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

Workload-adaptive Vertical Hopping of A Single-legged Robot using Model-based Optimization 基于模型优化的单足机器人工作量自适应垂直跳跃

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517355

Yongming Yue, Yu Zhang, Wei Gao, Shiwu Zhang

{"title":"Workload-adaptive Vertical Hopping of A Single-legged Robot using Model-based Optimization","authors":"Yongming Yue, Yu Zhang, Wei Gao, Shiwu Zhang","doi":"10.1109/RCAR52367.2021.9517355","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517355","url":null,"abstract":"Frequent workload variation is inevitable when legged robots are deployed for human service tasks like package delivery. The varying workloads can be detrimental to the robustness of robots' locomotion and may even cause task failures if no adjustment of the leg controller is applied. To maintain consistent locomotion performance, this paper explores fast gait generation given system workloads using Spring-Loaded Inverted Pendulum based models and advanced Nonlinear Programming tools. Through solving the properly posed optimization problem, the optimal control trajectory can be generated within seconds to help a vertical hopper maintain the desired apex heights, with an error within ±4% under the tested workloads from 48% up to 190% of the robot's weight. The differences among the optimal control trajectories under different workloads for achieving the same or different apex heights necessitates controller adjustment to ensure workload-adaptive locomotion. Additionally, the leg's proprioceptive-only sensing of the ground reaction force during the stance phase reveals a promising way to quantify workload variation, which can then facilitate to close the control loop of workload-adaptive locomotion in the near future.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"47 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":"115313267","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

A Predictive Control for Electromagnetic Scanning Micro-Mirrors 电磁扫描微镜的预测控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517531

Peijun Zheng, Ruili Dong, Yonghong Tan, Huiyu Wang

引用次数: 1

Estimating Pose of Object and Manipulator Grasping Control 物体姿态估计与机械手抓取控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517600

Dong Wang, Dong Yang, Qinghui Pan, Chaochao Qiu, Y. Dong, Jie Lian

{"title":"Estimating Pose of Object and Manipulator Grasping Control","authors":"Dong Wang, Dong Yang, Qinghui Pan, Chaochao Qiu, Y. Dong, Jie Lian","doi":"10.1109/RCAR52367.2021.9517600","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517600","url":null,"abstract":"In this paper, we mainly study the pose estimation based on feature matching and manipulator grasping control. Obtaining the pose information of the object is an important part of autonomous grasp of the manipulator. In order to obtain the precise pose information of the object, an improved algorithm is proposed based on the GMS (Grid-Based Motion Statistics) algorithm. Firstly, we use the RANSAC algorithm to remove the point pairs whose the distance error is more than 1.5 pixels after transformation. Secondly, the Euclidean distance of the coefficients is calculated between original image and object image. Some point pairs with a larger distance are removed because of the affine invariant principle. The correct correspondences are transformed from 2D pixel coordinate frame to 3D camera coordinate frame with depth image. The least square method combined with SVD algorithm is used to solve the rotation and translation matrices of the object relative to the camera coordinate frame. These matrices are used to estimate the pose of the object. The high accuracy of feature matching in the improved GMS algorithm is verified. The estimated error of the position $(x, y, z)^{T}$ is within ±2.4mm, and the orientation $(text{Roll}, text{Pitch}, text{Yaw})^{T}$ is within ±1°. Finally, the performance of the algorithm is verified through the grasping experiments with the manipulator.","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":"124395683","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 Modular Rehabilitation Lower Limb Exoskeleton for Stroke Patients With Hemiplegia 一种用于脑卒中偏瘫患者的模块化康复下肢外骨骼

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517612

Pengbo Li, Wenhao Wei, Ruoyu Bao, Bailin He, Zhilong Su, Can Wang, Xinyu Wu

引用次数: 1

Kinematics Modeling and Trajectory Planning for the Skull Reconstruction Robot 颅骨重建机器人运动学建模与轨迹规划

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517514

Weiqun Wang, Xiruo. Wang, Z. Hou, Zhijie Fang, Yuze Jiao, Yangyu Luo, Jian Gong

{"title":"Kinematics Modeling and Trajectory Planning for the Skull Reconstruction Robot","authors":"Weiqun Wang, Xiruo. Wang, Z. Hou, Zhijie Fang, Yuze Jiao, Yangyu Luo, Jian Gong","doi":"10.1109/RCAR52367.2021.9517514","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517514","url":null,"abstract":"A skull reconstruction robot has been developed to effectively overcome shortcomings of the traditional skull surgery and improve its efficiency and quality. In this study, kinematics modeling and trajectory planning for the robot are carried out. Firstly, the kinematic model of the robot is established based on the D-H method. The positioning points, which are used to determine the cutting plane, are given on the surface of the skull by the surgeon. The contour data for cutting can be obtained using a two dimensional laser scanning sensor, by adjusting the scanning light curtain to coincide with the cutting plane. Then, to ensure that the tools fixed at the robot end can always be perpendicular to the skull surface during the cutting or drawing process, the principal component analysis is introduced to design the normal vectors at the selected key points of the point cloud data for the skull surface. Moreover, the quintic polynomial interpolation method is used to construct the curves between the adjacent key points to make up for the defects of the skull surface and ensure the smoothness of the trajectory. Finally, workspace analysis based on Monte Carlo method is implemented, and the proposed kinematics modeling and trajectory planning methods are validated by simulation.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"129 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":"127094676","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

