IEEE Robotics and Automation Letters最新文献

IEEE Robotics and Automation Letters Information for Authors IEEE机器人与自动化作者信函信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712845

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IEEE Robotics and Automation Society Information IEEE机器人与自动化学会信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712843

引用次数: 0

IEEE Robotics and Automation Society Publication Information IEEE机器人与自动化协会出版信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712841

引用次数: 0

Lie-Algebraic Approach to Geometric Nonlinear $mathcal {H}_infty$ Inverse Optimal Attitude Tracking on $SO(3)$ 几何非线性的lie -代数方法$mathcal {H}_infty$逆最优姿态跟踪 $SO(3)$

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-04 DOI: 10.1109/LRA.2026.3700377

Junsik Kim;Youngjun Joo;Youngjin Choi

{"title":"Lie-Algebraic Approach to Geometric Nonlinear $mathcal {H}_infty$ Inverse Optimal Attitude Tracking on $SO(3)$","authors":"Junsik Kim;Youngjun Joo;Youngjin Choi","doi":"10.1109/LRA.2026.3700377","DOIUrl":"https://doi.org/10.1109/LRA.2026.3700377","url":null,"abstract":"This letter presents a geometric nonlinear <inline-formula><tex-math>$mathcal {H}_infty$</tex-math></inline-formula> inverse optimal attitude tracking framework formulated on <inline-formula><tex-math>$SO(3)$</tex-math></inline-formula> using its Lie algebra <inline-formula><tex-math>$mathfrak {so}(3)$</tex-math></inline-formula>. By adopting exponential coordinates <inline-formula><tex-math>$bm {xi }in mathfrak {so}(3)$</tex-math></inline-formula>, attitude errors are mapped directly onto the Lie algebra. This preserves a bijective, distortion-free relationship with the geodesic distance within the injectivity radius (<inline-formula><tex-math>$Vert bm {xi }Vert < pi$</tex-math></inline-formula>), while eliminating singularities and unwinding issues inherent in Euler angles and quaternions. A sliding-mode-inspired nonlinear <inline-formula><tex-math>$mathcal {H}_infty$</tex-math></inline-formula> controller is developed, with robust stability established using the exponential coordinates and a systematic tuning methodology provided to facilitate practical implementation. Quadrotor simulations demonstrate faster convergence in large-angle maneuvers by mitigating the vanishing error and sluggish response of conventional methods. Experiments on a robotic manipulator validate the tuning rule for the prescribed disturbance attenuation level <inline-formula><tex-math>$gamma$</tex-math></inline-formula>, showing that the peak tracking error scales as <inline-formula><tex-math>$mathcal {O}(gamma ^{2})$</tex-math></inline-formula> for small gains and <inline-formula><tex-math>$mathcal {O}(gamma)$</tex-math></inline-formula> for large gains. These results demonstrate the robustness and predictability of the proposed framework and the effectiveness of Lie algebra-based attitude control on <inline-formula><tex-math>$SO(3)$</tex-math></inline-formula>.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8920-8927"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442265","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

CPP: Cooperative Priority Planning for Multi-Agent Path Finding With Coordination and Action Durations 具有协调和行动持续时间的多智能体寻径的合作优先级规划

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693986

Zhengchen Li;Boyu Li;Weimin Wu;Dacheng Li

{"title":"CPP: Cooperative Priority Planning for Multi-Agent Path Finding With Coordination and Action Durations","authors":"Zhengchen Li;Boyu Li;Weimin Wu;Dacheng Li","doi":"10.1109/LRA.2026.3693986","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693986","url":null,"abstract":"Modern multi-agent systems are evolving toward scalability, heterogeneity, and collaboration, where agents are required to coordinate to perform shared tasks. However, existing Cooperative Multi-Agent Path Finding (Co-MAPF) research typically assumes instantaneous actions. In reality, individual and joint actions require non-negligible durations, which introduce additional state dimensions that significantly expand the conflict-resolution search space. This letter formulates the problem as Co-MAPF with Action Durations and proposes Cooperative Priority Planning (CPP). CPP maintains high-level constraints and a priority queue to serialize task allocation and optimize meeting vertex selection, while employing a low-level solver that integrates synchronization windows with Time-Constrained A* to couple safe action durations with conflict-free path planning. Experimental results demonstrate that CPP achieves near-linear empirical time complexity and superior scalability. It exhibits near-optimal path quality with a small optimality gap in small-scale scenarios, while scaling more effectively than existing Co-MAPF solvers to constrained scenarios. Moreover, it maintains high success rates in large-scale, constrained scenarios, and ablation studies validate the effectiveness of each component.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8976-8983"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442951","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

