IEEE Robotics and Automation Letters最新文献_第5页

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

Flexible Affine Formation Control Based on Dynamic Hierarchical Reorganization 基于动态分层重组的灵活仿射编队控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490407

Yuzhu Li;Wei Dong

{"title":"Flexible Affine Formation Control Based on Dynamic Hierarchical Reorganization","authors":"Yuzhu Li;Wei Dong","doi":"10.1109/LRA.2024.3490407","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490407","url":null,"abstract":"Current formations commonly rely on invariant hierarchical structures, such as predetermined leaders or enumerated formation shapes. These structures could be unidirectional and sluggish, constraining their flexibility and agility when encountering cluttered environments. To surmount these constraints, this work proposes a dynamic hierarchical reorganization approach with affine formation. Central to our approach is the fluid leadership and authority redistribution, for which we develop a minimum time-driven leadership evaluation algorithm and a power transition control algorithm. These algorithms facilitate autonomous leader selection and ensure smooth power transitions, enabling the swarm to adapt hierarchically in alignment with the external environment. Extensive simulations and real-world experiments validate the effectiveness of the proposed method. The formation of five aerial robots successfully performs dynamic hierarchical reorganizations, enabling the execution of complex tasks such as swerving maneuvers and navigating through hoops at velocities of up to 1.05m/s. Comparative experimental results further demonstrate the significant advantages of hierarchical reorganization in enhancing formation flexibility and agility, particularly during complex maneuvers such as U-turns. Notably, in the aforementioned real-world experiments, the proposed method reduces the flight path length by at least 33.8% compared to formations without hierarchical reorganization.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11290-11297"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598645","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

PEP: Policy-Embedded Trajectory Planning for Autonomous Driving PEP：自主驾驶的政策嵌入式轨迹规划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490377

Dongkun Zhang;Jiaming Liang;Sha Lu;Ke Guo;Qi Wang;Rong Xiong;Zhenwei Miao;Yue Wang

引用次数: 0

TICMapNet: A Tightly Coupled Temporal Fusion Pipeline for Vectorized HD Map Learning TICMapNet：用于矢量化高清地图学习的紧密耦合时态融合管道

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490384

Wenzhao Qiu;Shanmin Pang;Hao Zhang;Jianwu Fang;Jianru Xue

{"title":"TICMapNet: A Tightly Coupled Temporal Fusion Pipeline for Vectorized HD Map Learning","authors":"Wenzhao Qiu;Shanmin Pang;Hao Zhang;Jianwu Fang;Jianru Xue","doi":"10.1109/LRA.2024.3490384","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490384","url":null,"abstract":"High-Definition (HD) map construction is essential for autonomous driving to accurately understand the surrounding environment. Most existing methods rely on single-frame inputs to predict local map, which often fail to effectively capture the temporal correlations between frames. This limitation results in discontinuities and instability in the generated map.To tackle this limitation, we propose a \u0000<italic>Ti</i>\u0000ghtly \u0000<italic>C</i>\u0000oupled temporal fusion \u0000<italic>Map</i>\u0000 \u0000<italic>Net</i>\u0000work (TICMapNet). TICMapNet breaks down the fusion process into three sub-problems: PV feature alignment, BEV feature adjustment, and Query feature fusion. By doing so, we effectively integrate temporal information at different stages through three plug-and-play modules, using the proposed tightly coupled strategy. Unlike traditional methods, our approach does not rely on camera extrinsic parameters, offering a new perspective for addressing the visual fusion challenge in the field of object detection. Experimental results show that TICMapNet significantly improves upon its single-frame baseline model, achieving at least a 7.0% increase in mAP using just two consecutive frames on the nuScenes dataset, while also showing generalizability across other tasks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11289-11296"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645394","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

S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM S3E：用于协作式 SLAM 的多机器人多模态数据集

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490402

Dapeng Feng;Yuhua Qi;Shipeng Zhong;Zhiqiang Chen;Qiming Chen;Hongbo Chen;Jin Wu;Jun Ma

