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arXiv 2024年

作者： Lee, Joong-Ku Kim, DongHyeon Park, Seong-Su Lee, Jiye Ryu, Jee-Hwan Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

This paper presents a bimanual haptic display based on collaborative robot arms. We address the limitations of existing robot arm-based haptic displays by optimizing the setup configuration and implementing inertia/friction compensation techniques. The optimized setup configuration maximizes workspace coverage, dexterity, and haptic feedback capability while ensuring collision safety. Inertia/friction compensation significantly improve transparency and reduce user fatigue, leading to a more seamless and transparent interaction. The effectiveness of our system is demonstrated in various applications, including bimanual bilateral teleoperation in both real and simulated environments. This research contributes to the advancement of haptic technology by presenting a practical and effective solution for creating high-performance bimanual haptic displays using collaborative robot arms. © 2024, CC BY.

关键词： Collaborative robots

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Frequency-Domain Modeling-Free Iterative Learning Control for Point-To-Point Motion

Frequency-Domain Modeling-Free Iterative Learning Control fo...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yoshihiro Maeda Makoto Iwasaki Department of Engineering Electrical and Mechanical Engineering Program Nagoya Institute of Technology Nagoya Japan

The data-driven autonomous feedforward (FF) control design technique known as frequency-domain modelling-free iterative learning control (MFILC) has gained attention for its ability to achieve fine motion control performance without excessive labor. However, the existing frequency-domain MFILC methods that rely on empirical transfer function estimation are not suitable for point-to-point (PTP) motion due to the leakage error that occurs in frequency response function (FRF) estimation. To address this issue, this study proposes an improved frequency-domain MFILC method that employs the differential filtering-based empirical transfer function estimation for FRF estimation. This enhancement enables the proposed method to learn the FF compensation in both reciprocating and PTP motion. Simulations were conducted to evaluate the effectiveness of the proposed method in achieving fast and precise motion control of a galvano scanner.

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Deep Transfer Learning-Based Demagnetization Analysis for Linear Oscillating Actuator Considering Circumferential Segmented Structure

Deep Transfer Learning-Based Demagnetization Analysis for Li...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Lee, Ji-Hyeon Park, Soo-Hwan Park, Du-Ha Jeong, Jae-Hoon Lim, Myung-Seop Seoul Korea Republic of Dongguk University Department of Mechanical Robotics and Energy Engineering Seoul Korea Republic of Lg Electronics H&a Fundamental Technology R&d Lab Seoul Korea Republic of

ISBN: (纸本)9798350314274

The linear oscillating actuator (LOA) achieves high efficiency and features a simple mechanical structure because it doesn't require the conversion of rotational motion into linear motion. Therefore, the LOA is an appealing option for devices such as compressors, linear pump and automobile active suspension due to its high efficiency and power density. The stability of permanent magnets (PMs) can be impacted by different factors such as temperature, electromagnetic fields, and other external influences. In more severe cases, these factors can result in the occurrence of irreversible demagnetization, causing permanent damage to the magnetic properties of the PM. The irreversible demagnetization of permanent magnets impacts the electromagnetic functionality of the LOA, making it necessary to account for it during the design stage. However, the intricate configuration of the LOA such as divided outer stator and PMs diminishes accuracy of the 2-D finite element analysis (FEA). Despite its high computational cost for calculating accurate demagnetization ratio (DR), 3-D FEA is essential. Thus, we propose a demagnetization analysis based on transfer learning (TL) to reduce the computational cost associated with accurately calculating the 3-D FEA-based DR. This approach takes into account the permeance in the stator core and circumferential leakage flux. With TL, the parameters of pre-trained models learned from a source dataset in different but similar domains are transferred to learn the target dataset and effectively enhance the performance of neural network. The TL is conducted with a substantial dataset from 2-D FEA-based demagnetization ratio (DR) anda limited dataset from 3-D FEA-based DR. TL is a cognitive learning approach that utilizes knowledge acquired from a source task to enhance learning in a related but different target task were compared. © 2023 IEEE.

关键词： Finite element method

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TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks

TartanDrive 2.0: More Modalities and Better Infrastructure t...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Matthew Sivaprakasam Parv Maheshwari Mateo Guaman Castro Samuel Triest Micah Nye Steve Willits Andrew Saba Wenshan Wang Sebastian Scherer Robotics Institute Carnegie Mellon University Department of Mathematics Indian Institute of Technology Kharagpur Department of Mechanical Engineering University of Pittsburgh

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present TartanDrive 2.0, a large-scale off-road driving dataset for self-supervised learning tasks. In 2021 we released TartanDrive 1.0, which is one of the largest datasets for off-road terrain. As a follow-up to our original dataset, we collected seven hours of data at speeds of up to 15m/s with the addition of three new LiDAR sensors alongside the original camera, inertial, GPS, and proprioceptive sensors. We also release the tools we use for collecting, processing, and querying the data, including our metadata system designed to further the utility of our data. Custom infrastructure allows end users to reconfigure the data to cater to their own platforms. These tools and infrastructure alongside the dataset are useful for a variety of tasks in the field of off-road autonomy and, by releasing them, we encourage collaborative data aggregation. These resources lower the barrier to entry to utilizing large-scale datasets, thereby helping facilitate the advancement of robotics in areas such as self-supervised learning, multi-modal perception, inverse reinforcement learning, and representation learning. The dataset is available at https://***/TartanDrive2.