Skeleton-based Action Recognition with Multi-scale Spatial-temporal Convolutional Neural Network 基于骨架的多尺度时空卷积神经网络动作识别

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517665

Qin Cheng, Ziliang Ren, Jun Cheng, Qieshi Zhang, Hao Yan, Jianming Liu

{"title":"Skeleton-based Action Recognition with Multi-scale Spatial-temporal Convolutional Neural Network","authors":"Qin Cheng, Ziliang Ren, Jun Cheng, Qieshi Zhang, Hao Yan, Jianming Liu","doi":"10.1109/RCAR52367.2021.9517665","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517665","url":null,"abstract":"The skeleton data convey significant information for human action recognition since they can robustly accommodate cluttered background and illumination variation. Early convolutional neural networks (CNN) based method mainly structure the skeleton sequence into pseudo-image and feed it into image classification neural network such as Resnet, which can not capture comprehensive spatial-temporal feature. Recently, graph convolutional networks (GCNs) have obtained superior performance. However, the computational complexity of GCN-based methods is quite high, some works even reach 100 GFLOPs for one action sample. This is contrary to the highly condensed attributes of skeleton data. In this paper, a Multi-scale Spatial-temporal Convolution Neural Network (MSST-Net) is proposed for skeleton-based action recognition. Our MSST-Net abandons complex graph convolutions and takes the implicit complementary advantages across different scales of spatial-temporal representations, which are often ignored in the previous work. On two datasets for action recognition, MSST-Net achieves impressive recognition accuracy with a small amount of calculation.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"26 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":"126061958","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

An Accurate Speckle 3D Reconstruction System Based on Binocular Endoscope 基于双目内窥镜的精确散斑三维重建系统

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517489

Junguang Guo, Feifei Gu, Yuping Ye, Zhan Song

引用次数: 1

Total Variation Regularized Low-Rank Tensor Decomposition with nonlocal for single image denoising 基于非局部全变分正则化低秩张量分解的单幅图像去噪

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517668

Shengchuan Li, Yanmei Wang, Qiong Luo, Kai Wang, Zhi Han, Yandong Tang

{"title":"Total Variation Regularized Low-Rank Tensor Decomposition with nonlocal for single image denoising","authors":"Shengchuan Li, Yanmei Wang, Qiong Luo, Kai Wang, Zhi Han, Yandong Tang","doi":"10.1109/RCAR52367.2021.9517668","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517668","url":null,"abstract":"Various noises in the image will reduce the quality of the image and seriously affect the processing of subsequent computer tasks. The recovery of single images is a more challenging problem than recovery of spectral images due to the lack of spectral information. In order to solve this problem, in this paper, we propose a method combining non-local self-similar priors and tensor decomposition to fully explore the inherent low-rank structure of a single image. Specifically, we use tucker decomposition to characterize the global self-similar patch of a single image. At the same time, we introduce anisotropic spatial-spectral total variation regularization to describe the segmented smooth structure in the image. In order to deal with the complex noise situation in the real scene. We model the noise in two parts, one part is sparse spot noise, and the other part is ubiquitous noise. Then we use the augmented Lagrange multiplier method to solve it. Experiments have proved that the introduction of non-local self-similar priors is crucial to the denoising problem of a single image. The proposed method is superior to all comparison methods.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"82 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":"125341952","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

Kinematic control of wheeled mobile manipulators subject to inherent physical constraints and noise disturbances 受固有物理约束和噪声干扰的轮式移动机械臂的运动控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517421

Xiaoxiao Li, Zhihao Xu, Shuai Li, Kanyang Jiang, Xuefeng Zhou, Li Jiang

{"title":"Kinematic control of wheeled mobile manipulators subject to inherent physical constraints and noise disturbances","authors":"Xiaoxiao Li, Zhihao Xu, Shuai Li, Kanyang Jiang, Xuefeng Zhou, Li Jiang","doi":"10.1109/RCAR52367.2021.9517421","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517421","url":null,"abstract":"This paper focuses on the kinematic control of wheeled mobile manipulator with physical limits and external noise disturbances. Based on the quadratic program, a hybrid multi-objective control (HMOC) scheme incorporating path following and physical constraints' compliance is proposed, where the path following task is formulated as an equality constraint. With the velocity escape method, physical limits are incorporated and totally described as an inequality constraint. An integration-enhanced state feedback strategy considering derivation between the robot's real position and the desired position is designed with an aim at achieving anti-noise capability for HMOC scheme, which is shown to improve the tracking performance of the robot in following the desired trajectory under noise disturbances. Lagrangian-based controller is constructed as an online solver of the resultant HMOC scheme to output and update control variables iteratively. With numerical simulation, efficiency of the HMOC scheme and Lagrangian-based controller are validated.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"47 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":"125397023","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

FT-MSTC*: An Efficient Fault Tolerance Algorithm for Multi-robot Coverage Path Planning FT-MSTC*:一种高效的多机器人覆盖路径规划容错算法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517650

Chun Sun, Jing Tang, Xinyu Zhang

引用次数: 4