of Ceiling Board Opening Robot for Installing El...

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International Workshop on Advanced Motion Control (AMC)

作者： Issa Omura Ryo Oyori Masami Iwase Department of Robotics and Mechatronics School of Science and Technology for Future Life Tokyo Denki University Tokyo JAPAN

The purpose of this research is to develop a robot that automates opening work of ceiling board for installing electric lights and air conditioning equipment. The opening operation requires that the error margin be kept within 2 mm of the drawing's indication. To ensure accuracy, a total station-based robot self-position estimation system is constructed. The cutting accuracy by the robot is ensured by using coordinate transformations to absorb the robot's movement error. Subsequently, each subsystem of the robot is integrated to achieve continuous opening work.

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Privacy-Preserving Classification of Photon-Limited Images Using Deep Learning

Privacy-Preserving Classification of Photon-Limited Images U...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Ongee Jeong Inkyu Moon Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper presents a privacy-preserving image classification scheme based on deep learning and photon counting imaging. The vision transformer-based deep learning model can classify the photon-limited color images, which represents the images sparsely by using a few photons. As a result, over 90 % of photon-limited images can be classified when at least 50,000 photons are used. Since some information from the original images is lost in the photon-limited images, it is challenging to recognize the personal information in the photon-limited images. Therefore, the proposed scheme can classify the images while concealing the portion of the information in the images.

关键词： Deep learning Image recognition Imaging Color Transformers Information and communication technology Photonics Image classification Convergence

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Design of Fast Model Predictive Controller for Permanent Magnet Synchronous Motor Drive System 42

Design of Fast Model Predictive Controller for Permanent Mag...

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42nd Chinese Control Conference, CCC 2023

作者： Guo, Qian Liu, Qinyao Cao, Zhimin Institute of Intelligent Manufacturing and Smart Transportation Suzhou City University Suzhou215104 China School of Advanced Technology Xi'an Jiaotong Liverpool University Department of Mechatronics and Robotics Suzhou215123 China

ISBN: (纸本)9789887581543

In this article, a new predictive controller based on fast model predictive control has been proposed for permanent magnet synchronous motor(PMSM) drive system to solve the quadratic programming problem online in a very short period. The mathematical model of PMSM has been described in terms of state space and discretized by Euler method firstly. Then, the discrete model is linearized at the steady state point. Based on this, the optimization objective function which is used to measure the performance of the PMSM is designed properly. The approximate primal barrier interior point method is employed to speed up the optimization process. Finally, the simulation results declare that the proposed algorithm is useful for PMSM drive system. In addition, the proposed approach enables the model parameters, the horizon size as well as the weighting factors updated in real time so that it has potential to solve problems such as model mismatch in the future. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Quadratic programming

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