{"title":"S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM","authors":"Dapeng Feng;Yuhua Qi;Shipeng Zhong;Zhiqiang Chen;Qiming Chen;Hongbo Chen;Jin Wu;Jun Ma","doi":"10.1109/LRA.2024.3490402","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490402","url":null,"abstract":"The burgeoning demand for collaborative robotic systems to execute complex tasks collectively has intensified the research community's focus on advancing simultaneous localization and mapping (SLAM) in a cooperative context. Despite this interest, the scalability and diversity of existing datasets for collaborative trajectories remain limited, especially in scenarios with constrained perspectives where the generalization capabilities of Collaborative SLAM (C-SLAM) are critical for the feasibility of multi-agent missions. Addressing this gap, we introduce S3E, an expansive multimodal dataset. Captured by a fleet of unmanned ground vehicles traversing four distinct collaborative trajectory paradigms, S3E encompasses 13 outdoor and 5 indoor sequences. These sequences feature meticulously synchronized and spatially calibrated data streams, including 360-degree LiDAR point cloud, high-resolution stereo imagery, high-frequency inertial measurement units (IMU), and Ultra-wideband (UWB) relative observations. Our dataset not only surpasses previous efforts in scale, scene diversity, and data intricacy but also provides a thorough analysis and benchmarks for both collaborative and individual SLAM methodologies.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11401-11408"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636540","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

Constrained Dirichlet Distribution Policy: Guarantee Zero Constraint Violation Reinforcement Learning for Continuous Robotic Control 受约束的 Dirichlet 分布策略：保证零违反约束的连续机器人控制强化学习

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490392

Jianming Ma;Zhanxiang Cao;Yue Gao

引用次数: 0

Physics-Guided Deep Learning Enabled Surrogate Modeling for Pneumatic Soft Robots 针对气动软机器人的物理引导式深度学习代理建模

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490258

Sameh I. Beaber;Zhen Liu;Ye Sun

{"title":"Physics-Guided Deep Learning Enabled Surrogate Modeling for Pneumatic Soft Robots","authors":"Sameh I. Beaber;Zhen Liu;Ye Sun","doi":"10.1109/LRA.2024.3490258","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490258","url":null,"abstract":"Soft robots, formulated by soft and compliant materials, have grown significantly in recent years toward safe and adaptable operations and interactions with dynamic environments. Modeling the complex, nonlinear behaviors and controlling the deformable structures of soft robots present challenges. This study aims to establish a physics-guided deep learning (PGDL) computational framework that integrates physical models into deep learning framework as surrogate models for soft robots. Once trained, these models can replace computationally expensive numerical simulations to shorten the computation time and enable real-time control. This PGDL framework is among the first to integrate first principle physics of soft robots into deep learning toward highly accurate yet computationally affordable models for soft robot modeling and control. The proposed framework has been implemented and validated using three different pneumatic soft fingers with different behaviors and geometries, along with two training and testing approaches, to demonstrate its effectiveness and generalizability. The results showed that the mean square error (MSE) of predicted deformed curvature and the maximum and minimum deformation at various loading conditions were as low as \u0000<inline-formula><tex-math>$10^{-4}$</tex-math></inline-formula>\u0000 mm\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000. The proposed PGDL framework is constructed from first principle physics and intrinsically can be applicable to various conditions by carefully considering the governing equations, auxiliary equations, and the corresponding boundary and initial conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11441-11448"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636297","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

Inferring Occluded Agent Behavior in Dynamic Games From Noise Corrupted Observations 从噪声破坏的观测结果推断动态游戏中的隐蔽代理行为

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490398

Tianyu Qiu;David Fridovich-Keil

引用次数: 0

Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems 自由初始化：多普勒激光雷达-惯性系统的无扫描、无运动和无对应初始化

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-01 DOI: 10.1109/LRA.2024.3490395

Mingle Zhao;Jiahao Wang;Tianxiao Gao;Chengzhong Xu;Hui Kong

{"title":"Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems","authors":"Mingle Zhao;Jiahao Wang;Tianxiao Gao;Chengzhong Xu;Hui Kong","doi":"10.1109/LRA.2024.3490395","DOIUrl":"https://doi.org/10.1109/LRA.2024.3490395","url":null,"abstract":"Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-inertial systems, leveraging both inertial sensors and Doppler LiDAR. The innovative FMCW Doppler LiDAR opens up a novel avenue for robotic sensing by capturing not only point range but also Doppler velocity via the intrinsic Doppler effect. By fusing point-wise Doppler velocity with inertial measurements under non-inertial kinematics, the proposed framework, Free-Init, eliminates reliance on motion undistortion of LiDAR scans, excitation motions, and map correspondences during the initialization phase. Free-Init is also plug-and-play compatible with typical LiDAR-inertial systems and is versatile to handle a wide range of initial motions when the system starts, including stationary, dynamic, and even violent motions. The embedded Doppler-inertial velocimeter ensures fast convergence and high-frequency performance, delivering outputs exceeding 10 kHz. Comprehensive experiments on diverse platforms and across myriad motion scenes validate the framework's effectiveness. The results demonstrate the superior performance of Free-Init, highlighting the necessity of fast, resilient, and dynamic initialization for online systems.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11329-11336"},"PeriodicalIF":4.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600423","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