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16th International Conference on Quality Control by Artificial Vision

作者： Oka, Shohei Ji, Yonghoon Fujii, Hiromitsu Kono, Hitoshi Graduate School for Advanced Science and Technology Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa Japan Department of Advanced Robotics Chiba Institute of Technology 2-17-1 Thudanuma Narashino Chiba Japan Department of Information and Communication Engineering School of Engineering Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo Japan

ISBN: (数字)9781510667471

ISBN: (纸本)9781510667464

In this study, we propose a new framework to perform visual simultaneous localization and mapping (SLAM) with RGB images artificially generated from thermal images in low light environments where an optical camera cannot be applied. We applied contrastive unpaired translation (CUT) and enhanced generative adversarial network for super-resolution (ESRGAN), which are image translation methods to generate a clear realistic RGB image from a thermal image. Oriented FAST and rotated BRIEF (ORB)-SLAM was performed using the super-resolution fake RGB image to generate a 3D point cloud. Experimental results showed that our thermography-based visual SLAM could generate a 3D temperature distribution map in the low light environment. © 2023 SPIE. All rights reserved.

关键词： Generative adversarial networks

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Framework for monitoring electric vehicle battery second life health and estimating remaining useful life

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Procedia CIRP 2025年 132卷 209-214页

作者： Mohammad Rajabzadeh Vincent Hargaden Pezhman Ghadimi Christoph F. Strnadl Nikolaos Papakostas Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Belfield Dublin Ireland IOTA Foundation 10437 Berlin Germany

The rapid development of electric vehicles has led to the increasingly important role of lithium batteries in the field of performance sources. New technologies make people become more environmentally conscious and switch from buying to controlling electric vehicle life cycles. The mass production of lithium batteries may lead to environmentally friendly transportation. However, traditional lithium battery manufacturers and government policies often focus on the production and recycling phases. The challenges for battery life cycle management and circular economy are increasingly complex and diverse. This paper presents a comprehensive framework for the secondary utilization of lithium batteries by discussing battery aging, monitoring, recycling, secondary use, and lithium battery health prediction. As a result, multiple algorithms were used to evaluate their effectiveness in battery performance prediction. The analysis of the experiments’ results revealed the importance of data pre-processing in improving predictive accuracy, the dependence of model selection on data adaptability, and the need for continuous adjustments during the predictive modeling process.

关键词： Sustainability Lithium batteries Electric vehicles

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Smart window of large transparency-tuning capacity

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Science China(Technological Sciences) 2023年第11期66卷 3107-3114页

作者： WANG YuZhe LI PengCheng OUYANG JianYong ZHU Jian Singapore Institute of Manufacturing Technology(SIMTech) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore Hubei Key Laboratory of Plasma Chemistry and Advanced Materials School of Materials Science and EngineeringWuhan Institute of TechnologyWuhan 430205China School of Science and Engineering Chinese University of Hong Kong at ShenzhenShenzhen 518172China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Windows are one of the least energy-efficient components of a *** allows energy to escape,leading to high cooling demand in summer and energy loss in *** and temperature in the building also affect people's health,comfort,and even *** a result,controlling the heat and light entering the building dynamically is critical for improving the comfort of building occupants and reducing energy *** this work,we develop a tunable smart window based on transparent dielectric elastomer actuators(DEAs)as an alternative solution to sunlight and temperature *** transparency-tuning is achieved by creating wrinkles in a soft elastomer film made of waterborne polyurethane(WPU).The actuation mechanism is based on highly transparent dielectric elastomer actuators that use all solid-state stretchable transparent conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)/(PEDOT:PSS/WPU)as compliant *** modulation range of the smart tunable window with direct viewing is achieved to be 35%to 90%,which is one of the largest among existing tunable *** the low-transparency state,the window can also effectively block the heat and decrease the temperature rise to 2℃over 200 s,while the ambient temperature rises by 6℃with direct *** anticipate that this transparency-tuning mechanism is potentially useful for privacy protection,smart glass,projector screens,displays,and *** heat isolation feature also has the potential to reduce carbon emissions and improve the sustainability of buildings and greenhouses.

