Rebar binding robot is a typical kind of construction robot, replacing manual binding of steel bars in a standard environment. We develop a robot prototype and build an experimental environment to verify the effectiveness of the Bag of Features based rebar binding state recognition for the binding images taken during the operation of the robot. The method successfully classifies two states of bound and unbound of rebar under different lighting conditions, providing guidance for the engineering application. The dataset used in the article has been publicly released.
{"title":"Recognition of the rebar binding state based on Bag of Features","authors":"Yunfei Shi, Shitao Liu, Gongzheng Chen, Yupo Pan, Lifang Han, Pengfei Wang","doi":"10.1109/RCAR54675.2022.9872189","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872189","url":null,"abstract":"Rebar binding robot is a typical kind of construction robot, replacing manual binding of steel bars in a standard environment. We develop a robot prototype and build an experimental environment to verify the effectiveness of the Bag of Features based rebar binding state recognition for the binding images taken during the operation of the robot. The method successfully classifies two states of bound and unbound of rebar under different lighting conditions, providing guidance for the engineering application. The dataset used in the article has been publicly released.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"54 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":"134393359","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872192
Mengdan Li, Xiao-qian Hu, Liang Du, Sheng Bao, Jianjun Yuan
In the robotic machining process, the external force due to the end-effector (EE) and the workpiece can lead to significant deviations of the desired trajectory. In addition, moving platforms have been added to many heavy-duty industrial robots to improve the workspace. The combination of a moving platform and a six-degree-of-freedom industrial robot constitutes the redundant robot system. However, the effect of moving platforms is rarely considered in the redundant robot system for deflection analysis. This paper analyzes the shortcomings of traditional methods for joint stiffness modeling. Considering the advantages and limitations of traditional methods, we propose an effective method for redundant heavy-duty robot stiffness modeling by considering joint and moving platform compliances. Firstly, the relationship equations of the joints and the end-effector (EE) deformation are derived. Secondly, the static equilibrium equations of the moving platform are established in its stiffness matrix expression, and then the whole redundant robot system stiffness model is derived. Finally, simulations are performed to verify the correctness of the stiffness model. This work can be used for motion planning of redundant serial robots and optimization of machining operations.
{"title":"Stiffness modeling of redundant robots with large load capacity and workspace","authors":"Mengdan Li, Xiao-qian Hu, Liang Du, Sheng Bao, Jianjun Yuan","doi":"10.1109/RCAR54675.2022.9872192","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872192","url":null,"abstract":"In the robotic machining process, the external force due to the end-effector (EE) and the workpiece can lead to significant deviations of the desired trajectory. In addition, moving platforms have been added to many heavy-duty industrial robots to improve the workspace. The combination of a moving platform and a six-degree-of-freedom industrial robot constitutes the redundant robot system. However, the effect of moving platforms is rarely considered in the redundant robot system for deflection analysis. This paper analyzes the shortcomings of traditional methods for joint stiffness modeling. Considering the advantages and limitations of traditional methods, we propose an effective method for redundant heavy-duty robot stiffness modeling by considering joint and moving platform compliances. Firstly, the relationship equations of the joints and the end-effector (EE) deformation are derived. Secondly, the static equilibrium equations of the moving platform are established in its stiffness matrix expression, and then the whole redundant robot system stiffness model is derived. Finally, simulations are performed to verify the correctness of the stiffness model. This work can be used for motion planning of redundant serial robots and optimization of machining operations.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"701 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":"132813770","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872288
Yue Ou, Biying Xu, H. Cai, Jie Zhao, Jizhuang Fan
Nuclear power is worldwide popular and keeps rapidly growing, but manual operation in nuclear facilities is challenging by safety and workload issues. Mobile manipulators are ideal to replace human works in nuclear applications. This article reviews the development of mobile manipulators in nuclear applications over time. Then, we summarize three tasks for robots in nuclear applications and corresponding requirements, followed by an overview of the design of selected specific models. Based on the existing design, we propose the challenges for future mobile manipulators in nuclear applications.
{"title":"An overview on mobile manipulator in nuclear applications*","authors":"Yue Ou, Biying Xu, H. Cai, Jie Zhao, Jizhuang Fan","doi":"10.1109/RCAR54675.2022.9872288","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872288","url":null,"abstract":"Nuclear power is worldwide popular and keeps rapidly growing, but manual operation in nuclear facilities is challenging by safety and workload issues. Mobile manipulators are ideal to replace human works in nuclear applications. This article reviews the development of mobile manipulators in nuclear applications over time. Then, we summarize three tasks for robots in nuclear applications and corresponding requirements, followed by an overview of the design of selected specific models. Based on the existing design, we propose the challenges for future mobile manipulators in nuclear applications.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"1 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":"130999392","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872253
Zhi Li, Jinghao Xin, Ning Li
Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-to-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here1
{"title":"End-to-End Autonomous Exploration for Mobile Robots in Unknown Environments through Deep Reinforcement Learning","authors":"Zhi Li, Jinghao Xin, Ning Li","doi":"10.1109/RCAR54675.2022.9872253","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872253","url":null,"abstract":"Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-to-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here1","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"5 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":"115201404","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872254
Zhiwei Xing, Lu Li, Runchao Ye, Jintao Wang, Xiaorui Zhu, Junting Lv
Although deep learning methods have greatly improved the accuracy of the object detection tasks, it is still challenging to balance the efficiency and accuracy of the algorithms under circumstances of point clouds only. In this paper, an anchor-free one-stage deep neural network, F-PCNet, is proposed to realize real-time detection based on point clouds on an autonomous driving platform while maintaining high accuracy. The proposed network takes the bird’s eye view of point clouds collected by LiDAR as input, and outputs the category and 2D bounding box of each detected object. The backbone of F-PCNet is composed of residual network modules of different sizes which effectively reduce the impact of learning degradation. The anchor-free detection head enables F-PCNet to achieve high levels of accuracy and efficiency. Experimental results show that F-PCNet achieves high detection accuracy in a short time consumption and is suitable for real-time detecting scenarios.
