IEEE Robotics and Automation Letters最新文献_第8页

Double-Integrator Multirobot Control With Uncoupled Dynamics for Transport of Deformable Objects 可变形物体运输的双积分多机器人非耦合动力学控制

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-28 DOI: 10.1109/LRA.2023.3320430

Rafael Herguedas;Miguel Aranda;Gonzalo López-Nicolás;Carlos Sagüés;Youcef Mezouar

引用次数: 1

Lightweight and Flexible Prosthetic Wrist With Shape Memory Alloy (SMA)-Based Artificial Muscle and Elliptic Rolling Joint 基于形状记忆合金（SMA）的人工肌肉和椭圆滚动关节的轻型柔性腕关节

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-28 DOI: 10.1109/LRA.2023.3320496

Kyujin Hyeon;Chongyoung Chung;Jihyeong Ma;Ki-Uk Kyung

{"title":"Lightweight and Flexible Prosthetic Wrist With Shape Memory Alloy (SMA)-Based Artificial Muscle and Elliptic Rolling Joint","authors":"Kyujin Hyeon;Chongyoung Chung;Jihyeong Ma;Ki-Uk Kyung","doi":"10.1109/LRA.2023.3320496","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320496","url":null,"abstract":"This letter proposes a novel prosthetic wrist that emulates the anatomical structure of the human wrist, specifically the wrist bones and muscles responsible for wrist movements. To achieve a range of motion (ROM) and load-bearing capacity comparable to the human wrist joint, we designed an elliptic rolling joint as an artificial wrist joint, mimicking the two-row structures of carpal bones. The joint offers two degrees of freedom (DOFs) and can support high loads while also providing adequate ROM. In addition, we designed the artificial muscles using the properties of human muscles, such as moment arm and displacement, and implemented them as shape memory alloy (SMA) spring-based actuators. The resulting prosthetic wrist, incorporating the artificial joint and artificial muscles, is lightweight at only 50g and can perform functional ranges of motion, including 53° for flexion, 50° for extension, 40° for radial deviation, and 42° for ulnar deviation. The use of SMA spring actuators confers restoring force and flexibility to the prosthetic wrist, allowing it to withstand external disturbances. Furthermore, the proposed wrist can be utilized as a robotic wrist, affording two additional DOFs, the ability to lift loads more than 20 times its weight, and variable joint stiffness.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7849-7856"},"PeriodicalIF":5.2,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50247669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Jump Over Block (JOB): An Efficient Line-of-Sight Checker for Grid/Voxel Maps With Sparse Obstacles 跳过块（JOB）：一种用于稀疏障碍物网格/体素映射的有效视线检查器

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-28 DOI: 10.1109/LRA.2023.3320435

Zhuo Yao;Wei Wang;Jiadong Zhang;Yan Wang;Jinjiang Li

{"title":"Jump Over Block (JOB): An Efficient Line-of-Sight Checker for Grid/Voxel Maps With Sparse Obstacles","authors":"Zhuo Yao;Wei Wang;Jiadong Zhang;Yan Wang;Jinjiang Li","doi":"10.1109/LRA.2023.3320435","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320435","url":null,"abstract":"Line-Of-Sight (LOS) check plays a crucial role in collision avoidance and time comsuming, particularly in scenarios involving large-scale maps with sparse obstacles, as it necessitates a grid-by-grid state check. Specifically, LOS check consumes more than half of the computational time in any-angle path planning algorithms, such as Theta*, Visibility Graph, and RRT. To address this issue, we propose an efficient LOS checker for maps of arbitrary dimensions with sparse obstacles. Our approach involves a two-step process. Firstly, we partition the passable space into blocks until there is no vacancy for a minimum-sized block. When the adapted Bresenham algorithm reaches a surface of a block, it bypasses grid-by-grid traversal within the block and directly jumps to the opposing surface. This method significantly reduces the number of grids examined, resulting in higher efficiency compared to traditional LOS checks. We refer to our approach as Jump Over Block (JOB). To demonstrate the advantages of JOB, we compare its performance against traditional LOS checks using a widely recognized public dataset\u00001\u0000. The results indicate that JOB incurs only 1/6 to 1/5 of the computational cost associated with raw LOS checks, making it a valuable tool for both researchers and practitioners in the field. In order to facilitate further research within the community, we have made the source code of the proposed algorithm publicly available\u00002\u0000. We anticipate that this framework will contribute to the development of more efficient path planning algorithms and expedite various aspects of robotics that involve collision checks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7575-7582"},"PeriodicalIF":5.2,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50248397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction 基于双向预测的6D目标姿态估计中逐点注意力的利用