关键词： Representation learning Laser radar Propioception Self-supervised learning Reinforcement learning Metadata Sensors

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Recent Advances in Artificial Intelligence-Driven Prognostics and Health Management of Mobility Batteries

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Multiscale Science and engineering 2025年 1-16页

作者： Kumar, Prashant Kim, Heung Soo Kim, Sungjun Kim, Hyun-Seok Moon, Janghyuk Department of AI and Big Data Woosong University Daejeon Republic of Korea Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul Republic of Korea Division of Electronics and Electrical Engineering Dongguk University Seoul Republic of Korea Department of Energy Systems Engineering Chung-Ang University Seoul Republic of Korea

The rise in demand for sustainable transportation solutions in recent years has resulted in Electric vehicles (EVs) attracting substantial interest. The rapid rise in EV adoption has led to the need for efficient prognostics and health management (PHM) solutions. To ensure reliable and safe operation, the batteries that power EVs require maintenance. This paper reviews the detailed artificial intelligence (AI)-driven PHM framework for mobility batteries. Different mobility batteries have different chemistries owing to the different battery materials used for their development. AI-driven PHM uses machine learning, deep learning, and data-driven techniques to accurately estimate key battery parameters that include state of health and remaining useful life. We review data collection strategies, data processing approaches, feature development, and battery health assessment techniques based on AI, reviewing various AI methodologies that include artificial neural networks, Gaussian process regression, convolutional neural networks, and many more. This inclusive review of the recent trends and methodologies of AI-based mobility battery PHM provides a framework to develop a future efficient PHM solution for safer, efficient, and reliable battery systems.

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Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion

Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile L...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuo Yang Zixin Zhang Zhengyu Fu Zachary Manchester Department of Mechanical Engineering Robotics Institute Carnegie Mellon University Pittsburgh PA USA

We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution for legged robots, called Cerberus, which precisely estimates position on various terrains in real-time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we perform online kinematic parameter calibration and outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that online calibration of kinematic parameters can reduce estimation drift to less than 1% during long-distance, high-speed locomotion. Our drift results are better than those of any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot experiences large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, is available at https://***/ShuoYangrobotics/Cerberus.

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Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy

arXiv

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arXiv 2023年

作者： Hu, Xiaohai Venkatesh, Aparajit Wan, Yusen Zheng, Guiliang Jawale, Neel Kaur, Navneet Chen, Xu Birkmeyer, Paul Department of Mechanical Engineering University of Washington United States Amazon Robotics United States

Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Simulation and Dynamic System Modeling in an Elastically Supported Rigid Cylinder for Vibration Energy Harvesting 1

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5th International Conference on mechanical engineering, ICOME 2021

作者： Subekti Guntur, Harus Laksana Djanali, Vivien S. Syaifudin, Achmad Graduate Program of Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Mechanical Engineering Institut Teknologi Sepuluh Nopember Kampus Keputih-Sukolilo Jl. Arif Rahman Hakim Surabaya60111 Indonesia Mechanical Engineering Department Universitas Mercu Buana West Jakarta Indonesia

ISBN: (数字)9789811915819

ISBN: (纸本)9789811915802

This paper discusses the dynamic system modeling in an elastically supported rigid cylinder for electrodynamic vibration energy harvesting. In this paper, the fluid flow used is very different from the research that has been done, the fluid that will be used is air. A lot of airflows is generated in tall buildings, especially in the ducting flow. By utilizing airflow, home energy is developed. The harnessed energy used is electrodynamic vibration energy harvesting which arises from the tension of the wire and beam that vibrates due to the turbulence that is formed which produces an external force, causing the cylinder to insulate. The simulation results show that the airflow velocity and diameter greatly affect the amount of power generated. At an airflow rate of 8 m/s, the power generated is 2 × 10−17 watts. At a diameter of 80 m, the power generated is less stable. The phenomenon that occurs due to the placement of electrodynamic vibration energy harvesting, placement at point B has a faster power than the placement at point A. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy harvesting

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Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance 19

Underwater Intention Recognition using Head Motion and Throa...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Guo, Yuqin Zhang, Rongzheng Qiu, Wanghongjie Asada, Harry Wan, Fang Song, Chaoyang Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Guangdong Shenzhen518055 China Southern University of Science and Technology Department of Mechanical and Energy Engineering Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA02139 United States SUSTech Institute of Robotics Southern University of Science and Technology Guangdong Shenzhen518055 China

ISBN: (纸本)9798350320695

This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support. © 2023 IEEE.

关键词： Human robot interaction

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Role of Anisotropic Diffusion and Strains in the Chemo-mechanical Response of Single-Crystalline Ni-Rich Cathode Materials in Lithium-Ion Batteries

SSRN

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SSRN 2023年

作者： Iqbal, Noman Lee, Seungjun Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of

In the context of lithium-ion battery technology, LiNixMnyCozO2 (x ≥ 0.8) (Ni-rich NMC) is considered a promising cathode material for next-generation batteries due to its high energy density. However, rapid capacity fade caused by mechanical failure hinders commercialization in the battery market. Anisotropic deformation in the basal and normal directions is the main source of mechanical damage to Ni-high NMC. In this study, we developed a fully coupled chemo-mechanical model that rigorously accounts for the anisotropic features of anisotropic diffusivity, anisotropic stress-induced diffusion and anisotropic mechanical deformation. Using the model, we investigate the effects of anisotropic and concentration-dependent lattice strains on the chemo-mechanical behavior of a single-crystalline Ni-rich NMC particle through a comparative study with the cases of a fully isotropic model, constant properties and an isotropic diffusion approximation. Numerical simulations reveal that due to the anisotropic features, Ni-high NMC materials generate larger concentration gradients and associated higher stresses than isotropic materials, thereby enhancing the possibility of mechanical fractures. This study provides fundamental insight into the role of the anisotropic chemo-mechanical behavior of Ni-rich cathode materials, which will help in the better design of robust, high-capacity batteries. © 2023, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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