检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Biomimetics

作者： Zheng Sun Zhipeng Dong Shixiong Wang Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Hyperspectral imaging has become essential in fields such as waste sorting and agriculture due to its ability to capture detailed spectral information. However, conventional hyperspectral imaging systems typically rely on mechanical movements, such as conveyor belts or linear stages, to scan the target objects, which limits their applicability in environments with static targets or space constraints. To address these challenges, we present PRISM (Polyhedral Reflective Imaging Scanning Mechanism), a novel system that facilitates rapid and high-precision imaging of stationary objects by leveraging a polyhedral reflective structure, thereby eliminating the need for external relative motion. Experimental evaluations demonstrate that PRISM can achieve full spatial-spectral data of 512 by 727 pixels with 448 bands in 2.01 seconds, while maintaining high spectral accuracy, with a reflectance standard deviation of 0.015. We further validate PRISM's effectiveness through textile waste sorting experiments, where PRISM accurately differentiates between various material types at both the pixel and object levels, showcasing its potential for practical applications in industrial settings.

关键词： Reflectivity Accuracy Service robots Cameras Belts Robustness Standards Hyperspectral imaging Sorting Textiles

来源：评论

学校读者我要写书评

暂无评论

Quantitative Progress Evaluation of Post-stroke Patients Using a Novel Bimanual Cable-driven Robot

引用

Journal of Bionic engineering 2021年第6期18卷 1331-1343页

作者： Thiago Alves Rogério Sales Gonçalves Giuseppe Carbone Laboratory of Automation and Robotics School of Mechanical EngineeringFederal University of UberlândiaUberlândia38400-902Brazil Department of Mechanical Energy and Management EngineeringUniversity of Calabria87036RendeItaly

Rehabilitation is the most effective way to reduce motor impairments in post-stroke *** process demands several hours with a specialized *** resources and personnel shortages,the literature reports a high interest in robotic assisted rehabilitation ***,cable-driven robotic architectures are attracting significant research interest for post-stroke ***,the existing cable-driven robots are mostly unilateral devices allowing the rehabilitation only of the most affected *** leaves unaddressed the rehabilitation of bimanual activities,which are predominant within the common Activities of Daily Living(ADL).Thus,this paper presents a specific novel design to achieve bimanual rehabilitation tasks that has been named as BiCAR ***,this paper provides a full insight on the BiCAR system as well as on its dedicated developed software *** particular,BiEval software has been developed as based on a serious game strategy and a virtual reality environment to track the patient exercising duration,motion ranges,speeds,and forces over time for achieving a quantitative assessment of the rehabilitation ***,the paper presents the BiCAR/BiEval capabilities by referring to a pilot Randomized Controlled Trial(RCT).The clinical trials have been used to validate the BiCAR/BiEval in terms of engineering feasibility and user acceptance to achieve an innovative cost-oriented integrated hardware/software device for the bimanual assistive rehabilitation of post-stroke patients.

关键词： Cable-driven robots Low cost Stroke rehabilitation Bimanual Progress tracking Serious games

来源：评论

学校读者我要写书评

暂无评论

Real-Time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes

Real-Time Motion Generation and Data Augmentation for Graspi...

引用

IEEE/SICE International Symposium on System Integration

作者： Kenjiro Yamamoto Hiroshi Ito Hideyuki Ichiwara Hiroki Mori Tetsuya Ogata Robotics Research Department Connective Automation Innovation Center R&D Group Hitachi Ltd. Institute for AI and Robotics Future Robotics Organization Faculty of Science and Engineering Waseda University

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object's position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xu, Zhihui Wei, Tanyong Li, Zongze Huang, Ding Liu, Shoubin Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350325706

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neural Network-Based Gain Scheduled Position Control of a Pneumatic Artificial Muscle

Neural Network-Based Gain Scheduled Position Control of a Pn...

