2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)最新文献_第9页

Compliant Peg-in-Hole Assembly for Components with Grooves Based on Attractive Region in Environment 环境中基于吸引域的带凹槽部件的柔性孔内销装配

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536140

Yang Liu, Ziyu Chen, Xiaodong Zhang, Jie Gao

引用次数: 4

Development of A Novel Dual-arm Robot via Modular Actuator 基于模块化驱动器的新型双臂机器人的研制

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536159

Weijun Wang, Jiangtao Hu, Xiaofeng Yang, Tian Xie, Chaoyang Ma, Wenjie Li

引用次数: 1

An Improved Vibration Controller for Precision Manufacture Based on Youla Parametrization and Fuzzy Logic 基于优拉参数化和模糊逻辑的精密制造振动改进控制器

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536176

Qimin Li, L. Ke, Huayan Pu, Jin Yi, Jie Ma, Ruqing Bai, Jinglei Zhao, Shilong Wang, Jun Luo, Tao Zhu

引用次数: 1

Design and Experimental Evaluation of a Non-anthropomorphic Passive Load-carrying Exoskeleton 非拟人化被动负重外骨骼的设计与实验评价

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536203

Zhijie Zhou, Wenbin Chen, Hao Fu, Xiang Fang, C. Xiong

{"title":"Design and Experimental Evaluation of a Non-anthropomorphic Passive Load-carrying Exoskeleton","authors":"Zhijie Zhou, Wenbin Chen, Hao Fu, Xiang Fang, C. Xiong","doi":"10.1109/ICARM52023.2021.9536203","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536203","url":null,"abstract":"Soldiers are often required to carry heavy loads during long distance march. Such load carriage can easily induce joint injuries and foot blisters, which further reduce the wearer’s task performance. To assist human walking with load carriage, various types of robotic devices are proposed, such as powered exoskeletons, supernumerary robotic limbs and suspended backpacks. However, these devices have individual shortcomings. This paper proposes a non-anthropomorphic passive load-carrying exoskeleton, which can dynamically support the carried load during the walking rhythm via a passive legged structure. This exoskeleton can reduce the load borne by human without energy input. The simple and passive structure design brings the highest robustness and flexibility. The simulation based on the mathematic model shows that the exoskeleton can reduce the foot pressure of the users. Such analysis results are also verified by the walking experiment. The experiment results show that the exoskeleton can transfer on average 68.0% of the load to the ground while standing, and 24.6% of the load while walking. The maximum load is reduced by 22.1% during walking.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Multiple Signals Fusing Framework for Tool Condition Monitoring Based on Deep Learning 基于深度学习的刀具状态监测多信号融合框架

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536086

Yufeng Li, Xingquan Wang, Yan He, Fei Ren, Yuling Wang

{"title":"A Multiple Signals Fusing Framework for Tool Condition Monitoring Based on Deep Learning","authors":"Yufeng Li, Xingquan Wang, Yan He, Fei Ren, Yuling Wang","doi":"10.1109/ICARM52023.2021.9536086","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536086","url":null,"abstract":"Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is vital to maintain the quality of tool and workpiece during machining process. Many studies for tool condition monitoring via monitoring signals based deep learning have been conducted. Each signal has a different sensitivity to a different status of tool wear. It is a key problem that how to combine the advantages of various signals and fuse the sensor signals to improve the accuracy of monitoring. This paper proposes a multiple signals fusing framework(MSFF) for tool condition monitoring based on deep learning. The monitoring signals in machining processes, including force signal, vibration signal, and acoustic emission signal, are collected and analyzed. Then, features related to tool wear on the collected signals are extracted based on deep learning and realize the mapping between the extracted features and tool condition through linear regression. The advantages and the disadvantages of different signal selection schemes based on deep learning are compared and analyzed. The experimental results show that the performance of the proposed MSFF is superior compared to other schemes for tool condition monitoring.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128315581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Event-based Object Detection with Lightweight Spatial Attention Mechanism 基于轻量级空间注意机制的事件目标检测

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536146

Zichen Liang, Guang Chen, Zhijun Li, Peigen Liu, Alois Knoll

引用次数: 5

Multi-objective Function Optimization of 2-RPU&2-SPS Parallel Mechanism Based on Adams 基于Adams的2-RPU&2-SPS并联机构多目标函数优化

