2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第10页

Construction and evaluation of SLAM and navigation system for mobile robot based on diverse environments 基于不同环境的移动机器人SLAM与导航系统构建与评价

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517330

Haodong Wang, W. Guo, Yu-Chen Fu

引用次数: 0

A Preliminary Study on an IMU-Based Surgery Assisting Method for Minimally Invasive Puncture of Intracranial Hematoma 基于imu的颅内血肿微创穿刺手术辅助方法的初步研究

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517500

Tianqi Shao, Ye Wang, Peng Jiang, Chong Wu, Guanglin Li, Lin Wang, Shiwei Du

{"title":"A Preliminary Study on an IMU-Based Surgery Assisting Method for Minimally Invasive Puncture of Intracranial Hematoma","authors":"Tianqi Shao, Ye Wang, Peng Jiang, Chong Wu, Guanglin Li, Lin Wang, Shiwei Du","doi":"10.1109/RCAR52367.2021.9517500","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517500","url":null,"abstract":"A navigation always plays a vital role during the minimally invasive puncture and drainage (MIPD) of intracranial hematoma (ICH). However, providing accurate navigation is very expensive and complex. This paper presents a low-cost method that supplies efficient and precise navigation information during operation. This method proposes a sensor fusion system with two Initial Measurement Units (IMU) and Computed Tomography (CT) to estimate the attitude (orientation) of puncture path and surgical tools in real-time. Here, the IMUs play the role of attitude (orientation) tracking. The one is the wearable IMU on the patient, tracking the patient's intracranial puncture path in real-time. The second is the IMU on the surgery needle, tracking the needle's orientation. The surgeon has the power to control the needle's orientation to match the puncture path's orientation to complete the puncture operation. The preliminary study was conducted to estimate the error of the two IMUs' orientation by calibrating them in the same coordinate system to explore this application's feasibility in surgery. The experimental results showed no significant difference between the two IMU measurements, and the mean error was within 1°. It suggested that two IMUs can work in the same coordinate system with high consistency. In the future, more experiment will be carried out for developing the method.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129973613","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

Effects of Backlash on the Walking Stability of Biped Robots 间隙对双足机器人行走稳定性的影响

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517629

Yisen Hu, Xin Wu, Hongyu Ding, Jingchen Li, Wenguang Wang, Jianxin Pang

引用次数: 1

Terrain Recognition and Gait Cycle Prediction Using IMU 基于IMU的地形识别和步态周期预测

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517670

Zhuo Wang, Yu Zhang, Jiangpeng Ni, Xin Wu, Yida Liu, Xin Ye, Chunjie Chen

{"title":"Terrain Recognition and Gait Cycle Prediction Using IMU","authors":"Zhuo Wang, Yu Zhang, Jiangpeng Ni, Xin Wu, Yida Liu, Xin Ye, Chunjie Chen","doi":"10.1109/RCAR52367.2021.9517670","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517670","url":null,"abstract":"It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132694690","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: 4

Zener Model based Impedance Control for High-Precision Force Tracking of Robot-Environment Interaction 基于齐纳模型的机器人-环境交互高精度力跟踪阻抗控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517078

Xi Wu, Panfeng Huang, Zhengxiong Liu, Zhiqiang Ma

引用次数: 0

Gun model recognition using geometric features of contour image 利用轮廓图像的几何特征识别火炮模型

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517482

Zhisheng Zhou, Jun Han, Jiaxin Chen, Yuming Dong

{"title":"Gun model recognition using geometric features of contour image","authors":"Zhisheng Zhou, Jun Han, Jiaxin Chen, Yuming Dong","doi":"10.1109/RCAR52367.2021.9517482","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517482","url":null,"abstract":"Global boarder customs seize large numbers of illegally smuggled guns annually, including large kinds of lethal aerodynamic guns. To classify the guns' types and recognize the models is beneficial and essential for the investigation of smuggling cases. Therefore, an automatic gun model recognition system with high efficiency is very important. In this work, we investigate the possibility that identifying a gun's model by its contour image. The procedure mainly involves acquiring a contour image using back-illuminated imaging and classifying the contour region based on geometric features. Four geometric features including area, circumference, maximum distance and Hu moment in-variants are extracted from the contour region. A combination of the above features is adopted for the gun's model recognition based on the Normalized Manhattan Distance rule. 79 water bomb guns that of 20 different models are used in the verifying experiment and 950 images are taken to form a dataset. Experimental results indicate that the method is able to classify and recognize the gun's model with a remarkably high accuracy of larger than 99%, which suggests that the contour differences between different models of guns can be detected by image classification. We expect this work will promote further studies on automatic gun model recognition and find potential applications in smuggling arms investigations.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114908849","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

A cooperative positioning approach of Unmanned Aerial Vehicles with Accuracy and Robustness 一种具有精度和鲁棒性的无人机协同定位方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517679

Ziwei Zhou, Ziying Lin, Yi Ni, Wei Dong, Xiangyang Zhu

{"title":"A cooperative positioning approach of Unmanned Aerial Vehicles with Accuracy and Robustness","authors":"Ziwei Zhou, Ziying Lin, Yi Ni, Wei Dong, Xiangyang Zhu","doi":"10.1109/RCAR52367.2021.9517679","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517679","url":null,"abstract":"Efficient localization is crucial for the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments. Current onboard visual positioning approach may easily deteriorate with varying illumination and unpredicted obstacles. To tackle this issue, a novel cooperative positioning approach is proposed in this paper. This approach is comprised of a marker-based position estimation and an end-to-end UAV detection. For the marker-based method, a prior known pattern is attached on UAV and the Perspective-n-point (PnP)algorithm is implemented to provide an accurate estimation of the UAV's position. Meanwhile, to enhance the localization robustness, an end-to-end detection method is designed based on FCOS neural network and ultra-wideband (UWB) range measurement. Then, with an additional data fusion, the aforementioned two methods are integrated eventually. In this way, the accuracy and robustness of the localization can be enhanced accordingly. Extensive experiments under different environments are carried out to verify the effectiveness of the proposed method. Results demonstrate that the proposed method can estimate the flight trajectory of UAV with high accuracy and robustness in cluttered environments with varying illumination.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115022796","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

Multi-chamber Pneumatic Actuator for Peristaltic Soft Robot 蠕动式软机器人的多腔气动执行器

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517382

Sheng Gao, Yue Wang, R. Xiong, Zhefeng Gong, Nenggan Zheng, Haojian Lu

引用次数: 0

The Design of an Aerial/Ground Dual-modal Mobile Robot for Exploring Complex Environments 一种探索复杂环境的空中/地面双模移动机器人设计

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517607

Daoxun Zhang, Ce Guo, Haoran Ren, Pengming Zhu, Ming Xu, Huimin Lu

引用次数: 3

Research on Autonomous Movement of Nursing Wheelchair Based on Multi-sensor Fusion 基于多传感器融合的护理轮椅自主运动研究

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517453

Yingbing Su, Huarui Zhu, Zhou Zhou, Bingshan Hu, Hongliu Yu, Renren Bao

引用次数: 0