Force Estimation With Concentric-Tube Robots for Surgical Palpation 同心管机器人在外科触诊中的力估计

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701528

Yufei Wu;Joshua Gaston;Samuel Tobin;Lauren Branscombe;Caleb Rucker

{"title":"Force Estimation With Concentric-Tube Robots for Surgical Palpation","authors":"Yufei Wu;Joshua Gaston;Samuel Tobin;Lauren Branscombe;Caleb Rucker","doi":"10.1109/LRA.2026.3701528","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701528","url":null,"abstract":"This paper presents the design, modeling, and experimental verification of a deflection-based force-sensing probe in a concentric-tube robot for palpation during robotic surgery. The proposed method leverages the inherent elastic compliance of the robot and a quasi-static Cosserat rod model to estimate contact forces from real-time tip deflections measured by a 5-DOF magnetic tracker, eliminating the need for distal force sensors. The system was experimentally validated on the Virtuoso Endoscopy System (Virtuoso Surgical, Nashville, TN) across multiple palpation locations and directions, including experiments on compliant tissue phantoms. The proposed method achieved a mean absolute force error of approximately 0.05 N, evaluated using a leave-one-group-out cross-validation scheme across palpation locations. Simulation studies using a real-time deformable-tissue model further demonstrate the tool’s potential for automated tissue stiffness mapping, which is further demonstrated by stiffness mapping a tissue phantom to localize an embedded simulated tumor. These results establish a compact, sensor-efficient, and accurate framework for model-based force sensing in continuum robotic palpation, providing a foundation for future clinical applications in minimally invasive surgery.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8896-8903"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442983","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

Open-Vocabulary Semantic Segmentation for Dynamic 3D Scenes Using Scene Flow Estimation 基于场景流估计的动态3D场景开放词汇语义分割

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693993

You-Jun Li;Yu-Kai Lin;Bang-Shien Chen;Chih-Wei Huang;Jann-Long Chern;Ching-Cherng Sun

{"title":"Open-Vocabulary Semantic Segmentation for Dynamic 3D Scenes Using Scene Flow Estimation","authors":"You-Jun Li;Yu-Kai Lin;Bang-Shien Chen;Chih-Wei Huang;Jann-Long Chern;Ching-Cherng Sun","doi":"10.1109/LRA.2026.3693993","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693993","url":null,"abstract":"3D open-vocabulary semantic segmentation has shown great potential in applications such as autonomous driving and mixed reality. However, achieving accurate segmentation in dynamic environments remains challenging due to motion-induced inconsistencies. To address this issue, we incorporate scene flow as temporal information into a static semantic backbone to enhance semantic consistency and accuracy over time. Our method captures inter-frame motion cues from point cloud sequences and leverages them, together with a local clustering mechanism, to refine semantic label consistency in consecutive frames. Furthermore, we introduce a two-way scene flow-based data augmentation strategy that exploits both forward and backward motion to jointly train the model in bidirectional temporal contexts. On the large-scale nuScenes autonomous driving dataset, our method achieves a 0.4% overall improvement in hIoU and a 2.37% gain under high-motion scenes. On the synthetic object-centric dataset, it achieves a 4.53% overall hIoU improvement and a 6.09% gain in high-motion scenes, while reducing the ID switch rate by 0.5%.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8140-8147"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504507","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

Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality 基于拉格朗日对偶的GNSS和局部帧的可认证对准