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320015

Yuhao Yang;Jun Wu;Yue Wang;Guangjian Zhang;Rong Xiong

{"title":"Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction","authors":"Yuhao Yang;Jun Wu;Yue Wang;Guangjian Zhang;Rong Xiong","doi":"10.1109/LRA.2023.3320015","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320015","url":null,"abstract":"Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.To address the problem,the letter proposes a bidirectional correspondence prediction network with a point-wise attention-aware mechanism. This network not only requires the model points to predict the correspondence but also explicitly models the geometric similarities between observations and the model prior. Our key insight is that the correlations between each model point and scene point provide essential information for learning point-pair matches. To further tackle the correlation noises brought by feature distribution divergence, we design a simple but effective pseudo-siamese network to improve feature homogeneity. Experimental results on the public datasets of LineMOD, YCB-Video, and Occ-LineMOD show that the proposed method achieves better performance than other state-of-the-art methods under the same evaluation criteria. Its robustness in estimating poses is greatly improved, especially in an environment with severe occlusions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7791-7798"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50247943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Pre-Training for 3D Leaf Instance Segmentation 3D叶实例分割的无监督预训练

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320018

Gianmarco Roggiolani;Federico Magistri;Tiziano Guadagnino;Jens Behley;Cyrill Stachniss

{"title":"Unsupervised Pre-Training for 3D Leaf Instance Segmentation","authors":"Gianmarco Roggiolani;Federico Magistri;Tiziano Guadagnino;Jens Behley;Cyrill Stachniss","doi":"10.1109/LRA.2023.3320018","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320018","url":null,"abstract":"Crops for food, feed, fiber, and fuel are key natural resources for our society. Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping. Traditionally, this task is done manually, which is time- and labor-intensive. Robots can automate phenotyping providing reproducible and high-frequency measurements. Today's perception systems use deep learning to interpret these measurements, but require a substantial amount of annotated data to work well. Obtaining such labels is challenging as it often requires background knowledge on the side of the labelers. This letter addresses the problem of reducing the labeling effort required to perform leaf instance segmentation on 3D point clouds, which is a first step toward phenotyping in 3D. Separating all leaves allows us to count them and compute relevant traits as their areas, lengths, and widths. We propose a novel self-supervised task-specific pre-training approach to initialize the backbone of a network for leaf instance segmentation. We also introduce a novel automatic postprocessing that considers the difficulty of correctly segmenting the points close to the stem, where all the leaves petiole overlap. The experiments presented in this letter suggest that our approach boosts the performance over all the investigated scenarios. We also evaluate the embeddings to assess the quality of the fully unsupervised approach and see a higher performance of our domain-specific postprocessing.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7448-7455"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50248391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences Mask4D：基于端到端掩模的激光雷达序列4D泛光学分割

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320020

Rodrigo Marcuzzi;Lucas Nunes;Louis Wiesmann;Elias Marks;Jens Behley;Cyrill Stachniss

{"title":"Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences","authors":"Rodrigo Marcuzzi;Lucas Nunes;Louis Wiesmann;Elias Marks;Jens Behley;Cyrill Stachniss","doi":"10.1109/LRA.2023.3320020","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320020","url":null,"abstract":"Scene understanding is crucial for autonomous systems to reliably navigate in the real world. Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a vehicle's environment by predicting semantic classes for each 3D point and to identify individual instances through different instance IDs. To describe the dynamics of the surroundings, 4D panoptic segmentation further extends this information with temporarily consistent instance IDs to identify the different instances in the scans consistently over whole sequences. Previous approaches for 4D panoptic segmentation rely on post-processing steps and are often not end-to-end trainable. In this paper, we propose a novel approach that can be trained end-to-end and directly predicts a set of non-overlapping masks along with their semantic classes and instance IDs that are consistent over time without any post-processing like clustering or associations between predictions. We extend a mask-based 3D panoptic segmentation model to 4D by reusing queries that decoded instances in previous scans. This way, each query decodes the same instance over time, carries its ID and the tracking is performed implicitly. This enables us to jointly optimize segmentation and tracking and directly supervise for 4D panoptic segmentation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7487-7494"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50387293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration D-Lite：面向导航的多机器人协作三维场景图压缩