{"title":"F-PCNet: A New Fast Object Detection Method Based on Point Cloud Only*","authors":"Zhiwei Xing, Lu Li, Runchao Ye, Jintao Wang, Xiaorui Zhu, Junting Lv","doi":"10.1109/RCAR54675.2022.9872254","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872254","url":null,"abstract":"Although deep learning methods have greatly improved the accuracy of the object detection tasks, it is still challenging to balance the efficiency and accuracy of the algorithms under circumstances of point clouds only. In this paper, an anchor-free one-stage deep neural network, F-PCNet, is proposed to realize real-time detection based on point clouds on an autonomous driving platform while maintaining high accuracy. The proposed network takes the bird’s eye view of point clouds collected by LiDAR as input, and outputs the category and 2D bounding box of each detected object. The backbone of F-PCNet is composed of residual network modules of different sizes which effectively reduce the impact of learning degradation. The anchor-free detection head enables F-PCNet to achieve high levels of accuracy and efficiency. Experimental results show that F-PCNet achieves high detection accuracy in a short time consumption and is suitable for real-time detecting scenarios.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"47 23 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":"124715083","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}
Generative adversarial networks are widely used in computer vision tasks like image translation and image style transfer. Most of mainstream methods including CycleGAN and pix2pix use the stacking of residual blocks to deepen the number of network layers, which makes the networks have a large number of parameters and floating point operations. This paper presents a ghost-module-based generative adversarial networks. We use the ghost module to replace the residual blocks in the traditional generative adversarial network for building lightweight generative adversarial networks. Experiments shows that our method significantly reducing the parameters and floating point operations of the generative adversarial network on the precondition of assuring the quality of the generated images.
{"title":"Lightweight Generative Adversarial Networks Based on Ghost Module","authors":"Xinyuan Xiang, Meiqin Liu, Senlin Zhang, Ping Wei, Badong Chen","doi":"10.1109/RCAR54675.2022.9872153","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872153","url":null,"abstract":"Generative adversarial networks are widely used in computer vision tasks like image translation and image style transfer. Most of mainstream methods including CycleGAN and pix2pix use the stacking of residual blocks to deepen the number of network layers, which makes the networks have a large number of parameters and floating point operations. This paper presents a ghost-module-based generative adversarial networks. We use the ghost module to replace the residual blocks in the traditional generative adversarial network for building lightweight generative adversarial networks. Experiments shows that our method significantly reducing the parameters and floating point operations of the generative adversarial network on the precondition of assuring the quality of the generated images.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"28 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":"125063953","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872293
Shiqing Fu, Jian Li, Zhang-Hua Fu
Homogeneous robotic sorting systems, such as the famous Kiva system which follows the shelves-to-workers mode, have been successfully used in warehouses. However, these systems generally have shortages in two-folds, i.e., (1) redundant moves of shelves, and (2) unavoidable manual operations. To overcome these shortages, an alternative solution is using heterogeneous robots (fetch and freight robots) cooperatively to accomplish sorting tasks. In this field, the existing works mostly focus on the design of robots, while there is no public literature (to our best knowledge) which studies how to coordinately schedule a large number of fetch and freight robots. To fit this blank, this paper first proposes a cooperative algorithm to schedule hundreds of heterogeneous robots. The algorithm adopts a cloud-edge-terminal architecture, where the cloud is responsible for allocating tasks and robots, the edge computing units monitor the status of each regional area, while the terminals (robots) are able to plan their own paths (guided by the edge computing units) and resolve conflicts by bidding mechanisms. A simulation platform is developed, based on a large amount of simulations (with up to 330 robots) are carried out to analyze the impacts of several key components of the algorithm and confirm the superiority of our algorithm.
