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

Dynamic Shape Estimation of Tendon-Driven Soft Manipulators via Actuation Readings 通过执行读数动态估计肌腱驱动软机械手的形状

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511406

Daniel Feliu-Talegon;Anup Teejo Mathew;Abdulaziz Y. Alkayas;Yusuf Abdullahi Adamu;Federico Renda

{"title":"Dynamic Shape Estimation of Tendon-Driven Soft Manipulators via Actuation Readings","authors":"Daniel Feliu-Talegon;Anup Teejo Mathew;Abdulaziz Y. Alkayas;Yusuf Abdullahi Adamu;Federico Renda","doi":"10.1109/LRA.2024.3511406","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511406","url":null,"abstract":"Soft robotic systems pose a significant challenge for traditional modeling, estimation, and control approaches, primarily owing to their inherent complexity and virtually infinite degrees of freedom (DoFs). This work introduces an innovative method for dynamically estimating the states of tendon-actuated soft manipulators. Our technique merges the Geometric Variable-Strain (GVS) approach with a kinematic formula that links the length variation of tendons to the deformations of the manipulator and a nonlinear observer design based on state-dependent Riccati equation (SDRE). In our methodology, the soft links are represented by Cosserat rods, and the robot's geometry is parameterized by the strain field along its length. Consequently, its infinite dimensions can be described by utilizing multiple degrees of freedom, depending on the required precision. This enables us to estimate the states (pose and velocity) of tendon-actuated soft manipulators solely based on tendon displacements and actuator forces. Through simulation, we demonstrate the convergence of our estimation method across various DoFs and actuator numbers, revealing a trade-off between the number of DoFs and required actuators for observing system states. Furthermore, we validate our approach with an experimental prototype of 25 cm in length, achieving an average tip position error during dynamic motion of 1.79 cm —less than 7\u0000<inline-formula><tex-math>${%}$</tex-math></inline-formula>\u0000 of the overall body length.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"780-787"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821223","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

Soft Human-Robot Handover Using a Vision-Based Pipeline

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511415

Chiara Castellani;Enrico Turco;Valerio Bo;Monica Malvezzi;Domenico Prattichizzo;Gabriele Costante;Maria Pozzi

{"title":"Soft Human-Robot Handover Using a Vision-Based Pipeline","authors":"Chiara Castellani;Enrico Turco;Valerio Bo;Monica Malvezzi;Domenico Prattichizzo;Gabriele Costante;Maria Pozzi","doi":"10.1109/LRA.2024.3511415","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511415","url":null,"abstract":"Handing over objects is an essential task in human-robot collaborative scenarios. Previous studies have predominantly employed rigid grippers to perform the handover, focusing on generating grasps that avoid physical contact with people. In this paper, we present a vision-based open-palm handover solution where a soft robotic hand exploits contact with the human hand for improved grasp success and robustness. The human-robot physical interaction allows the robotic hand to slide over the human palm and firmly cage the object. The identification of the human hand plane and object pose is achieved through a versatile perception pipeline that exploits a single RGB-D camera. Through experimental trials, we show that the system achieves successful grasps over multiple objects with different geometries and textures. A comparative analysis assesses the robustness of the proposed \u0000<italic>soft</i>\u0000 handover method against a baseline approach. A study with 30 participants evaluates users' perception of human-robot interaction during the handover, highlighting the effectiveness and preference for the proposed pipeline.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"891-898"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844335","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

Modular Reinforcement Learning for a Quadrotor UAV With Decoupled Yaw Control

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511412

Beomyeol Yu;Taeyoung Lee

{"title":"Modular Reinforcement Learning for a Quadrotor UAV With Decoupled Yaw Control","authors":"Beomyeol Yu;Taeyoung Lee","doi":"10.1109/LRA.2024.3511412","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511412","url":null,"abstract":"This letter presents modular reinforcement learning (RL) frameworks for the low-level control of a quadrotor, with direct control of yawing motion. While traditional monolithic RL approaches have been successfully applied to real-world autonomous flight, they often struggle to precisely control both translational and yawing motions due to their distinct dynamic characteristics and coupling. Moreover, training a large-scale monolithic network typically requires extensive training data to achieve broad generalization. To address these issues, we decompose the quadrotor dynamics into translational and yaw subsystems and assign a dedicated modular RL agent to each. This design significantly improves performance, as each RL agent is trained for its specific purpose and integrated in a synergistic way. It further enhances robustness, as potential failures within one module have minimal impact on the other, promoting fault tolerance. These improvements are demonstrated through flight experiments achieved via zero-shot sim-to-real transfer, where it is shown that the proposed modular policies substantially enhance training efficiency, tracking performance, and adaptability to real-world conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"572-579"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821193","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