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536212

Chongshan Wang, Bin Li, Jiaqi Zhu, Qi Li, Yuan Zhang

引用次数: 0

Real-time Monocular 3D People Localization and Tracking on Embedded System 嵌入式系统的实时单目三维人物定位与跟踪

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536118

Yipeng Zhu, Tao Wang, Shiqiang Zhu

{"title":"Real-time Monocular 3D People Localization and Tracking on Embedded System","authors":"Yipeng Zhu, Tao Wang, Shiqiang Zhu","doi":"10.1109/ICARM52023.2021.9536118","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536118","url":null,"abstract":"Localizing people in 3D space, rather than in original 2D image plane, provides a more comprehensive understanding of the scene and brings up more potential applications. However, inferring 3D locations usually requires stereo camera or additional sensors since deriving depth information from single image is regarded as an ill-posed problem. With recent progress in deep learning methods, depth estimation neural network can provide convincing depth map by a single RGB image. This work develops a people localization and tracking method based on a monocular camera. Specifically, an efficient self-supervised monocular depth estimation method is adopted to generate pseudo depth map. Afterwards, 2D object detection results are adopted for finding accurate people location. Finally, a filter based tracking method is adopted to fuse temporal information and improve the accuracy. Aiming to provide a real time solution for people tracking on embedded system, our methods are deployed and tested on a NVIDIA Jetson Xavier NX develop kit. The proposed efficient localization and tracking method is validated by a group of field tests. The overall performance reaches 12 fps with an acceptable accuracy compared to ground truth.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125614278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic Sign Detection using Feature Fusion and Contextual Information 基于特征融合和上下文信息的交通标志检测

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536126

Haitao Wang, Guang Chen, Zhijun Li, Zhengfa Liu

{"title":"Traffic Sign Detection using Feature Fusion and Contextual Information","authors":"Haitao Wang, Guang Chen, Zhijun Li, Zhengfa Liu","doi":"10.1109/ICARM52023.2021.9536126","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536126","url":null,"abstract":"Traffic sign detection based on image and video data is critical, which captures real-time traffic road information for autonomous vehicle. With the rapid development of CNN, more and more CNN-based detectors have promoted general object detection. However, these mainstream detectors still suffer from small object detection task because of small size and fuzzy representation. Traffic signs are representative small object on road scenes causing a rigid challenge for autonomous driving perception system. In this paper, traffic sign detection (TSD) is regard as a small object detection task. We propose a feature fusion method via cross-connection to enhance feature representation. In addition, contextual information searched by dilated convolution is also used to support small traffic sign detection. We have implemented our modules into Faster R-CNN and evaluated effectiveness of proposed method on Tsinghua-Tencent 100K dataset. Our experimental results prove that the feature fusion method via cross connection and contextual information improve detection result of small traffic sign.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134054200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Skeleton-based Human Activity Analysis Using Deep Neural Networks with Adaptive Representation Transformation 基于骨骼的自适应表示变换的深度神经网络人体活动分析

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) Pub Date : 2021-07-03 DOI: 10.1109/ICARM52023.2021.9536067

Jiahui Yu, Hongwei Gao, Qing Gao, Dalin Zhou, Zhaojie Ju

{"title":"Skeleton-based Human Activity Analysis Using Deep Neural Networks with Adaptive Representation Transformation","authors":"Jiahui Yu, Hongwei Gao, Qing Gao, Dalin Zhou, Zhaojie Ju","doi":"10.1109/ICARM52023.2021.9536067","DOIUrl":"https://doi.org/10.1109/ICARM52023.2021.9536067","url":null,"abstract":"Compared with RGB-D-based human action analysis, skeleton-based works reach higher robustness and better performance, which are widely applied in the real world. However, the diversity of action observation perspectives hinders the improvement of recognition accuracy. Most of the existing works solve this problem by increasing the amount of training data, which brings a huge computational cost and cannot improve the robustness of the models. This paper proposes an adaptive model to obtain high-performance representations to improve human action recognition accuracy. First, a skeleton representation transfer scheme is proposed to transform the input skeleton-based body model to the best perspective, in which all parameters can be adaptively learned. This is more robust and cost-effective than hand-crafted features. Next, a re-designed backbone is proposed to train the model with a small computational cost based on the 3D-CNN. In the training process, a data enhancement method is also introduced to enhance robustness. Finally, extensive experimental evaluations are conducted on two benchmarks. The results show that this deep model can effectively and adaptively obtain high-performance skeleton representation and its performance is better than other state-of-the-art methods.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130753771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1