2021 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第2页

Markov Parallel Tracking and Mapping for Probabilistic SLAM 概率SLAM的马尔可夫并行跟踪与映射

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561238

Zheng Huai, G. Huang

引用次数: 1

Optimizing Cellular Networks via Continuously Moving Base Stations on Road Networks 基于道路网络的连续移动基站优化蜂窝网络

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561052

Yogesh A. Girdhar, D. Rivkin, Di Wu, Michael Jenkin, Xue Liu, Gregory Dudek

{"title":"Optimizing Cellular Networks via Continuously Moving Base Stations on Road Networks","authors":"Yogesh A. Girdhar, D. Rivkin, Di Wu, Michael Jenkin, Xue Liu, Gregory Dudek","doi":"10.1109/ICRA48506.2021.9561052","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561052","url":null,"abstract":"Although existing cellular network base stations are typically immobile, the recent development of small form factor base stations and self driving cars has enabled the possibility of deploying a team of continuously moving base stations that can reorganize the network infrastructure to adapt to changing network traffic usage patterns. Given such a system of mobile base stations (MBSes) that can freely move on the road, how should their path be planned in an effort to optimize the experience of the users? This paper addresses this question by modeling the problem as a Markov Decision Process where the actions correspond to the MBSes deciding which direction to go at traffic intersections; states corresponds to the position of MBSes; and rewards correspond to minimization of packet loss in the network. A Monte Carlo Tree Search (MCTS)-based anytime algorithm that produces path plans for multiple base stations while optimizing expected packet loss is proposed. Simulated experiments in the city of Verdun, QC, Canada with varying user equipment (UE) densities and random initial conditions show that the proposed approach consistently outperforms myopic planners, and is able to achieve near-optimal performance.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125041731","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

Shape-Based Transfer of Generic Skills 基于形状的通用技能转移

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560894

Skye Thompson, L. Kaelbling, Tomas Lozano-Perez

引用次数: 7

Motion Planning and Feedback Control for Bipedal Robots Riding a Snakeboard 蛇形板双足机器人运动规划与反馈控制

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560963

Jonathan Anglingdarma, Ayush Agrawal, Joshua Morey, K. Sreenath

引用次数: 0

A Generative Model-Based Predictive Display for Robotic Teleoperation 基于生成模型的机器人遥操作预测显示

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561787

Bowen Xie, Mingjie Han, Jun Jin, Martin Barczyk, Martin Jägersand

引用次数: 0

Reinforcement Learning Control of A Novel Magnetic Actuated Flexible-joint Robotic Camera System for Single Incision Laparoscopic Surgery 单切口腹腔镜手术磁驱动柔性关节机器人摄像系统的强化学习控制

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560927

Dong Xu, Yuanlin Zhang, Wenshuai Tan, Hongxing Wei

{"title":"Reinforcement Learning Control of A Novel Magnetic Actuated Flexible-joint Robotic Camera System for Single Incision Laparoscopic Surgery","authors":"Dong Xu, Yuanlin Zhang, Wenshuai Tan, Hongxing Wei","doi":"10.1109/ICRA48506.2021.9560927","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9560927","url":null,"abstract":"This paper describes the control of a novel Magnetic Actuated Flexible-joint Robotic Surgical (MAFRS) camera system with four degrees of freedom (4-DOF) for single incision laparoscopic surgery. Based on the idea of motion decoupling, we designed a novel MAFRS system which is consists of an external driving device and a motor-free insertable wireless robotic device with a hollow flexible joint. Due to the problems of abdominal wall obstruction and variability in abdominal wall thickness during the actual application of the MAFRS system, as well as the existence of multiple permanent magnets and magnetically conductive media, high- precision position and attitude control of the insertable device without onboard motors has always been a challenge. We use the external driving device to generate a magnetic field to control the position and attitude of the internal robotic device. Aiming at the automatic precise tilt motion control of the novel MAFRS camera system, we have developed a closed-loop control scheme using the Deep Deterministic Policy Gradient (DDPG) algorithm. By referring to the damping characteristics of human muscles, a virtual-muscle method is proposed to eliminate the chattering problem of the MAFRS camera at specific angles. The experimental investigations indicate that the internal robotic device can be effectively controlled under different abdominal wall thicknesses. The tilt motion control accuracy is within 0.5°, and it has good adaptability and antiinterference performance.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123416946","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

