IEEE Robotics and Automation Letters最新文献_第10页

Tightly Coupled SLAM With Imprecise Architectural Plans 紧耦合SLAM与不精确的建筑计划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-23 DOI: 10.1109/LRA.2025.3582108

Muhammad Shaheer;Jose Andres Millan-Romera;Hriday Bavle;Marco Giberna;Jose Luis Sanchez-Lopez;Javier Civera;Holger Voos

{"title":"Tightly Coupled SLAM With Imprecise Architectural Plans","authors":"Muhammad Shaheer;Jose Andres Millan-Romera;Hriday Bavle;Marco Giberna;Jose Luis Sanchez-Lopez;Javier Civera;Holger Voos","doi":"10.1109/LRA.2025.3582108","DOIUrl":"https://doi.org/10.1109/LRA.2025.3582108","url":null,"abstract":"Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While some SLAM algorithms leverage these plans for global localization in real-world environments, they typically overlook a critical challenge: the “as-planned” architectural designs frequently deviate from the “as-built” real-world environments. To address this gap, we present a novel algorithm that tightly couples LIDAR-based simultaneous localization and mapping with architectural plans in the presence of deviations. Our method utilizes a multi-layered semantic representation to not only localize the robot, but also to estimate global alignment and structural deviations between “as-planned” and “as-built” environments in real-time. To validate our approach, we performed experiments in simulated and real datasets demonstrating robustness to structural deviations up to 35 cm and <inline-formula><tex-math>$15^circ$</tex-math></inline-formula>. On average, our method achieves 43% less localization error than baselines in simulated environments, while in real environments, the “as-built” 3D maps show 7% lower average alignment error.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8019-8026"},"PeriodicalIF":4.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519468","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

Data-Driven Compliance Discrimination via Biomimetic Soft Optical Tactile Sensors: Implementation and Benchmarking With a Model-Based Approach 基于仿生软光学触觉传感器的数据驱动顺应性判别：基于模型方法的实现和基准测试

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-23 DOI: 10.1109/LRA.2025.3582538

Paolo Susini;Giulia Pagnanelli;Saekwang Nam;Nathan F. Lepora;Matteo Bianchi

{"title":"Data-Driven Compliance Discrimination via Biomimetic Soft Optical Tactile Sensors: Implementation and Benchmarking With a Model-Based Approach","authors":"Paolo Susini;Giulia Pagnanelli;Saekwang Nam;Nathan F. Lepora;Matteo Bianchi","doi":"10.1109/LRA.2025.3582538","DOIUrl":"https://doi.org/10.1109/LRA.2025.3582538","url":null,"abstract":"Humans can easily manipulate soft, deformable objects, relying on the intrinsic deformability of theirfingerpads and their capabilities to infer item compliance. Transferring these skills into robotic systems is still an open challenging task. Recently, the introduction of soft biomimetic tactile sensors such as the TacTip, which aims at mimicking the structure of human skin layers, has represented an attempt to bridge this gap. However, while the advancement in endowing artificial systems with bio-aware embodied intelligence, the computational aspects of compliance estimation are still largely unexplored, mostly relying on Data-Driven (DD) methods. In a previous work [Pagnanelli, et al. (2023)], a model-based approach to compliance estimation by combining TacTip with a computational model of human tactile perception has been proposed. However, both these categories of techniques suffer from limitations (generalizability issues for the model-inspired; the curse of data for DD), suggesting that a hybrid approach could represent a more suitable solution. The first step in exploring the feasibility of a hybrid framework is to develop robust neural network architectures and then benchmark them against the results achieved through the analytical method. In this manner, we can assess their complementary strengths and potential integration. This work specifically addresses this step by proposing a novel approach for compliance estimation via TacTip using deep learning techniques. The neural network architecture was validated, and a comparative analysis of its performance against our previously developed model-based approach was performed.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8083-8090"},"PeriodicalIF":4.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536385","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

Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven DLO Manipulation 视觉驱动DLO操作中以对象为中心的Agent自适应的Real2Sim2Real分布处理

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-20 DOI: 10.1109/LRA.2025.3581744

