2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第3页

Closed-loop Electromagnetic Actuation System for Magnetic Capsule Robot In a Large Scale 大型磁胶囊机器人闭环电磁作动系统

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872182

Xi Wang, Weilin Chen, Jiaole Wang, Shuang Song

{"title":"Closed-loop Electromagnetic Actuation System for Magnetic Capsule Robot In a Large Scale","authors":"Xi Wang, Weilin Chen, Jiaole Wang, Shuang Song","doi":"10.1109/RCAR54675.2022.9872182","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872182","url":null,"abstract":"Accurate positioning and efficient movement are essential for magnetic capsule endoscopy, which has attracted more and more attention in recent years. However, moving in the desired trajectory often conflicts with precise positioning, as magnetic localization is only feasible in a small area near the sensors. In this paper, we proposed a closed-loop magnetic capsule robot actuation system, which can accomplish localization and actuation simultaneously on a large scale in the fluid environment of the human body. To achieve large-scale detection, electromagnetic coil and sensor array are fixed together on a 3-axis screw mobile platform. The distribution of magnetic field is analyzed with magnetic dipole model and rectangular electromagnetic coil model. Levenberg-Marquardt algorithm has been employed to estimate the position of the capsule robot by subtracting the actuation magnetic field. PI closed-loop controller with localization of the robot as feedback is applied in the system. Although the response speed of the system with the PI controller is not fast, it could perform well in stability, which is expected when the capsule is moving inside the human body. Two specific path following experiments were carried out to verify the performance of simultaneous localization and movement on a large scale. Results showed that the proposed system and method could work well.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"33 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":"115952495","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

Image-Based Visual Tracking Attitude Control Research on Small Video Satellites for Space Targets 空间目标小视频卫星基于图像的视觉跟踪姿态控制研究

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872236

Mengmeng Wang, Cai-zhi Fan, Chao Song

{"title":"Image-Based Visual Tracking Attitude Control Research on Small Video Satellites for Space Targets","authors":"Mengmeng Wang, Cai-zhi Fan, Chao Song","doi":"10.1109/RCAR54675.2022.9872236","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872236","url":null,"abstract":"Small video satellites are capable of conducting real-time continuous observation of space targets through attitude control and have broad application prospects. Since the traditional method of tracking based on location information needs the priori location information of the known target, effective tracking observation cannot be accomplished for non-cooperative targets. In this paper, we are going to design a visual tracking attitude control method for spatial targets based on image information, which can perform autonomous tracking observation for both cooperative and non-cooperative targets. Firstly, based on the principle of perspective projection, the internal and external parameter model of the camera is derived, and the conversion relationship between the inertial coordinate system and the pixel coordinate system of the on-board camera is established. Then the attitude dynamical model and kinematical model of the rigid satellite are given. The desired attitude and desired angular velocity of the small video satellite are derived based on the deviation information of the location coordinates of the target in the image plane projection point from the desired coordinates. Using the attitude error and angular velocity error as the control feedback quantity, the space target tracking PD controller is designed. The global stability of the closed-loop control system is proved using Barbalat theorem. The simulation results show that the proposed control method is effective for the visual tracking attitude control of space targets.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"132 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":"116755465","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

Variable Admittance Control for Robotic Contact Force Tracking in Dynamic Environment Based on Reinforcement Learning 基于强化学习的动态环境下机器人接触力跟踪变导纳控制

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872292

Yufei Zhou, Tianyu Liu, Jingkai Cui, Yanhui Li, Mingchao Zhu

引用次数: 1

A Research on SOTIF of LKA based on STPA* 基于STPA*的LKA SOTIF研究

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872242

Jun Yu Li, Yunshuang Zhang, Shuai Zhao, Chen Chao, Zhibin Du

引用次数: 1

Research on Axial Thermal Error Modeling Method of CNC Machine Tool Spindle Based on GA-ARMA* 基于GA-ARMA*的数控机床主轴轴向热误差建模方法研究

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872211

Weicheng Lin, Ling Yin, Fei Zhang, Zewei He, Yu Chen, Wenhao Li, Yeming Song

{"title":"Research on Axial Thermal Error Modeling Method of CNC Machine Tool Spindle Based on GA-ARMA*","authors":"Weicheng Lin, Ling Yin, Fei Zhang, Zewei He, Yu Chen, Wenhao Li, Yeming Song","doi":"10.1109/RCAR54675.2022.9872211","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872211","url":null,"abstract":"In order to improve the prediction accuracy of the thermal error model of CNC machine tools based on time series and reduce the time of model parameter identification, a time series thermal error modeling method based on intelligent optimization (GA-ARMA) was proposed. Using the reciprocal of the residual between the actual value and the predicted value as the genetic algorithm (GA) individual fitness value function, select the best individual obtained by evolution for several generations as the parameter of the ARMA model, quickly identify the parameters of the ARMA model, and establish the GA-ARMA spindle axial thermal error model. Through experiments to compare the prediction effects of the time series thermal error model based on intelligent optimization and the time series thermal error model, taking a certain type of three-axis CNC machine tool as the object, the prediction and comparison are carried out under different working conditions. The experimental results show that the model prediction average residual error reaches 1.28 $mu$m, and the modeling efficiency is improved by 544%.","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":"116754374","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