Diffuser: Multi-View 2D-to-3D Label Diffusion for Semantic Scene Segmentation Diffuser:用于语义场景分割的多视图2d到3d标签扩散

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561801

R. Mascaro, L. Teixeira, M. Chli

{"title":"Diffuser: Multi-View 2D-to-3D Label Diffusion for Semantic Scene Segmentation","authors":"R. Mascaro, L. Teixeira, M. Chli","doi":"10.1109/ICRA48506.2021.9561801","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9561801","url":null,"abstract":"Semantic 3D scene understanding is a fundamental problem in computer vision and robotics. Despite recent advances in deep learning, its application to multi-domain 3D semantic segmentation typically suffers from the lack of extensive enough annotated 3D datasets. On the contrary, 2D neural networks benefit from existing large amounts of training data and can be applied to a wider variety of environments, sometimes even without need for retraining. In this paper, we present ‘Diffuser’, a novel and efficient multi-view fusion framework that leverages 2D semantic segmentation of multiple image views of a scene to produce a consistent and refined 3D segmentation. We formulate the 3D segmentation task as a transductive label diffusion problem on a graph, where multi-view and 3D geometric properties are used to propagate semantic labels from the 2D image space to the 3D map. Experiments conducted on indoor and outdoor challenging datasets demonstrate the versatility of our approach, as well as its effectiveness for both global 3D scene labeling and single RGB-D frame segmentation. Furthermore, we show a significant increase in 3D segmentation accuracy compared to probabilistic fusion methods employed in several state-of-the-art multi-view approaches, with little computational overhead.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485765","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: 14

Micro Robotic Manipulation System for the Force Stimulation of Muscle Fiber-like Cell Structure 肌肉纤维样细胞结构力刺激的微型机器人操作系统

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560846

Xie Chen, Qing Shi, S. Shimoda, Tao Sun, Huaping Wang, Qiang Huang, T. Fukuda

{"title":"Micro Robotic Manipulation System for the Force Stimulation of Muscle Fiber-like Cell Structure","authors":"Xie Chen, Qing Shi, S. Shimoda, Tao Sun, Huaping Wang, Qiang Huang, T. Fukuda","doi":"10.1109/ICRA48506.2021.9560846","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9560846","url":null,"abstract":"Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123585331","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

Improving Ranging-Based Location Estimation with Rigidity-Constrained CRLB-Based Motion Planning 基于刚度约束的crlb运动规划改进基于距离的位置估计

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9560750

Justin Cano, J. L. Ny

{"title":"Improving Ranging-Based Location Estimation with Rigidity-Constrained CRLB-Based Motion Planning","authors":"Justin Cano, J. L. Ny","doi":"10.1109/ICRA48506.2021.9560750","DOIUrl":"https://doi.org/10.1109/ICRA48506.2021.9560750","url":null,"abstract":"Ranging systems can provide inexpensive, accurate, energy- and computationally-efficient navigation solutions for mobile robots. This work focuses on location and pose estimation in ranging networks composed of anchors with known positions as well as mobile robots modeled as rigid bodies, each carrying multiple tags to localize. Noisy distance measurements can be obtained between a subset of the nodes (anchors and tags), and the robots can move in order to improve the accuracy of the localization process, which depends on the geometry of the network. We propose a method to find trajectories for the robots leading to configurations that locally optimize this localization accuracy. These trajectories minimize a cost function based on the constrained Cramér-Rao Lower Bound (CRLB), where the constraints capture the information about the known distances between tags carried by the same robot. A primal-dual optimization scheme aims to enforce these distance constraints between tags in the motion planner as well. An important feature of the approach is that the gradient terms necessary to plan the motion can be computed essentially in closed form, thereby simplifying the implementation. We compare the proposed method to a naive two-stage algorithm that optimizes the positions and orientations of the robots independently. Simulation results illustrate the benefits of using the constrained optimization approach.","PeriodicalId":108312,"journal":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123809215","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: 6

A Geometric Folding Pattern for Robot Coverage Path Planning 机器人覆盖路径规划的几何折叠模式

2021 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2021-05-30 DOI: 10.1109/ICRA48506.2021.9561433

Lifeng Zhu, Shuai Yao, Bo Li, Aiguo Song, Yiyang Jia, J. Mitani

引用次数: 5