IEEE Robotics and Automation Letters最新文献

Visual-Inertial Localization Leveraging Skylight Polarization Pattern Constraints 利用天光极化模式约束进行视觉惯性定位

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-11 DOI: 10.1109/LRA.2024.3495375

Zhenhua Wan;Peng Fu;Kunfeng Wang;Kaichun Zhao

引用次数: 0

CMGFA: A BEV Segmentation Model Based on Cross-Modal Group-Mix Attention Feature Aggregator CMGFA：基于跨模态混合组注意特征聚合器的 BEV 细分模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-11 DOI: 10.1109/LRA.2024.3495376

Xinkai Kuang;Runxin Niu;Chen Hua;Chunmao Jiang;Hui Zhu;Ziyu Chen;Biao Yu

{"title":"CMGFA: A BEV Segmentation Model Based on Cross-Modal Group-Mix Attention Feature Aggregator","authors":"Xinkai Kuang;Runxin Niu;Chen Hua;Chunmao Jiang;Hui Zhu;Ziyu Chen;Biao Yu","doi":"10.1109/LRA.2024.3495376","DOIUrl":"https://doi.org/10.1109/LRA.2024.3495376","url":null,"abstract":"Bird's eye view (BEV) segmentation map is a recent development in autonomous driving that provides effective environmental information, such as drivable areas and lane dividers. Most of the existing methods use cameras and LiDAR as inputs for segmentation and the fusion of different modalities is accomplished through either concatenation or addition operations, which fails to exploit fully the correlation and complementarity between modalities. This letter presents the CMGFA (Cross-Modal Group-mix attention Feature Aggregator), an end-to-end learning framework that can adapt to multiple modal feature combinations for BEV segmentation. The CMGFA comprises the following components: i) The camera has a dual-branch structure that strengthens the linkage between local and global features. ii) Multi-head deformable cross-attention is applied as cross-modal feature aggregators to aggregate camera, LiDAR, and Radar feature maps in BEV for implicit fusion. iii) The Group-Mix attention is used to enrich the attention map feature space and enhance the ability to segment between different categories. We evaluate our proposed method on the nuScenes and Argoverse2 datasets, where the CMGFA significantly outperforms the baseline.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11497-11504"},"PeriodicalIF":4.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636447","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

Virtual Obstacles Regulation for Multi-Agent Path Finding 多代理路径搜索的虚拟障碍调节

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-08 DOI: 10.1109/LRA.2024.3494653

Sike Zeng;Xi Chen;Li Chai

{"title":"Virtual Obstacles Regulation for Multi-Agent Path Finding","authors":"Sike Zeng;Xi Chen;Li Chai","doi":"10.1109/LRA.2024.3494653","DOIUrl":"https://doi.org/10.1109/LRA.2024.3494653","url":null,"abstract":"Multi-agent path finding (MAPF) involves finding collision-free paths for multiple agents while minimizing the total path costs. Explicit estimation conflict-based search (EECBS) represents a state-of-the-art variant of the widely used conflict-based search (CBS) method, offering bounded-suboptimal solutions. However, both CBS and its variants rely on pairwise conflict resolution methods. A conflict boom means many conflicts occur at one location, which frequently exists in scenarios that a large number of agents operate in small space, and usually leads to heavy computational burden. The location that conflict boom occurs is regarded as conflict boom vertex. This letter proposes a novel method, the Virtual Obstacles Regulation, to expedite algorithmic solving processes (such as EECBS) for MAPF. The proposed method identifies conflicts boom vertices and strategically regulates them as global or local virtual obstacles to circumvent concentrated conflicts. Then, the pairwise conflict resolution processes on conflicts boom vertices are significantly simplified, hence accelerating overall algorithm runtime–often dominated by conflict resolution. Numerical studies validate the efficacy of this approach.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11417-11424"},"PeriodicalIF":4.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636311","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

Shape Visual Servoing of a Cable Suspended Between Two Drones 对悬挂在两架无人机之间的缆线进行形状可视化伺服控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-07 DOI: 10.1109/LRA.2024.3494655

Lev Smolentsev;Alexandre Krupa;François Chaumette

{"title":"Shape Visual Servoing of a Cable Suspended Between Two Drones","authors":"Lev Smolentsev;Alexandre Krupa;François Chaumette","doi":"10.1109/LRA.2024.3494655","DOIUrl":"https://doi.org/10.1109/LRA.2024.3494655","url":null,"abstract":"In this letter, we propose a shape visual servoing approach for manipulating a suspended cable attached between two quadrotor drones. A leader-follower control strategy is presented, where a human operator controls the rigid motion of the cable by teleoperating one drone (the leader), while the second drone (the follower) performs a shape visual servoing task to autonomously apply a desired deformation to the cable. The proposed shape visual servoing approach uses an RGB-D camera embedded on the follower drone and has the advantage to rely on a simple geometrical model of the cable that only requires the knowledge of its length. In the same time, our control strategy maintains the best visibility of the cable in the camera field of view. A robust image processing pipeline allows detecting and tracking in real-time the cable shape from the data provided by the onboard RGB-D camera. Experimental results demonstrate the effectiveness of the proposed visual control approach to shape a flexible cable into a desired shape. In addition, we demonstrate experimentally that such system can be used to perform an aerial transport task by grasping with the cable an object fitted with a hook, then moving and releasing it at another location.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11473-11480"},"PeriodicalIF":4.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636274","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

