2023 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第4页

Tactile based robotic skills for cable routing operations 触觉为基础的机器人技能电缆布线操作

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160729

Andrea Monguzzi, Martina Pelosi, A. Zanchettin, P. Rocco

引用次数: 1

An equivalent two section method for calculating the workspace of multi-segment continuum robots 计算多段连续体机器人工作空间的等效二段方法

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160611

Yeman Fan, Dikai Liu

{"title":"An equivalent two section method for calculating the workspace of multi-segment continuum robots","authors":"Yeman Fan, Dikai Liu","doi":"10.1109/ICRA48891.2023.10160611","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160611","url":null,"abstract":"Obtaining the shape and size of a robot's workspace is essential for both its design and control. However, determining the accurate workspace of a multi-segment continuum robot by graphic or analytical methods is a challenging task due to its inherent flexibility and complex structure. Existing numerical methods have limitations when applied to a continuum robot. This paper presents an Equivalent Two Section (ETS) method for calculating the workspace of multi-segment continuum robots. This method is based on the forward kinematics and a piecewise constant curvature (PCC) model to determine the boundaries of the workspace. In order to verify the proposed method, simulation experiments are conducted using six different maximum bending angles and seven different number of segments. Results of the ETS method are compared to the true workspaces of these configurations estimated by an exhaustive approach. The results show that the proposed ETS method is both efficient and accurate, and has small estimation errors. Discussions on the advantages and limitations of the proposed ETS method are also presented.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114512482","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

Non-Minimal Solvers for Relative Pose Estimation with a Known Relative Rotation Angle 已知相对旋转角度的相对姿态估计的非最小解

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160580

Deshun Hu

引用次数: 0

Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators 基于学习的冗余机械手路径跟踪问题轨迹优化初始化

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161426

Minsung Yoon, Mincheul Kang, Daehyung Park, S.-E. Yoon

{"title":"Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators","authors":"Minsung Yoon, Mincheul Kang, Daehyung Park, S.-E. Yoon","doi":"10.1109/ICRA48891.2023.10161426","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161426","url":null,"abstract":"Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution trajectories and the lack of prior knowledge about task constraints in configuration space. To alleviate the issue, we present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget by adopting example-guided reinforcement learning. In addition, we suggest a null-space projected imitation reward to consider null-space constraints by efficiently learning kinematically feasible motion captured in expert demonstrations. Our statistical evaluation in simulation shows the improved optimality, efficiency, and applicability of TO when we plug in our method's output, compared with three other baselines. We also show the performance improvement and feasibility via real-world experiments with a seven-degree-of-freedom manipulator.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122000976","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: 3

FDLNet: Boosting Real-time Semantic Segmentation by Image-size Convolution via Frequency Domain Learning FDLNet:基于频域学习的图像大小卷积增强实时语义分割

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161421

Qingqing Yan, Shu Li, Chengju Liu, Meilin Liu, Qi Chen

{"title":"FDLNet: Boosting Real-time Semantic Segmentation by Image-size Convolution via Frequency Domain Learning","authors":"Qingqing Yan, Shu Li, Chengju Liu, Meilin Liu, Qi Chen","doi":"10.1109/ICRA48891.2023.10161421","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161421","url":null,"abstract":"This paper proposes a novel real-time semantic segmentation network via frequency domain learning, called FDLNet, which revisits the segmentation task from two critical perspectives: spatial structure description and multilevel feature fusion. We first devise an image-size convolution (IS-Conv) as a global frequency-domain learning operator to capture long-range dependency in a single shot. To model spatial structure information, we construct the global structure representation path (GSRP) based on IS-Conv, which learns a unified edge-region representation with affordable complexity. For efficient and lightweight multi-level feature fusion, we propose the factorized stereoscopic attention (FSA) module, which alleviates semantic confusion and reduces feature redundancy by introducing level-wise attention before channel and spatial attention. Combining the above modules, we propose a concise semantic segmentation framework named FDLNet. We experimentally demonstrate the effectiveness and superiority of the proposed method. FDLNet achieves state-of-the-art performance on the Cityscapes, which reports 76.32% mIoU at 150+ FPS and 79.0% mIoU at 41+ FPS. The code is available at https://github.com/qyan0131/FDLNet.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603877","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

Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators 机械臂低维控制的状态条件线性映射学习

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160585

Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich, Masood Dehghan, Faezeh Haghverd, Martin Jagersand

{"title":"Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators","authors":"Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich, Masood Dehghan, Faezeh Haghverd, Martin Jagersand","doi":"10.1109/ICRA48891.2023.10160585","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10160585","url":null,"abstract":"Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands outside of a single low-dimensional subspace. We propose that learning local linear action representations can achieve both of these benefits. Our state-conditioned linear maps ensure that for any given state, the high-dimensional robotic actuation is linear in the low-dimensional actions. As the robot state evolves, so do the action mappings, so that necessary motions can be performed during a task. These local linear representations guarantee desirable theoretical properties by design. We validate these findings empirically through two user studies. Results suggest state-conditioned linear maps outperform conditional autoencoder and PCA baselines on a pick-and-place task and perform comparably to mode switching in a more complex pouring task.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128549356","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

Multi-swarm Genetic Gray Wolf Optimizer with Embedded Autoencoders for High-dimensional Expensive Problems 嵌入自编码器的高维昂贵问题多群遗传灰狼优化算法

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161299

J. Bi, Jiahui Zhai, Haitao Yuan, Ziqi Wang, J. Qiao, Jia Zhang, Mengchu Zhou

{"title":"Multi-swarm Genetic Gray Wolf Optimizer with Embedded Autoencoders for High-dimensional Expensive Problems","authors":"J. Bi, Jiahui Zhai, Haitao Yuan, Ziqi Wang, J. Qiao, Jia Zhang, Mengchu Zhou","doi":"10.1109/ICRA48891.2023.10161299","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161299","url":null,"abstract":"High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130411037","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

Obstacle-Aware Topological Planning over Polyhedral Representation for Quadrotors 基于多面体表示的四旋翼飞行器障碍物感知拓扑规划

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10161295

Junjie Gao, Fenghua He, W. Zhang, Yu Yao

{"title":"Obstacle-Aware Topological Planning over Polyhedral Representation for Quadrotors","authors":"Junjie Gao, Fenghua He, W. Zhang, Yu Yao","doi":"10.1109/ICRA48891.2023.10161295","DOIUrl":"https://doi.org/10.1109/ICRA48891.2023.10161295","url":null,"abstract":"In this paper, we propose a novel mapping-planning framework for autonomous quadrotor navigation. First, a polyhedron-based mapping algorithm is presented to fully exploit the information of the onboard sensor data. Polyhedra are generated to approximate the segmented clusters of occupied voxels. Then, customized data structures are designed to extract information for motion planning in real time. With complete knowledge of the shape, position, and number of the observed obstacles, we can conveniently generate smooth trajectories with sufficient obstacle clearance along the most desired direction. Before searching for the initial path, a local topological graph is constructed to keep the path expanding in the most favorable topology class. The following path search is segmented based on the graph vertices, which allows fast convergence. The refined trajectory is obtained after smoothing, and large deviations are penalized in the formulated optimization problem to preserve the original clearance. Finally, we analyze and validate the proposed framework through extensive simulations and real-world quadrotor flights.","PeriodicalId":360533,"journal":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130416905","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: 3

Small-shot Multi-modal Distillation for Vision-based Autonomous Steering 基于视觉的自主转向小样本多模态蒸馏

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160803

Yu Shen, Luyu Yang, Xijun Wang, Ming-Chyuan Lin

引用次数: 0

Effective Combination of Vertical, Longitudinal and Lateral Data for Vehicle Mass Estimation 车辆质量估算的纵向、纵向和横向数据有效组合

2023 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2023-05-29 DOI: 10.1109/ICRA48891.2023.10160550

Younesse El Mrhasli, B. Monsuez, X. Mouton

引用次数: 0