关键词： smart window dielectric elastomer transparency tuning transparent electrodes wrinkles

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Development of Perching and Fail-Safe Module for Wall Climbing Multi-copter 10th

Development of Perching and Fail-Safe Module for Wall Cli...

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10th International Conference on Robot Intelligence Technology and Applications, RiTA 2022

作者： Kim, Taewan Tirtawardhana, Christian Yu, Byeongho Nahrendra, I. Made Aswin Kim, Seoktae Myung, Hyun Robotics Program Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of KEPCO RI Korea Electric Power Corporation Daejeon34056 Korea Republic of

ISBN: (纸本)9783031268885

In recent years, various researches using UAVs (Unmanned Aerial Vehicles), especially multi-copter in various tasks, have been conducted due to their high mobility. Various perching mechanisms to perch the multi-copter onto the surface were developed for energy efficiency and to ensure safety while doing its tasks. However, developing mechanisms for multi-copter has limitations, such as limited payload. In this paper, we propose the perching and fail-safe module for the wall-climbing aerial robot "CAROS-Q" which can tilt its pose to perch on tilted surfaces. The proposed module consists of a passive perching mechanism with a specially designed suction cup to perch onto the surface safely, and a fail-safe mechanism to prevent falling from unexpected accidents. The prototype verifies the performance of the proposed module. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy efficiency

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Research on Material and Morphological Structure of Venus Flytrap Trigger Hair

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Journal of Bionic engineering 2021年第5期18卷 1126-1136页

作者： Qian Wang Kun Xu Cheng Fan Lining Sun Lei Zhang Kejun Wang Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow UniversitySuzhou 215021China Key Laboratory for Bionic Engineering(Ministry of Education) Jilin UniversityChangchun 130022China

Venus flytrap can sense the very small insects that touch its tactile receptors,known as trigger hairs,and thus capture prey to maintain its nutrient ***,there are few studies on the trigger hair and its morphological structure and material properties are not fully *** this study,the trigger hair is systematically characterized with the help of diff erent *** show that trigger hair is a special cantilever beam structure and it has a large longitudinal diameter ***,it is composed of a hair lever and a basal podium,and there is a notch near the hair *** crosssection of the trigger hair is approximately a honeycomb structure,which is composed of many *** to measure mechanical properties of trigger hair are introduced in this *** on the mechanical tests,trigger hair proved to be a variable stiff ness structure and shows a high sensitivity to the external *** features can provide supports for the understanding of the high-sensitivity sensing mechanism of trigger hairs from the perspective of structure and material,and off er inspirations for the development of high-performance tactile sensors.

关键词： Venus flytrap Morphological structure Mechanical properties Trigger hair Bioinspiration

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Physics-Informed Deep Learning-Based Monocular Vision and IMU Fusion for Extracting Position and Heading Data of Multirotors

Physics-Informed Deep Learning-Based Monocular Vision and IM...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Arshia Rezaei Erfan Radfar Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs.

关键词： Deep learning Accuracy Measurement units Magnetic sensors Motion estimation Estimation Autonomous aerial vehicles Robot sensing systems Robustness Real-time systems

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Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition:A Review

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Cyborg and Bionic Systems 2021年第1期2021卷 192-205页

作者： Dongfang Xu Qining Wang Robotics Research Group College of EngineeringPeking UniversityChina Beijing Engineering Research Center of Intelligent Rehabilitation Engineering China The Beijing Innovation Center for Engineering Science and Advanced Technology(BIC-ESAT) Peking UniversityChina

The lower-limb robotic prostheses can provide assistance for amputees’daily activities by restoring the biomechanical functions of missing limb(s).To set proper control strategies and develop the corresponding controller for robotic prosthesis,a prosthesis user’s intent must be acquired in time,which is still a major challenge and has attracted intensive *** work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective(locomotion mode recognition,gait event detection,and continuous gait phase estimation)and reviews the state-ofthe-art intent recognition techniques in a lower-limb prosthesis *** current research status,including recognition approach,progress,challenges,and future prospects in the human’s intent recognition,has been *** particular for the recognition approach,the paper analyzes the recent studies and discusses the role of each element in locomotion intent *** work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

关键词： Intent recognition motion

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Robot Manipulation of Dynamic Object with Vision-Based Reinforcement Learning

Robot Manipulation of Dynamic Object with Vision-Based Reinf...