{"title":"Cooperatively Scheduling Hundreds of Fetch and Freight Robots in an Autonomous Warehouse","authors":"Shiqing Fu, Jian Li, Zhang-Hua Fu","doi":"10.1109/RCAR54675.2022.9872293","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872293","url":null,"abstract":"Homogeneous robotic sorting systems, such as the famous Kiva system which follows the shelves-to-workers mode, have been successfully used in warehouses. However, these systems generally have shortages in two-folds, i.e., (1) redundant moves of shelves, and (2) unavoidable manual operations. To overcome these shortages, an alternative solution is using heterogeneous robots (fetch and freight robots) cooperatively to accomplish sorting tasks. In this field, the existing works mostly focus on the design of robots, while there is no public literature (to our best knowledge) which studies how to coordinately schedule a large number of fetch and freight robots. To fit this blank, this paper first proposes a cooperative algorithm to schedule hundreds of heterogeneous robots. The algorithm adopts a cloud-edge-terminal architecture, where the cloud is responsible for allocating tasks and robots, the edge computing units monitor the status of each regional area, while the terminals (robots) are able to plan their own paths (guided by the edge computing units) and resolve conflicts by bidding mechanisms. A simulation platform is developed, based on a large amount of simulations (with up to 330 robots) are carried out to analyze the impacts of several key components of the algorithm and confirm the superiority of our algorithm.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"8 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":"127361034","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872247
Tianyu Jiang, Chen Zhang, Rui Wang, S. Chai
With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle.
{"title":"Current Problems and Solutions of Industrial Control Network Intrusion Detection: A Brief Survey","authors":"Tianyu Jiang, Chen Zhang, Rui Wang, S. Chai","doi":"10.1109/RCAR54675.2022.9872247","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872247","url":null,"abstract":"With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle.","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":"124181991","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}
Pub Date : 2022-07-17DOI: 10.1109/RCAR54675.2022.9872205
Hongyan Liu, D. Qu, Fang Xu, Z. Du, Kai Jia, Mingmin Liu
This paper proposes a novel and effective online trajectory generation method to help 6 DOF non-redundant manipulators avoid dynamic obstacles. The proposed method decouples the robot motion planning in the task space into front-end path search and back-end trajectory optimization modules. The path planning module uses the constraint-based kinodynamic path search approach to generate a safe and feasible initial trajectory. In the following stage, the cubic B-spline-based trajectory optimization method is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. The optimization method of the links collision avoidance based on constraint relaxation is integrated into the online trajectory planning task. The task space trajectory is converted to the joint space based on the robot inverse kinematics. Detailed simulations and real-world experiments are reported to demonstrate the effectiveness of our approach.
{"title":"Collision-Free Motion Planning Method Based on Online Trajectory Generation in High Dimensional Dynamic Workspace","authors":"Hongyan Liu, D. Qu, Fang Xu, Z. Du, Kai Jia, Mingmin Liu","doi":"10.1109/RCAR54675.2022.9872205","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872205","url":null,"abstract":"This paper proposes a novel and effective online trajectory generation method to help 6 DOF non-redundant manipulators avoid dynamic obstacles. The proposed method decouples the robot motion planning in the task space into front-end path search and back-end trajectory optimization modules. The path planning module uses the constraint-based kinodynamic path search approach to generate a safe and feasible initial trajectory. In the following stage, the cubic B-spline-based trajectory optimization method is adopted to minimize the penalty of collision cost, smoothness, and dynamical feasibility. The optimization method of the links collision avoidance based on constraint relaxation is integrated into the online trajectory planning task. The task space trajectory is converted to the joint space based on the robot inverse kinematics. Detailed simulations and real-world experiments are reported to demonstrate the effectiveness of our approach.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"34 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":"126583868","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}
To achieve an optimal clinical outcome in orbital fracture reduction surgery (OFRS), accurate localization of the orbital bone is essential. The mixed reality (MR) technology, which also called augmented reality (AR) through optical see-through head-mounted displays (OST-HMD), offers a promising new approach to visualization and navigation in the operating room. We hypothesized that the MR navigation is a feasible, reliable and accurate guide-wire in the orbital bone positioning and visualization in treatment surgery of orbital fracture. Focus on the navigation during OFRS, a specific marker is designed and adopted to perform registration step and the OST-HMD is used as display platform in the navigation system. Model experiments are conducted to evaluate the clinical application value.
{"title":"Mixed Reality Assisted Orbital Reconstruction Navigation System for Reduction Surgery of Orbital Fracture","authors":"Dongsheng Xie, X. Duan, Liuhong Ma, Minghao Zhao, Jianjian Lu, Changsheng Li","doi":"10.1109/RCAR54675.2022.9872226","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872226","url":null,"abstract":"To achieve an optimal clinical outcome in orbital fracture reduction surgery (OFRS), accurate localization of the orbital bone is essential. The mixed reality (MR) technology, which also called augmented reality (AR) through optical see-through head-mounted displays (OST-HMD), offers a promising new approach to visualization and navigation in the operating room. We hypothesized that the MR navigation is a feasible, reliable and accurate guide-wire in the orbital bone positioning and visualization in treatment surgery of orbital fracture. Focus on the navigation during OFRS, a specific marker is designed and adopted to perform registration step and the OST-HMD is used as display platform in the navigation system. Model experiments are conducted to evaluate the clinical application value.","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":"125649787","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}
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