引用

2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Majumder, Arunabha Sarkar, Debadrata Chakraborty, Sagnik Singh, Abhijit Roy, Shibendu Shekhar Arora, Aman Robotics and Automation Group Durgapur713209 India Ghaziabad201002 India National Institute of Technology Durgapur Department of Mechanical Engineering Durgapur713209 India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

The pneumatic artificial muscle (PAM) is considered one of the most preferred actuators in a variety of robotic and industrial applications. However, due to their inherent nonlinearities and hysteretic properties, they are difficult to model and the controller's design becomes more sophisticated. The position control problem of a PAM having different regions of operations at various axial loads is considered in this paper. A neural network-based gain scheduled proportional-integral-derivative (PID-NN) control scheme has been synthesized and compared to the classical linear PID controllers. The PID gains for different operating regions at different loads are determined using Zeigler Nichols sustained oscillation method. These sets of PID gains are then used to determine the neural network (NN) model that schedules them based on the region of operations and axial loads. To validate the efficacy of the proposed control scheme with regards to different step inputs and a sinusoidal input reference tracking performance, experimental studies are conducted, and comparisons have been made with the PID controller. The experimental results for position control confirm the efficacy of the proposed control strategy. © 2022 IEEE.

关键词： Three term control systems

来源：评论

学校读者我要写书评

暂无评论

Crop and Weeds Classification in Aerial Imagery of Sesame Crop Fields Using a Patch-Based Deep Learning Model-Ensembling Method 2

Crop and Weeds Classification in Aerial Imagery of Sesame Cr...

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Moazzam, S. Imran Khan, Umar S. Nawaz, Tahir Qureshi, Waqar S. National University of Sciences and Technology National Centre of Robotics and Automation Department of Mechatronics Engineering Islamabad Pakistan Technological University Dublin Department of Computer Science Dublin Ireland

ISBN: (纸本)9781665498197

Sesame (Sesamum indicum L.) is an important commercial and food crop, and its yields is limited by many insects, pests, diseases, and weeds. Autonomous aerial agrochemicals spray application on sesame fields using a drone aims to save crop from these yield limiting factors and in addition agrochemicals application quantity and site could be controlled, and human health is expected to be protected. For accurate and selective spray application, autonomous systems would need some parameters to distinguish between crop, weed and background. In this research an aerial sesame field dataset has been collected with the focus to classify patch areas of sesame and weeds present in the field. Dataset was captured using Agrocam. We have developed a patch image-based classification approach along with a novel SesameWeedNet convolutional neural network (CNN) inspired by the layer's configuration of VGG networks and depth-wise convolutions of the MobileNet. The small model contains 6 convolutional layers, and it runs faster and accurately on small patch images. Our approach breaks 1920×1080-pixel images into smaller patch images of size 45×45 pixels. After that, these small patch images are fed to a relatively small CNN for training, validation, and finally for classification. Patch based model ensemble and dataset grouping are two major parts in our methodology. Our system recommends the dataset grouping according to vegetation present in the images to enhance classification results. We have achieved accuracy up to 96.7% with our proposed method. We have tested our system under sunlight variation, in wet and dry soil conditions and at different growth stages. To the best of our knowledge, no attempt has been made to classify and treat crop and weeds in sesame fields at the post-emergence stage previously. In this research we have made the contribution of aerial sesame-weed dataset and a complete deep learning-based approach to classify weeds in sesame fields under variable lighti

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Design and Control of High Precision Buoyancy Adjustment System for Small AUVs

Design and Control of High Precision Buoyancy Adjustment Sys...

引用

Computer Science, Electronic Information engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Yongyao Wang Tao Sui Shuxue Yan Cong Chen Zhigang Li School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Marine Robotics Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

This paper addresses the issue of buoyancy variation caused by differences in seawater density at varying operational depths for small AUVs working within 200 meters. To tackle this, a buoyancy adjustment system is designed that uses a piston-type oil cylinder to take in and release oil. The system integrates a digital-to-analog converter with a cable sensor to achieve precise measurement and control of the oil volume by accurately measuring the piston position. A mathematical model of the buoyancy adjustment system is established, and a fuzzy PID control strategy optimized by the Beetle Antennae Search (BAS) algorithm is proposed. Through Simulink simulation analysis, the system's precision adjustment capability and stability are validated. The simulation results demonstrate that the system exhibits good control performance in terms of adjustment time, overshoot, and disturbance rejection, thereby proving its reliability and feasibility.