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IEEE International Conference on Control and robotics engineering (ICCRE)

作者： Chenchen Liu Zhengshen Zhang Lei Zhou Zhiyang Liu Marcelo H. Ang Wenfeng Lu Francis EH Tay Advanced Robotics Centre National University of Singapore Singapore Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

With the advanced development of AI, Deep Reinforcement Learning (DRL) is one way to let robots learn how to complete tasks independently. Based on DRL algorithms, robot manipulation in the tabletop scene is regarded as a typical environment for testing and validation. Within this context, we designed a vision-based scene to explore the performance of DRL algorithms in manipulating a dynamic object. Contrary to previous work with simple moving trajectories, the complexity of our approach was increased by applying a randomized vibrating trajectory. To better capture visual cues in motion, we also incorporated a Long Short-term Memory (LSTM) module to process the temporal information between the serial frames. By comparing with setups that only used basic CNN encoders, we found the addition of LSTM can drastically increase the learning efficiency under model-free RL algorithms like SAC. The learning inflection point of applying LSTM appeared at 300k time steps while the others were delayed to 500k time steps or even later, which underscores the necessity of extracting temporal features in dynamic scenes. Furthermore, we observed that by integrating LSTM, there is no need to explicitly append kinematic information, which also supports the idea that temporal information inherently encodes kinematics data.

关键词： Visualization Heuristic algorithms Kinematics Feature extraction Trajectory Data mining Robots

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Hybrid Continuum-Eversion Robot: Precise Navigation and Decontamination in Nuclear Environments using Vine Robot

arXiv

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arXiv 2024年

作者： Al-Dubooni, Mohammed Wong, Cuebong Althoefer, Kaspar The Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science United Kingdom The National Nuclear Laboratory

Soft growing vine robots show great potential for navigation and decontamination tasks in the nuclear industry. This paper introduces a novel hybrid continuum-eversion robot designed to address certain challenges in relation to navigating and operating within pipe networks and enclosed remote vessels. The hybrid robot combines the flexibility of a soft eversion robot with the precision of a continuum robot at its tip, allowing for controlled steering and movement in hard to access and/or complex environments. The design enables the delivery of sensors, liquids, and aerosols to remote areas, supporting remote decontamination activities. This paper outlines the design and construction of the robot and the methods by which it achieves selective steering. We also include a comprehensive review of current related work in eversion robotics, as well as other steering devices and actuators currently under research, which underpin this novel active steering approach. This is followed by an experimental evaluation that demonstrates the robot's real-world capabilities in delivering liquids and aerosols to remote locations. The experiments reveal successful outcomes, with over 95% success in precision spraying tests. The paper concludes by discussing future work alongside limitations in the current design, ultimately showcasing its potential as a solution for remote decontamination operations in the nuclear industry. © 2024, CC BY.

关键词： Nuclear industry

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Control of Continuum Manipulators with Shape Constraints via Deep Reinforcement Learning

Control of Continuum Manipulators with Shape Constraints via...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Nima Maghooli Omid Mahdizadeh Mohammad Bajelani S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Continuum robots, while versatile for handling complex tasks, present significant challenges in control system design. This paper introduces a novel framework that integrates position and orientation control laws to address shape constraints effectively. Specifically, a Deep Reinforcement Learning (DRL) strategy is proposed to facilitate trajectory tracking within the desired orientation. To ensure safety, the framework directly controls the end-effector's position and orientation in the workspace, avoiding unsafe zones. A centralized control law, the Modified Transpose Jacobian (MTJ), is employed to resolve the robot's redundancy without relying on inverse kinematics. The proposed approach is validated through simulations on a Tendon-Driven Continuum Robot (TDCR), demonstrating superior performance compared to similar learning-based controllers.

关键词： Meters Mechatronics Shape Trajectory tracking Termination of employment Position control Kinematics Deep reinforcement learning Safety Continuum robots

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