Georgios Kamaras;Subramanian Ramamoorthy

{"title":"Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven DLO Manipulation","authors":"Georgios Kamaras;Subramanian Ramamoorthy","doi":"10.1109/LRA.2025.3581744","DOIUrl":"https://doi.org/10.1109/LRA.2025.3581744","url":null,"abstract":"We present an integrated (or end-to-end) framework for the Real2Sim2Real problem of manipulating deformable linear objects (DLOs) based on visual perception. Working with a parameterised set of DLOs, we use likelihood-free inference (LFI) to compute the posterior distributions for the physical parameters using which we can approximately simulate the behaviour of each specific DLO. We use these posteriors for domain randomisation while training, in simulation, object-specific visuomotor policies (i.e. assuming only visual and proprioceptive sensory) for a DLO reaching task, using model-free reinforcement learning. We demonstrate the utility of this approach by deploying sim-trained DLO manipulation policies in the real world in a zero-shot manner, i.e. without any further fine-tuning. In this context, we evaluate the capacity of a prominent LFI method to perform fine classification over the parametric set of DLOs, using only visual and proprioceptive data obtained in a dynamic manipulation trajectory. We then study the implications of the resulting domain distributions in sim-based policy learning and real-world performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8075-8082"},"PeriodicalIF":4.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536534","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

OverlapMamba: A Shift State Space Model for LiDAR-Based Place Recognition 重叠曼巴：一种基于激光雷达位置识别的移位状态空间模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-20 DOI: 10.1109/LRA.2025.3582109

Jiehao Luo;Jintao Cheng;Qiuchi Xiang;Jin Wu;Rui Fan;Xieyuanli Chen;Xiaoyu Tang

{"title":"OverlapMamba: A Shift State Space Model for LiDAR-Based Place Recognition","authors":"Jiehao Luo;Jintao Cheng;Qiuchi Xiang;Jin Wu;Rui Fan;Xieyuanli Chen;Xiaoyu Tang","doi":"10.1109/LRA.2025.3582109","DOIUrl":"https://doi.org/10.1109/LRA.2025.3582109","url":null,"abstract":"Place recognition is the foundation for autonomous systems to achieve independent decision-making and secure operation. It is also crucial in tasks such as loop closure detection and global localization in Simultaneous Localization and Mapping (SLAM) technology. Existing LiDAR-based place recognition (LPR) methods use raw point cloud representations or multifarious point cloud representations as inputs, as well as employ convolutional neural networks or transformer architectures. However, the recently proposed Mamba deep learning model combined with State Space Models (SSMs) has enormous potential in long sequence modeling. Therefore, we have developed a novel place recognition network OverlapMamba, which represents input range images as sequences. In a novel way, we use a stochastic reconstruction method to establish shifted state space models to compress the visual representation. Extensive experiments on three public datasets demonstrate that OverlapMamba achieves competitive performance with real-time inference speed, which effectively detects loop closure even when traversing previously visited locations from different directions, indicating its strong place recognition ability and real-time efficiency.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8380-8387"},"PeriodicalIF":4.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597759","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

Optimization of Preemptive Impact Mitigation Without Prior Collision Testing 无预先碰撞测试的先发制人冲击缓解优化

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-20 DOI: 10.1109/LRA.2025.3582137

Hayato Nakamura;Hikaru Arita;Shunsuke Tokiwa;Kenji Tahara

{"title":"Optimization of Preemptive Impact Mitigation Without Prior Collision Testing","authors":"Hayato Nakamura;Hikaru Arita;Shunsuke Tokiwa;Kenji Tahara","doi":"10.1109/LRA.2025.3582137","DOIUrl":"https://doi.org/10.1109/LRA.2025.3582137","url":null,"abstract":"Effective impact mitigation strategies are crucial for preventing potential damage to both robotic systems and their operational environments during high-velocity and dynamic maneuvers, as well as during the execution of high-precision tasks. The successful implementation of impact mitigation strategies in real-world applications fundamentally requires appropriate parameter tuning. However, owing to the destructive nature of collisions, heuristic parameter tuning is impractical, as it risks damage to both the robotic system and its operational environment during experimental trials. This study eliminates the need for preliminary collision experiments in parameter optimization by introducing a novel methodology that leverages recent proximity sensor-based preemptive impact mitigation strategies that reframe impact mitigation as a geometric rather than physical problem. The key innovation of this work lies in the reformulation of the proximity sensor output to enable both the analytical derivation of preemptive motion trajectories and the direct application of standard optimization solvers. The effectiveness of the proposed methodology is validated through numerical simulations and two different experimental configurations. By eliminating the need for collision trials, robotic systems can safely execute potentially destructive tasks that would otherwise result in system damage without proper impact mitigation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8059-8066"},"PeriodicalIF":4.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519465","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

Learning Behaviours for Decentralised Multi-Robot Collision Avoidance in Constrained Pathways Using Curriculum Reinforcement Learning 基于课程强化学习的约束路径下分散多机器人避碰学习行为

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-19 DOI: 10.1109/LRA.2025.3581430

Md Mostafizur Rahman Komol;Brendan Tidd;Will Browne;Frederic Maire;Jason Williams;David Howard