Real-Time Human Falling Recognition via Spatial and Temporal Self-Attention Augmented Graph Convolutional Network 基于时空自注意增强图卷积网络的人体跌倒实时识别

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872276

Jiayao Yuan, Chengju Liu, Chuangwei Liu, Liuyi Wang, Qi Chen

{"title":"Real-Time Human Falling Recognition via Spatial and Temporal Self-Attention Augmented Graph Convolutional Network","authors":"Jiayao Yuan, Chengju Liu, Chuangwei Liu, Liuyi Wang, Qi Chen","doi":"10.1109/RCAR54675.2022.9872276","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872276","url":null,"abstract":"Currently, the skeleton-based human action recognition (e.g. walking, sitting and falling down) has achieved great interest, because the skeleton graph is robust to complex background and illumination changes compared to images. In this paper, a complete solution to real-time falling recognition task for intelligent monitoring has been provided. First, a manually annotated skeleton dataset for falling down action recognition is published. Then, a real-time self-attention augmented graph convolutional network (ST-SAGCN) is proposed. The network contains two novel architectures: a spatial self-attention module and a temporal self-attention module, which can effectively learn intra-frame correlations between different body parts, and inter-frame correlations between different frames for each joint. Finally, extensive comparative experiments on the dataset have proven that the proposed model can achieve remarkable improvement on falling recognition task. When the model is deployed in intelligent monitoring system, it achieves an inference speed over 40 fps and meets the demand of practical applications.","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":"123459605","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: 2

Physics-informed Data-driven Approach for Ship Docking Prediction 船舶对接预测的物理数据驱动方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872179

Tongtong Wang, R. Skulstad, Motoyasu Kanazawa, Guoyuan Li, V. Æsøy, Houxiang Zhang

{"title":"Physics-informed Data-driven Approach for Ship Docking Prediction","authors":"Tongtong Wang, R. Skulstad, Motoyasu Kanazawa, Guoyuan Li, V. Æsøy, Houxiang Zhang","doi":"10.1109/RCAR54675.2022.9872179","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872179","url":null,"abstract":"Accurate ship motion predictions play a vital role in supporting the decision-making process onboard. Generally, the ship dynamics are described by either a deterministic model derived from hydrodynamic principles or a black-box model learned from the observations. However, there are always cases in real life where the physics information is insufficient to develop a complete model, and the data quantity is also limited so that a data-driven model is away from expectation. For this obstacle, we propose a physics-data cooperative modeling approach based on a rough ship numerical model and a few operational data to enhance the model quality. The prior knowledge leveraged by the ship’s numerical model is integrated into the neural network as informative inputs, and the informed neural network calibrates the bias between model outcomes and actual states in principle. The proposed approach is validated in the real docking operation of a research vessel. Comparisons with both the purely hydrodynamic model and the data-driven model without physics informed are conducted. The results convinced that the physicsdata hybrid way yields a more accurate model with relaxed data requirements and less learning consumption.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"108 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":"123536424","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

Trajectory Optimization on Safety, Length and Smoothness in Complex Environments with A Locally Trained and Globally Working Agent 基于局部训练全局工作Agent的复杂环境下安全、长度和平滑轨迹优化

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872237

Qianyi Zhang, Dingye Yang, Lei Zhou, Zhengxi Hu, Jingtai Liu

{"title":"Trajectory Optimization on Safety, Length and Smoothness in Complex Environments with A Locally Trained and Globally Working Agent","authors":"Qianyi Zhang, Dingye Yang, Lei Zhou, Zhengxi Hu, Jingtai Liu","doi":"10.1109/RCAR54675.2022.9872237","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872237","url":null,"abstract":"Focused on the balance among safety, length, and smoothness, this paper proposes a novel model to train an agent with deep reinforcement learning to optimize trajectory in complex environments. Inspired by the human habit that first finds the shortest trajectory and then slightly optimizes safety and smoothness, State is initialized as a radical trajectory combined with local obstacle distribution. Action adjusts dangerous waypoints jointly. Reward penalizes length increase based on local smoothness change. Episode is early terminated to divide the whole problem into smaller ones, while reward assembles them back with a large amount of training data. This allows the agent to be trained locally and work globally to accelerate convergence. Performances in various scenarios demonstrate our method’s ability to balance safety, length, and smoothness. With the Markov property of the problem and our newly discovered mathematical property of B-spline, it adjusts waypoints under sub-grid map and can be generalized stably in various maps with dense obstacles.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"55 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":"121808840","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

Attitude control of ultra-low orbit satellite based on RBF neural network 基于RBF神经网络的超低轨道卫星姿态控制

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872306

Cai-zhi Fan, Shaoting Yu, Mengmeng Wang

引用次数: 0

An Autonomous Fire-fighting Robot with Ackermann Steering Mechanism 具有阿克曼转向机构的自主灭火机器人

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872258

Jiaqing Zhang, Yong Zhang, Xiaodong Xu, Zhengqing Wu, Bin Ye

引用次数: 0