2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第8页

Recognition of the rebar binding state based on Bag of Features 基于特征包的钢筋粘结状态识别

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872189

Yunfei Shi, Shitao Liu, Gongzheng Chen, Yupo Pan, Lifang Han, Pengfei Wang

引用次数: 0

Stiffness modeling of redundant robots with large load capacity and workspace 具有大载荷和工作空间的冗余机器人刚度建模

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872192

Mengdan Li, Xiao-qian Hu, Liang Du, Sheng Bao, Jianjun Yuan

{"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}

引用次数: 1

An overview on mobile manipulator in nuclear applications* 移动机械臂在核领域的应用综述*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872288

Yue Ou, Biying Xu, H. Cai, Jie Zhao, Jizhuang Fan

引用次数: 0

End-to-End Autonomous Exploration for Mobile Robots in Unknown Environments through Deep Reinforcement Learning 基于深度强化学习的移动机器人在未知环境中的端到端自主探索

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872253

Zhi Li, Jinghao Xin, Ning Li

{"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}

引用次数: 4

F-PCNet: A New Fast Object Detection Method Based on Point Cloud Only* F-PCNet:一种新的仅基于点云的快速目标检测方法*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872254

Zhiwei Xing, Lu Li, Runchao Ye, Jintao Wang, Xiaorui Zhu, Junting Lv

引用次数: 0

Lightweight Generative Adversarial Networks Based on Ghost Module 基于Ghost模块的轻量级生成对抗网络

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872153

Xinyuan Xiang, Meiqin Liu, Senlin Zhang, Ping Wei, Badong Chen

引用次数: 0

Cooperatively Scheduling Hundreds of Fetch and Freight Robots in an Autonomous Warehouse 自主仓库中数百个取货机器人的协同调度

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872293

Shiqing Fu, Jian Li, Zhang-Hua Fu

{"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}

引用次数: 1

Current Problems and Solutions of Industrial Control Network Intrusion Detection: A Brief Survey 当前工业控制网络入侵检测存在的问题及解决方案综述

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872247

Tianyu Jiang, Chen Zhang, Rui Wang, S. Chai

引用次数: 0

Collision-Free Motion Planning Method Based on Online Trajectory Generation in High Dimensional Dynamic Workspace 高维动态工作空间中基于在线轨迹生成的无碰撞运动规划方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872205

Hongyan Liu, D. Qu, Fang Xu, Z. Du, Kai Jia, Mingmin Liu

引用次数: 1

Mixed Reality Assisted Orbital Reconstruction Navigation System for Reduction Surgery of Orbital Fracture 混合现实辅助眼眶重建导航系统用于眼眶骨折复位手术

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872226

Dongsheng Xie, X. Duan, Liuhong Ma, Minghao Zhao, Jianjian Lu, Changsheng Li

引用次数: 0