{"title":"Learning Behaviours for Decentralised Multi-Robot Collision Avoidance in Constrained Pathways Using Curriculum Reinforcement Learning","authors":"Md Mostafizur Rahman Komol;Brendan Tidd;Will Browne;Frederic Maire;Jason Williams;David Howard","doi":"10.1109/LRA.2025.3581430","DOIUrl":"https://doi.org/10.1109/LRA.2025.3581430","url":null,"abstract":"Mobile robot teams often require decentralised autonomous navigation through narrow gaps in limited communication environments (e.g., underground search-and-rescue operations). Existing navigation approaches exhibit suboptimal performance for avoiding multi-robot collisions in such bottlenecks due to an inability to address the dynamic nature of the robots. Initial work utilising reinforcement learning has demonstrated success in navigating a single robot through narrow gaps. However, when training agents to produce give-way behaviour for navigating through constrained gaps, end-to-end reinforcement learning using simple rewards suffers from slow convergence due to the increased search space of viable policies. This paper introduces a novel curriculum reinforcement learning framework, incorporating a <italic>multi-robot bootstrap curriculum</i> with preprogrammed behaviour to guide initial policy formation, subsequently refined by a <italic>gap curriculum</i> that progressively reduces training complexity towards an optimal policy. This framework learns multi-robot interaction behaviours, which are impractical to program manually. Our model achieves a 99% success-rate in give-way behaviour generation without inter-agent communications in high-fidelity simulations. The success-rate reduced to 73% in simulations incorporating noisy sensors, and 60% in field-robot tests, substantiating our model's practical viability despite sensor noise and real-world uncertainties. The simple benchmark methods lack efficiency in basic interaction behaviours.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8538-8545"},"PeriodicalIF":4.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634880","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

Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements 基于动态聚类协议的多智能体多资源分配的分散强化学习

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-19 DOI: 10.1109/LRA.2025.3581126

Antonio Marino;Esteban Restrepo;Claudio Pacchierotti;Paolo Robuffo Giordano

引用次数: 0

Bioinspired Microrobot Climbing on Fabrics Using a Single Actuator 仿生微型机器人在织物上爬行，使用单个驱动器

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-19 DOI: 10.1109/LRA.2025.3581124

Jiliang Ma;Jun Peng;Yusen Ma;Kanglong Yuan;Ao Qin;Wenwu Zhu;Xuefeng Chen

引用次数: 0

I2D-LocX: An Efficient, Precise and Robust Method for Camera Localization in LiDAR Maps I2D-LocX：一种高效、精确、鲁棒的激光雷达地图相机定位方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-19 DOI: 10.1109/LRA.2025.3581122

Huai Yu;Xubo Zhu;Shu Han;Wen Yang;Gui-Song Xia

{"title":"I2D-LocX: An Efficient, Precise and Robust Method for Camera Localization in LiDAR Maps","authors":"Huai Yu;Xubo Zhu;Shu Han;Wen Yang;Gui-Song Xia","doi":"10.1109/LRA.2025.3581122","DOIUrl":"https://doi.org/10.1109/LRA.2025.3581122","url":null,"abstract":"Camera localization within LiDAR maps has gained significant attention due to its potential for accurate positioning with low-cost and lightweight sensors compared to LiDAR-based systems. However, existing methods often prioritize localization accuracy, sometimes compromising efficiency, which can limit their suitability for real-time applications. To address these issues, we propose I2D-LocX, a lightweight monocular camera localization framework with three branches, establishing pixel-level and feature-level constraints to enhance localization performance without increasing model complexity. Specifically, the main branch generates a flow map to represent pixel-point displacements. One auxiliary branch shares the same input as the main branch and employs an additional decoder to evaluate the confidence of the flow map. The other auxiliary branch leverages a zero-flow generated from the displacement-free input to guide feature matching, thereby enhancing localization robustness. Notably, both auxiliary branches share parameters with the main branch and are omitted during inference, ensuring computational efficiency. Extensive experiments on benchmark datasets, including KITTI-Odometry, Argoverse, Waymo, and nuScenes, show that I2D-LocX can achieve centimeter-level localization accuracy with about 37 ms inference time, greatly improving the localization performance for real-world applications.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7899-7906"},"PeriodicalIF":4.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481900","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

OmniNet: Omnidirectional Jumping Neural Network With Height-Awareness for Quadrupedal Robots 基于高度感知的四足机器人全向跳跃神经网络

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-19 DOI: 10.1109/LRA.2025.3580993

Yimin Han;Jiahui Zhang;Zeren Luo;Yingzhao Dong;Jinghan Lin;Liu Zhao;Shihao Dong;Peng Lu

引用次数: 0