Gas Source Localization in Unknown Indoor Environments Using Dual-Mode Information-Theoretic Search

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511375

Seunghwan Kim;Jaemin Seo;Hongro Jang;Changseung Kim;Murim Kim;Juhyun Pyo;Hyondong Oh

{"title":"Gas Source Localization in Unknown Indoor Environments Using Dual-Mode Information-Theoretic Search","authors":"Seunghwan Kim;Jaemin Seo;Hongro Jang;Changseung Kim;Murim Kim;Juhyun Pyo;Hyondong Oh","doi":"10.1109/LRA.2024.3511375","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511375","url":null,"abstract":"This letter proposes a dual-mode planner for localizing gas sources using a mobile sensor in unknown indoor spaces. The complexity of indoor environments creates constraints on search paths, leading to situations where no valid paths can be generated, which are termed as dead end in this letter. The proposed dual-mode planner is designed to effectively address the dead end problem while maintaining efficient search paths. In addition, the absence of analytical dispersion models that can be used in unknown indoor environments presents another critical issue for indoor gas source localization (GSL). To address this, we present an indoor Gaussian dispersion model (IGDM) that can analytically model indoor gas dispersion without a complete map. Finally, we establish a GSL framework for indoor environments along with real-time mapping, utilizing the dual-mode planner and IGDM. This framework is validated in indoor scenarios with the realistic gas dispersion simulator. The simulation results show the high success rate of the proposed method, its ability to reduce search time, and its computational efficiency. Furthermore, through real-world experiments, we demonstrate the potential of the proposed approach as a practical solution, evidenced by its satisfactory performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"588-595"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821136","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

Equivariant IMU Preintegration With Biases: A Galilean Group Approach

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511424

Giulio Delama;Alessandro Fornasier;Robert Mahony;Stephan Weiss

{"title":"Equivariant IMU Preintegration With Biases: A Galilean Group Approach","authors":"Giulio Delama;Alessandro Fornasier;Robert Mahony;Stephan Weiss","doi":"10.1109/LRA.2024.3511424","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511424","url":null,"abstract":"This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discrete-time formulation of the IMU preintegration on \u0000<inline-formula><tex-math>${mathbf {Gal}(3) ltimes mathfrak {gal}(3)}$</tex-math></inline-formula>\u0000, the left-trivialization of the tangent group of the Galilean group \u0000<inline-formula><tex-math>$mathbf {Gal}(3)$</tex-math></inline-formula>\u0000. We define a novel preintegration error that geometrically couples the navigation states and the bias leading to lower linearization error. Our method improves in consistency compared to existing preintegration approaches which treat IMU biases as a separate state-space. Extensive validation against state-of-the-art methods, both in simulation and with real-world IMU data, implementation in the Lie++ library, and open-source code are provided.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"724-731"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821139","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

GeoRecon: Geometric Coherence for Online 3D Scene Reconstruction From Monocular Video

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511423

Yanmei Wang;Fupeng Chu;Zhi Han;Yandong Tang

{"title":"GeoRecon: Geometric Coherence for Online 3D Scene Reconstruction From Monocular Video","authors":"Yanmei Wang;Fupeng Chu;Zhi Han;Yandong Tang","doi":"10.1109/LRA.2024.3511423","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511423","url":null,"abstract":"Online 3D scene reconstruction from monocular video aims to incrementally recover 3D mesh from monocular RGB videos. It enables robots to accomplish tasks involving interactions with the environment. Due to the high memory consumption of 3D data, almost all existing methods adopt the coarse-to-fine architecture, in which the voxel is progressively sparsified and split across levels. However, these methods overlook alignment between different levels, resulting in poor geometric properties of the reconstructed scene. Furthermore, the whole framework relies on voxel features for supervision, lacking effective supervision of the image geometric features extracted by the feature extraction network. These geometric features are essential for further 3D scene reconstruction. To tackle the above problems, we propose GeoRecon, which achieves geometric coherent reconstruction through keyframe 2D representation self-regression and cross-level 3D voxel feature alignment. Specifically, for 2D image space, to alleviate the lack of supervision in 2D feature extraction, an image reconstruction self-supervision regression constraint is introduced on the input 2D keyframes to ensure that the extracted features can learn accurate geometric features and further voxel features. For 3D voxel features space, to achieve consistent alignment between different levels, the high-level voxel features are used to constrain low-level voxel features, and achieve alignment from coarse (i.e., low-level) voxel features to fine (i.e., high-level) voxel features. With the design of these two components, the proposed method effectively reconstructs the geometric structures of the scene. The experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"500-507"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821221","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

ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511372

Jingxi Xu;Runsheng Wang;Siqi Shang;Ava Chen;Lauren Winterbottom;To-Liang Hsu;Wenxi Chen;Khondoker Ahmed;Pedro Leandro La Rotta;Xinyue Zhu;Dawn M. Nilsen;Joel Stein;Matei Ciocarlie

{"title":"ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke","authors":"Jingxi Xu;Runsheng Wang;Siqi Shang;Ava Chen;Lauren Winterbottom;To-Liang Hsu;Wenxi Chen;Khondoker Ahmed;Pedro Leandro La Rotta;Xinyue Zhu;Dawn M. Nilsen;Joel Stein;Matei Ciocarlie","doi":"10.1109/LRA.2024.3511372","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511372","url":null,"abstract":"Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming. In this letter, we propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts (i.e., a given sequence of EMG signals). ChatEMG enables us to collect only a small dataset from the new condition, session, or subject and expand it with synthetic samples conditioned on prompts from this new context. ChatEMG leverages a vast repository of previous data via generative training while still remaining context-specific via prompting. Our experiments show that these synthetic samples are classifier-agnostic and can improve intent inferral accuracy for different types of classifiers. We demonstrate that our complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks. To the best of our knowledge, this is the first time an intent classifier trained partially on synthetic data has been deployed for functional control of an orthosis by a stroke survivor.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"907-914"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858903","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

VLM-Social-Nav: Socially Aware Robot Navigation Through Scoring Using Vision-Language Models VLM-Social-Nav：通过视觉语言模型评分实现社交感知机器人导航

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-12-04 DOI: 10.1109/LRA.2024.3511409

Daeun Song;Jing Liang;Amirreza Payandeh;Amir Hossain Raj;Xuesu Xiao;Dinesh Manocha

{"title":"VLM-Social-Nav: Socially Aware Robot Navigation Through Scoring Using Vision-Language Models","authors":"Daeun Song;Jing Liang;Amirreza Payandeh;Amir Hossain Raj;Xuesu Xiao;Dinesh Manocha","doi":"10.1109/LRA.2024.3511409","DOIUrl":"https://doi.org/10.1109/LRA.2024.3511409","url":null,"abstract":"We propose VLM-Social-Nav, a novel Vision-Language Model (VLM) based navigation approach to compute a robot's motion in human-centered environments. Our goal is to make real-time decisions on robot actions that are socially compliant with human expectations. We utilize a perception model to detect important social entities and prompt a VLM to generate guidance for socially compliant robot behavior. VLM-Social-Nav uses a VLM-based scoring module that computes a cost term that ensures socially appropriate and effective robot actions generated by the underlying planner. Our overall approach reduces reliance on large training datasets and enhances adaptability in decision-making. In practice, it results in improved socially compliant navigation in human-shared environments. We demonstrate and evaluate our system in four different real-world social navigation scenarios with a Turtlebot robot. We observe at least 27.38% improvement in the average success rate and 19.05% improvement in the average collision rate in the four social navigation scenarios. Our user study score shows that VLM-Social-Nav generates the most socially compliant navigation behavior.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"508-515"},"PeriodicalIF":4.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821194","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

IEEE Robotics and Automation Society Information

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-27 DOI: 10.1109/LRA.2024.3506175

引用次数: 0

IEEE Robotics and Automation Society Information 电气和电子工程师学会机器人与自动化协会信息

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-11-27 DOI: 10.1109/LRA.2024.3506177

引用次数: 0