关键词： Pistons PI control Oils Volume measurement Sea measurements Buoyancy Position measurement Bladder Stability analysis PD control

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Control of Highly Deformable Robotic Endoscopes Using Proprioceptive Shape Feedback

Data-Driven Control of Highly Deformable Robotic Endoscopes ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Yiang Lu Jixuan Zheng Yun-Hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this paper, we introduce a versatile data-driven approach for servo-controlling the highly deformable robotic endoscope equipped with Draw Tower Gratings (DTGs). The advancement of perception and decision-making technologies is pivotal for enabling flexible robots to execute automatic procedures. However, modeling these robots presents a significant challenge due to their large deformations and susceptibility to environmental disturbances. To address this issue, we propose a robust adaptive control framework that leverages DTG-based proprioceptive shape feedback. This automatic paradigm can online estimate the unknown deformation model of the flexible robot based on function approximation (FA), while also accounting for unknown perturbations without prior identification. By employing the proposed FA-based adaptive algorithm, we can ensure the convergence of the shape control error. Experimental results validating a highly deformable flexible robot embedded with DTG sensors showcase the efficiency of the proposed control strategy in unknown scenarios.

关键词： Deformable models Adaptation models Optical fiber sensors Shape control Shape Endoscopes Deformation Perturbation methods Propioception Sensors

来源：评论

学校读者我要写书评

暂无评论

Power Harvesting towards Sustainable Energy Technology through Ambient Vibrations and Capacitive Transducers

Power Harvesting towards Sustainable Energy Technology throu...

引用

2023 International Conference on Emerging Power Technologies, ICEPT 2023

作者： Jamil, Umar Sulaiman, Muhammad Ghafoor, Nouman Malmir, Mostafa Nawaz, Faisal Shakoor, Rana I. University of Texas at San Antonio Department of Electrical Engineering San Antonio United States Air University Department of Electrical Engineering Islamabad Pakistan University of Limerick Department of Science and Engineering Limerick Ireland Ajou University Department of Electrical Engineering Suwon Korea Republic of National Center of Robotics and Automation Air University Mems Sensor Design and Test Lab Islamabad Pakistan

ISBN: (纸本)9798350301014

As part of the fourth industrial revolution, low-power electronic devices require a power harvesting system to provide energy for their operation because energy harvesting technologies are crucial for enabling 90% of wireless sensor networks. While solar and thermal energy harvesting are popular methods, they have limitations such as dependence on sunlight and thermal gradients, which make them unreliable for low-wattage electronic equipment and wireless sensing networks. Power harvesting from ambient vibrations can lead to a sustainable system. Currently, there is a focus on different power harvesting principles such as electromagnetic, piezoelectric, and electrostatic. This research work presents a novel power harvester design that uses three in-plane capacitive transducers - gap-closing, overlap-varying, and pattern-varying - to operate under ambient vibrations as input energy. With low levels of input vibrations, the device moves, leading to changes in capacitance and the harvesting of reliable electrical power. The design analysis includes capacitance, frequency, and electromechanical evaluations to acquire capacitance matrices, natural frequencies for different modes, displacement covered by moving mass, charge distribution, and pressure distribution. © 2023 IEEE.

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

A Robotic Skin Using Low-density FBGs for Human-robot Interaction

A Robotic Skin Using Low-density FBGs for Human-robot Intera...

引用

IEEE International Conference on robotics and Biomimetics

作者： Xiyue Dong Chin-Kiu Lam Wei Chen Bohan Yang Yun-Hui Liu Hong Kong Center for Logistics Robotics Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In collaborative robotic arms, tactile sensing is crucial for enhancing interaction accuracy and safety. Robotic skins play an vital role in tactile sensing by mimicking the human sense of touch, enabling robots to detect and respond to physical interactions. Fiber Bragg gratings (FBGs), with their high sensitivity, environmental adaptability, and real-time response are increasingly adopted in the design of robotic skin systems. However, current FBG-based research has not resolved the issue of the high-density FBG usage and the large number of optical fibers required, limiting their widespread application in practical scenarios. In this work, we present a design of a soft robotic skin with efficient use of FBGs to estimate the location and magnitude of external forces. Our prototype features a cylindrical shape with a total area of 720 cm 2 and incorporates one optical fiber with 16 evenly spaced FBGs. We optimized the structure through finite element analysis (FEA) and designed experiments to characterize its properties. Using data-driven methods, we achieved root mean square errors (RMSEs) of 4.29 mm and 4.05 degrees in estimating locations along the axial and radial directions, respectively, and 0.17 $N$ for force magnitude. By achieving high sensitivity and an effective area per unit of 50 cm 2 , this approach shows promise for future applications in robotic skin.

关键词： Sensitivity Shape Force Prototypes Soft robotics Robot sensing systems Fiber gratings Skin Sensors Root mean square

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：