2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第4页

Active Pedestrian Detection for Excavator Robots based on Multi-Sensor Fusion 基于多传感器融合的挖掘机机器人主动行人检测

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872286

Meiyuan Zou, Jiajie Yu, Bo Lu, Wenzheng Chi, Lining Sun

{"title":"Active Pedestrian Detection for Excavator Robots based on Multi-Sensor Fusion","authors":"Meiyuan Zou, Jiajie Yu, Bo Lu, Wenzheng Chi, Lining Sun","doi":"10.1109/RCAR54675.2022.9872286","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872286","url":null,"abstract":"As a common multi-functional engineering equipment, excavators are widely used in civil construction, coal mining, power engineering, etc. The excellent performance of the excavator not only greatly improves the work efficiency during the construction process, but also effectively saves labor costs. However, due to the complexity of the working environment of the excavator and the blind area of the excavator itself, the driver cannot make timely judgments on the surrounding environment, which may cause potential threats to pedestrians. In response to such problems, this paper proposes a multi-sensor fusion detection method applied to excavators to provide vision assistance for excavator drivers, thereby reducing the risk of pedestrian casualties. Based on the results of the joint calibration, the transformation relationship between the camera and lidar coordinate systems is determined. Combining the detection results of the pedestrian detection algorithm YOLO-v5 and the segmented image information, the position of the pedestrian in the image can be inversely mapped to the 3D point clouds via the matrix transformation, which can accurately display the position of the pedestrian in the point clouds, consequently making up for the lack of depth information in the image. The experimental results show that our method can effectively extract the location information of pedestrians from the complex background environment and realize timely pedestrian alarm.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131462165","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: 3

Improved RRT*-A*-based Three-Dimensional Path Planning Algorithm for the Robotic Dolphin 基于改进RRT*-A*的机器海豚三维路径规划算法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872185

Chi Zhang, Zhenna Liu, Yaoguang Wei, Dong An, Jincun Liu

引用次数: 0

Two-stage Self-supervised MVS Network using Adaptive Depth Sampling 基于自适应深度采样的两阶段自监督MVS网络

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872231

Yangyan Deng, Ding Yuan, Hong Zhang

{"title":"Two-stage Self-supervised MVS Network using Adaptive Depth Sampling","authors":"Yangyan Deng, Ding Yuan, Hong Zhang","doi":"10.1109/RCAR54675.2022.9872231","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872231","url":null,"abstract":"With the development of deep learning, multi-view stereo has achieved significant progress recently. Due to the expensive three-dimension supervision, self-supervised methods have more potential. In this work, a novel two-stage self-supervised learning framework for multi-view stereo is proposed to overcome photometric dependency and the effect of foreshortening. On considering that accurate depth hypothesis always plays an important role in estimating depth information. Therefore, this work concentrates on designing an adaptive depth sampling module based on neighboring spatial patches propagation, which is determined by the normal maps. From this point of view, a two-stage process is carried out in this work. In detail, the coarse initial depth maps and normal maps are obtained in the first stage, and then the network in the second stage refines the depth sampling module by taking the influence of foreshortening into account. Furthermore, the loss functions are developed including feature-metric consistency to overcome the photometric inconsistency caused by lighting variation. Moreover, the consistency between depth maps and normal maps is also employed in the loss functions. To evaluate the effectiveness of our proposed two-stage framework, the experiments are carried out on the DTU datasets. The experimental results demonstrate that our self-supervised learning framework has excellent performance compared to the baseline methods.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602419","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

Decision Making for Autonomous Driving Via Multimodal Transformer and Deep Reinforcement Learning* 基于多模态变压器和深度强化学习的自动驾驶决策*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872180

Wen Fu, Yanjie Li, Zhaohui Ye, Qi Liu

引用次数: 0

A semi-supervised support vector machines approach for condition monitoring of construction equipment 建筑设备状态监测的半监督支持向量机方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872264

Shubo Cao, Shitao Liu, Yunfei Shi, Yubo Pan, Lifang Han, Yiwei Yang

{"title":"A semi-supervised support vector machines approach for condition monitoring of construction equipment","authors":"Shubo Cao, Shitao Liu, Yunfei Shi, Yubo Pan, Lifang Han, Yiwei Yang","doi":"10.1109/RCAR54675.2022.9872264","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872264","url":null,"abstract":"In this paper, a semi-supervised learning-based method for condition monitoring of construction equipment is developed. The method is suitable for vibration datasets collected from mechanical equipment on the construction site, for which class definitions are difficult to obtain. The collected vibration signals are analyzed in the time and frequency domain, respectively. Combining the statistical features of the vibration data and some expert information to obtain the category labels of extremely few data, the Fast Fourier transform (FFT) of the vibration signal is used for feature extraction to increase the ability of the classifier. Finally, the limited labeled samples and a large number of unlabeled samples are used as training sets to establish a condition monitoring model based on semi-supervised support vector machines. The performance of the proposed method is evaluated on the real datasets which collected on three different mechanical devices. The result shows that the correct classification rates of the method is 98.87%, 97.37% and 95.33% respectively, which proves that the proposed method is suitable for the condition monitoring of multiple mechanical equipment.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122346750","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

Design of A Continuum Robot System with Object Detection for the Diagnosis of Vocal Fold Lesions 具有目标检测的连续机器人系统用于声带病变诊断的设计

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872301

Fan Feng, Zefeng Liu, Yongfeng Cao, Le Xie

引用次数: 1

Teleoperation of Dexterous Micro-Nano Hand with Haptic Devices 基于触觉装置的微纳灵巧手遥操作

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872241

Yue Zhao, Xiaoming Liu, Junnan Chen, M. Kojima, Qiang Huang, T. Arai

引用次数: 0

Design of a Movable Rotating Magnetic Field Actuation System for Target Delivery in 3-D Vascular Model 三维血管模型中可移动旋转磁场致动系统的设计

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872287

Yuanhe Chen, Qingsong Xu

引用次数: 1

Pipeline Robot Positioning System Based on Machine Learning 基于机器学习的管道机器人定位系统

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872302

Binglin Li, Qiang Lei, Pai Li, Y. Lian

引用次数: 1

Wireless Ionic sensor on microrobots for Medical Application 医用微型机器人的无线离子传感器

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872232

Jing Zhao, Zhongyi Li, Chunyang Li, Fanqing Zhang

引用次数: 0