A Dynamic Calibration Framework for the Event-Frame Stereo Camera System 事件帧立体相机系统的动态校准框架

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-04 DOI: 10.1109/LRA.2024.3491426

Rui Hu;Jürgen Kogler;Margrit Gelautz;Min Lin;Yuanqing Xia

引用次数: 0

Minimal Impact Pokes to Place Objects on Planar Surfaces 将物体放置在平面上的最小冲击戳

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-04 DOI: 10.1109/LRA.2024.3491412

Ahmed Zermane;Léo Moussafir;Youcan Yan;Abderrahmane Kheddar

引用次数: 0

DLO Perceiver: Grounding Large Language Model for Deformable Linear Objects Perception DLO 感知器：为可变形线性物体感知建立大型语言模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-04 DOI: 10.1109/LRA.2024.3491428

Alessio Caporali;Kevin Galassi;Gianluca Palli

{"title":"DLO Perceiver: Grounding Large Language Model for Deformable Linear Objects Perception","authors":"Alessio Caporali;Kevin Galassi;Gianluca Palli","doi":"10.1109/LRA.2024.3491428","DOIUrl":"https://doi.org/10.1109/LRA.2024.3491428","url":null,"abstract":"The perception of Deformable Linear Objects (DLOs) is a challenging task due to their complex and ambiguous appearance, lack of discernible features, typically small sizes, and deformability. Despite these challenges, achieving a robust and effective segmentation of DLOs is crucial to introduce robots into environments where they are currently underrepresented, such as domestic and complex industrial settings. In this context, the integration of language-based inputs can simplify the perception task while also enabling the possibility of introducing robots as human companions. Therefore, this letter proposes a novel architecture for the perception of DLOs, wherein the input image is augmented with a text-based prompt guiding the segmentation of the target DLO. After encoding the image and text separately, a Perceiver-inspired structure is exploited to compress the concatenated data into transformer layers and generate the output mask from a latent vector representation. The method is experimentally evaluated on real-world images of DLOs like electrical cables and ropes, validating its efficacy and efficiency in real practical scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11385-11392"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636275","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

Proximal Control of UAVs With Federated Learning for Human-Robot Collaborative Domains 利用联合学习对无人飞行器进行近端控制，实现人机协作领域

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-04 DOI: 10.1109/LRA.2024.3491417

Lucas Nogueira Nobrega;Ewerton de Oliveira;Martin Saska;Tiago Nascimento

{"title":"Proximal Control of UAVs With Federated Learning for Human-Robot Collaborative Domains","authors":"Lucas Nogueira Nobrega;Ewerton de Oliveira;Martin Saska;Tiago Nascimento","doi":"10.1109/LRA.2024.3491417","DOIUrl":"https://doi.org/10.1109/LRA.2024.3491417","url":null,"abstract":"The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11305-11312"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645395","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

RoadRunner M&M - Learning Multi-Range Multi-Resolution Traversability Maps for Autonomous Off-Road Navigation RoadRunner M&M - 学习多范围多分辨率可穿越性地图，用于自主越野导航

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490404

Manthan Patel;Jonas Frey;Deegan Atha;Patrick Spieler;Marco Hutter;Shehryar Khattak

{"title":"RoadRunner M&M - Learning Multi-Range Multi-Resolution Traversability Maps for Autonomous Off-Road Navigation","authors":"Manthan Patel;Jonas Frey;Deegan Atha;Patrick Spieler;Marco Hutter;Shehryar Khattak","doi":"10.1109/LRA.2024.3490404","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490404","url":null,"abstract":"Autonomous robot navigation in off–road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing–to–mapping latency and the look–ahead map range can limit the maximum speed of the vehicle. Building on top of the recent work RoadRunner, in this work, we address the challenge of long-range (\u0000<inline-formula><tex-math>$pm 100 ,text{m}$</tex-math></inline-formula>\u0000) traversability estimation. Our RoadRunner (M&M) is an end-to-end learning-based framework that directly predicts the traversability and elevation maps at multiple ranges (\u0000<inline-formula><tex-math>$pm 50 ,text{m}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$pm 100 ,text{m}$</tex-math></inline-formula>\u0000) and resolutions (\u0000<inline-formula><tex-math>$0.2 ,text{m}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$0.8 ,text{m}$</tex-math></inline-formula>\u0000) taking as input multiple images and a LiDAR voxel map. Our method is trained in a self–supervised manner by leveraging the dense supervision signal generated by fusing predictions from an existing traversability estimation stack (X-Racer) in hindsight and satellite Digital Elevation Maps. RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real–time performance. Experiments on various out–of–distribution datasets also demonstrate that our data-driven approach starts to generalize to novel unstructured environments. We integrate our proposed framework in closed–loop with the path planner to demonstrate autonomous high–speed off–road robotic navigation in challenging real–world environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11425-11432"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636431","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

Bimanual Grasp Synthesis for Dexterous Robot Hands 灵巧机器手的双臂抓握合成技术

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490393

Yanming Shao;Chenxi Xiao

引用次数: 0