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693943

Baoshan Song;Matthew Giamou;Penggao Yan;Chunxi Xia;Li-Ta Hsu

{"title":"Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality","authors":"Baoshan Song;Matthew Giamou;Penggao Yan;Chunxi Xia;Li-Ta Hsu","doi":"10.1109/LRA.2026.3693943","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693943","url":null,"abstract":"Estimating the absolute orientation of a local system relative to a global navigation satellite system (GNSS) reference often suffers from local minima and high dependency on satellite availability. Existing methods for this alignment task rely on abundant satellites unavailable in GNSS-degraded environments, or use local optimization methods which cannot guarantee the optimality of a solution. This work proposed a certifiable method, meaning it can numerically verify the optimality of the result, filling a gap where existing local optimizers fail. We first formulate the original Doppler-based frame alignment problem as a nonconvex quadratically constrained quadratic program (QCQP) problem and relax the QCQP problem to a concave Lagrangian dual problem that provides a lower cost bound for the original problem. Then we perform relaxation tightness and observability analysis to derive criteria for certifiable optimality of the solution. Finally, simulation and real world experiments are conducted to evaluate the proposed method. The experiments show that our method provides certifiably optimal solutions even with only 2 satellites with Doppler measurements and 2D vehicle motion, while the traditional velocity-based VOBA method and the advanced GVINS alignment technique may fail or converge to local optima without notice.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8196-8203"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508796","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

Three Percent is Enough: Semi-Supervised Martian Segmentation Labeling With Active Learning 百分之三是足够的：半监督火星分割标记与主动学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-14 DOI: 10.1109/LRA.2026.3693576

Muyao Li;Ruyi Zhou;Liang Ding;Huaiguang Yang;Haibo Gao;Zongquan Deng

{"title":"Three Percent is Enough: Semi-Supervised Martian Segmentation Labeling With Active Learning","authors":"Muyao Li;Ruyi Zhou;Liang Ding;Huaiguang Yang;Haibo Gao;Zongquan Deng","doi":"10.1109/LRA.2026.3693576","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693576","url":null,"abstract":"Accurate, large-scale Martian segmentation datasets are a cornerstone of autonomous scene understanding in support of exploration and navigation in Martian environments. However, high-quality segmentation labeling on planetary images requires annotators to have professional extraterrestrial geological knowledge, and even skilled annotators need a long time to label a single image in detail. In this paper, we propose a semi-automatic annotation method for Martian scene segmentation. By integrating a semi-supervised segmentation network architecture with an active learning strategy, our framework achieves near-fully supervised performance using a minimal amount of manual annotations, significantly reducing dependence on human experts. Experiments on the S<inline-formula><tex-math>$^{5}$</tex-math></inline-formula>Mars dataset show that our framework reaches 77.01% mIoU with only 3.13% of the manual annotations (169 images), corresponding to 92.23% of the performance (83.50% mIoU) of the official fully supervised model trained on 5400 labeled images. We also conducted an extended experiment on AI4Mars, where the proposed framework consistently achieved strong performance, exceeding the fully supervised baseline by 8.12% using only 3.12% of labeled data. This annotation ratio is substantially lower than the nearly 20% labeled data typically required by conventional semi-supervised approaches, highlighting the efficiency of our method for large-scale Martian scene annotation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8156-8163"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508923","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

Dynamic Policy Learning for Legged Robot With Simplified Model Pretraining and Model-Homotopy-Inspired Transfer 基于简化模型预训练和模型同伦启发迁移的腿式机器人动态策略学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692082

Dongyun Kang;Min-Gyu Kim;Tae-Gyu Song;Hajun Kim;Sehoon Ha;Hae-Won Park

{"title":"Dynamic Policy Learning for Legged Robot With Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","authors":"Dongyun Kang;Min-Gyu Kim;Tae-Gyu Song;Hajun Kim;Sehoon Ha;Hae-Won Park","doi":"10.1109/LRA.2026.3692082","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692082","url":null,"abstract":"Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges. However, the model discrepancy poses a significant challenge when transferring policies to full-body dynamics environments. In this work, we introduce a continuation-based learning framework that combines simplified model pretraining and model-homotopy-inspired transfer to efficiently generate and refine complex dynamic behaviors. First, we pretrain the policy using a single rigid body model to capture core motion patterns in a simplified environment. Next, we employ a continuation strategy to progressively transfer the policy to the full-body environment, minimizing performance loss. To define the continuation path, we introduce a parametric transition path from the single rigid body model to the full-body model by gradually redistributing mass and inertia between the trunk and legs. The proposed method achieves faster convergence and demonstrates superior stability during the transfer process compared to baseline methods. Our framework is validated on a range of dynamic tasks, including flips and wall-assisted maneuvers, and is successfully deployed on a real quadrupedal robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8068-8075"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509049","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