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320011

Yun Chang;Luca Ballotta;Luca Carlone

{"title":"D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration","authors":"Yun Chang;Luca Ballotta;Luca Carlone","doi":"10.1109/LRA.2023.3320011","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320011","url":null,"abstract":"For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that the collected information is efficiently shared among robots in order to support exploration and navigation tasks. Practical constraints of wireless channels, such as limited bandwidth, urge robots to carefully select information to be transmitted. In this letter, we consider the case where environmental information is modeled using a \u0000<italic>3D Scene Graph\u0000, a hierarchical map representation that describes both geometric and semantic aspects of the environment. Then, we leverage graph-theoretic tools, namely \u0000<italic>graph spanners\u0000, to design greedy algorithms that efficiently compress 3D Scene Graphs with the aim of enabling communication between robots under bandwidth constraints. Our compression algorithms are \u0000<italic>navigation-oriented\u0000 in that they are designed to approximately preserve shortest paths between locations of interest while meeting a user-specified communication budget constraint. The effectiveness of the proposed algorithms is demonstrated in robot navigation experiments in a realistic simulator.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7527-7534"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/7083369/10254630/10265226.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50247661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Comparison of Typical Physics Engines Using Robot Models With Multiple Joints 使用多关节机器人模型的典型物理发动机性能比较

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320019

Chengye Liao;Yarong Wang;Xuda Ding;Yi Ren;Xiaoming Duan;Jianping He

引用次数: 0

Model-Based Underwater 6D Pose Estimation From RGB 基于模型的RGB水下6D姿态估计

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320028

Davide Sapienza;Elena Govi;Sara Aldhaheri;Marko Bertogna;Eloy Roura;Èric Pairet;Micaela Verucchi;Paola Ardón

{"title":"Model-Based Underwater 6D Pose Estimation From RGB","authors":"Davide Sapienza;Elena Govi;Sara Aldhaheri;Marko Bertogna;Eloy Roura;Èric Pairet;Micaela Verucchi;Paola Ardón","doi":"10.1109/LRA.2023.3320028","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320028","url":null,"abstract":"Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, however, the data is not clear and the sensors expensive. For this reason, the community has focused on extracting pose estimates from RGB input. In this work, we propose an approach that leverages 2D object detection to reliably compute 6D pose estimates in different underwater scenarios. We test our proposal with 4 objects with symmetrical shapes and poor texture spanning across \u0000<inline-formula><tex-math>$33{,}920$</tex-math></inline-formula>\u0000 synthetic and 10 real scenes. All objects and scenes are made available in an open-source dataset that includes annotations for object detection and pose estimation. When benchmarking against similar end-to-end methodologies for 6D object pose estimation, our pipeline provides estimates that are \u0000<inline-formula><tex-math>$sim !8{%}$</tex-math></inline-formula>\u0000 more accurate. We also demonstrate the real-world usability of our pose estimation pipeline on an underwater robotic manipulator in a reaching task.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7535-7542"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50247900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Seeing Through the Grass: Semantic Pointcloud Filter for Support Surface Learning 透视草地：支持表面学习的语义点云过滤器

IF 5.2 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2023-09-27 DOI: 10.1109/LRA.2023.3320016

Anqiao Li;Chenyu Yang;Jonas Frey;Joonho Lee;Cesar Cadena;Marco Hutter

{"title":"Seeing Through the Grass: Semantic Pointcloud Filter for Support Surface Learning","authors":"Anqiao Li;Chenyu Yang;Jonas Frey;Joonho Lee;Cesar Cadena;Marco Hutter","doi":"10.1109/LRA.2023.3320016","DOIUrl":"https://doi.org/10.1109/LRA.2023.3320016","url":null,"abstract":"Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs. However, the measured depth fails to align with the true support surface in the presence of high grass or other penetrable vegetation. In this work, we present the semantic pointcloud filter (SPF), a convolutional neural network (CNN) that learns to adjust LiDAR measurements to align with the underlying support surface. The SPF is trained in a semi-self-supervised manner and takes as an input a LiDAR pointcloud and RGB image. The network predicts a binary segmentation mask that identifies the specific points requiring adjustment, along with estimating their corresponding depth values. To train the segmentation task, 464 distinct images are manually labeled into rigid and non-rigid terrain. The depth estimation task is trained in a self-supervised manner by utilizing the future footholds of the robot to estimate the support surface based on a Gaussian process. Our method can correctly adjust the support surface prior to interacting with the terrain and is extensively tested on the quadruped robot ANYmal. We show the qualitative benefits of SPF in natural environments for elevation mapping and traversability estimation compared to using raw sensor measurements and existing smoothing methods. Quantitative analysis is performed in various natural environments, and an improvement by 48% RMSE is achieved within a meadow terrain.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"8 11","pages":"7687-7694"},"PeriodicalIF":5.2,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50248218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4