IEEE Robotics and Automation Letters最新文献_第9页

Efficiently Kinematic-Constraint-Coupled State Estimation for Integrated Aerial Platforms in GPS-Denied Environments

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI: 10.1109/LRA.2025.3536292

Ganghua Lai;Yushu Yu;Fuchun Sun;Jing Qi;Vincezo Lippiello

{"title":"Efficiently Kinematic-Constraint-Coupled State Estimation for Integrated Aerial Platforms in GPS-Denied Environments","authors":"Ganghua Lai;Yushu Yu;Fuchun Sun;Jing Qi;Vincezo Lippiello","doi":"10.1109/LRA.2025.3536292","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536292","url":null,"abstract":"Small-scale autonomous aerial vehicles (AAVs) are widely used in various fields. However, their underactuated design limits their ability to perform complex tasks that require physical interaction with environments. The fully-actuated Integrated Aerial Platforms (IAPs), where multiple AAVs are connected to a central platform via passive joints, offer a promising solution. However, achieving accurate state estimation for IAPs in GPS-denied environments remains a significant hurdle. In this letter, we introduce a centralized state estimation framework for IAPs with a fusion of odometry and kinematics, using only onboard cameras and inertial measurement units (IMUs). We develop a forward-kinematic-based formulation to fully leverage localization information from kinematic constraints. An online calibration method for kinematic parameters is proposed to enhance state estimation accuracy with forward kinematics. Additionally, we perform an observability analysis, theoretically proving that these kinematic parameters are fully observable under conditions of fully excited motion. Dataset and real-world experiments on a three-agent IAP prototype confirm that our method improves localization accuracy and reduces drift compared to the baseline.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2838-2845"},"PeriodicalIF":4.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396338","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

Human-Inspired Robotic Assembly for Multiple Peg-In/Out-Hole Tasks in On-Orbit Refueling

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI: 10.1109/LRA.2025.3536298

Rui Zhang;Qiang Zhang;Xiaodong Zhou

{"title":"Human-Inspired Robotic Assembly for Multiple Peg-In/Out-Hole Tasks in On-Orbit Refueling","authors":"Rui Zhang;Qiang Zhang;Xiaodong Zhou","doi":"10.1109/LRA.2025.3536298","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536298","url":null,"abstract":"On-orbit refueling technology requires using robots with multiple peg-in-hole and peg-out-hole capabilities. However, complex contact conditions can cause jamming, thus posing significant challenges to automated refueling. To address this shortcoming, this letter proposes a human-inspired multiple peg-in/out-hole assembly method. The proposed method integrates a variable admittance force controller based on a non-diagonal stiffness matrix and a strategy for handling multiple peg-in/out-hole operations. In addition, by coupling the position and orientation stiffness, a robot's adaptability in dynamic assembly environments is significantly enhanced. Moreover, the proposed method enables autonomous posture adjustment based on real-time force sensor data and allows a robot to retry operations in case of jamming, thus eliminating the need for complex motion trajectory planning. The results of the ground refueling experiments show that the proposed method can successfully complete the multiple peg-in/out-hole tasks and effectively resist external interference. The proposed method could be of valuable reference significance for on-orbit refueling tasks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2670-2677"},"PeriodicalIF":4.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361300","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

TLS-SLAM: Gaussian Splatting SLAM Tailored for Large-Scale Scenes

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI: 10.1109/LRA.2025.3536876

Sicong Cheng;Songyang He;Fuqing Duan;Ning An

{"title":"TLS-SLAM: Gaussian Splatting SLAM Tailored for Large-Scale Scenes","authors":"Sicong Cheng;Songyang He;Fuqing Duan;Ning An","doi":"10.1109/LRA.2025.3536876","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536876","url":null,"abstract":"3D Gaussian splatting (3DGS) has shown promise for fast and high-quality mapping in simultaneous localization and mapping (SLAM), but faces convergence challenges in large-scale scenes across three key aspects. Firstly, the excessive Gaussian points in 3DGS models for large-scale scenes make the search space of the model optimization process more complex, leading to local optima. Secondly, trajectory drift caused by long-term localization in large-scale scenes displaces Gaussian point cloud positions. Thirdly, dynamic objects commonly found in large-scale scenes produce numerous noise Gaussian points that disrupt gradient backpropagation. We propose TLS-SLAM to address these convergence challenges. To ensure large-scale scene map optimization attains the global optimal, we use scene memory features to encode and adaptively build sub-maps, dividing the optimization space into subspaces, which reduces the optimization complexity. To reduce trajectory drift, we use a pose update method guided by semantic information, ensuring accurate Gaussian point cloud creation. To mitigate the impact of dynamic objects, we utilize 3D Gaussian distributions to accurately extract, encode, and model dynamic objects from the scene, thereby avoiding the generation of noise points. Experiments on four datasets show that our method achieves strong performance in tracking, mapping, and rendering accuracy.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2814-2821"},"PeriodicalIF":4.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379534","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

LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI: 10.1109/LRA.2025.3536840

Weiyi Xiong;Zean Zou;Qiuchi Zhao;Fengchun He;Bing Zhu

引用次数: 0

CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI: 10.1109/LRA.2025.3536218

Tim Brödermann;Christos Sakaridis;Yuqian Fu;Luc Van Gool

{"title":"CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes","authors":"Tim Brödermann;Christos Sakaridis;Yuqian Fu;Luc Van Gool","doi":"10.1109/LRA.2025.3536218","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536218","url":null,"abstract":"Leveraging multiple sensors is crucial for robust semantic perception in autonomous driving, as each sensor type has complementary strengths and weaknesses. However, existing sensor fusion methods often treat sensors uniformly across all conditions, leading to suboptimal performance. By contrast, we propose a novel, <italic>condition-aware multimodal</i> fusion approach for robust semantic perception of driving scenes. Our method, CAFuser, uses an RGB camera input to classify environmental conditions and generate a <italic>Condition Token</i> that guides the fusion of multiple sensor modalities. We further newly introduce modality-specific feature adapters to align diverse sensor inputs into a shared latent space, enabling efficient integration with a single and shared pre-trained backbone. By dynamically adapting sensor fusion based on the actual condition, our model significantly improves robustness and accuracy, especially in adverse-condition scenarios. CAFuser ranks first on the public MUSES benchmarks, achieving 59.7 PQ for multimodal panoptic and 78.2 mIoU for semantic segmentation, and also sets the new state of the art on DeLiVER.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3134-3141"},"PeriodicalIF":4.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455247","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

IMU-Aided Geographic Pose Estimation Method for UAVs Using Satellite Imageries Matching

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-29 DOI: 10.1109/LRA.2025.3536285

Yongfei Li

引用次数: 0

Prediction of Delay-Free Scene for Quadruped Robot Teleoperation: Integrating Delayed Data With User Commands

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-29 DOI: 10.1109/LRA.2025.3536222

Seunghyeon Ha;Seongyong Kim;Soo-Chul Lim

{"title":"Prediction of Delay-Free Scene for Quadruped Robot Teleoperation: Integrating Delayed Data With User Commands","authors":"Seunghyeon Ha;Seongyong Kim;Soo-Chul Lim","doi":"10.1109/LRA.2025.3536222","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536222","url":null,"abstract":"Teleoperation systems are utilized in various controllable systems, including vehicles, manipulators, and quadruped robots. However, during teleoperation, communication delays can cause users to receive delayed feedback, which reduces controllability and increases the risk faced by the remote robot. To address this issue, we propose a delay-free video generation model based on user commands that allows users to receive real-time feedback despite communication delays. Our model predicts delay-free video by integrating delayed data (video, point cloud, and robot status) from the robot with the user's real-time commands. The LiDAR point cloud data, which is part of the delayed data, is used to predict the contents of areas outside the camera frame during robot rotation. We constructed our proposed model by modifying the transformer-based video prediction model VPTR-NAR to effectively integrate these data. For our experiments, we acquired a navigation dataset from a quadruped robot, and this dataset was used to train and test our proposed model. We evaluated the model's performance by comparing it with existing video prediction models and conducting an ablation study to verify the effectiveness of its utilization of command and point cloud data.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2846-2853"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396337","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

DecTrain: Deciding When to Train a Monocular Depth DNN Online

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-29 DOI: 10.1109/LRA.2025.3536206

Zih-Sing Fu;Soumya Sudhakar;Sertac Karaman;Vivienne Sze

{"title":"DecTrain: Deciding When to Train a Monocular Depth DNN Online","authors":"Zih-Sing Fu;Soumya Sudhakar;Sertac Karaman;Vivienne Sze","doi":"10.1109/LRA.2025.3536206","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536206","url":null,"abstract":"Deep neural networks (DNNs) can deteriorate in accuracy when deployment data differs from training data. While performing online training at all timesteps can improve accuracy, it is computationally expensive. We propose DecTrain, a new algorithm that decides when to train a monocular depth DNN online using self-supervision with low overhead. To make the decision at each timestep, DecTrain compares the cost of training with the predicted accuracy gain. We evaluate DecTrain on out-of-distribution data, and find DecTrain maintains accuracy compared to online training at all timesteps, while training only 44% of the time on average. We also compare the recovery of a low inference cost DNN using DecTrain and a more generalizable high inference cost DNN on various sequences. DecTrain recovers the majority (97%) of the accuracy gain of online training at all timesteps while reducing computation compared to the high inference cost DNN which recovers only 66%. With an even smaller DNN, we achieve 89% recovery while reducing computation by 56%. DecTrain enables low-cost online training for a smaller DNN to have competitive accuracy with a larger, more generalizable DNN at a lower overall computational cost.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2822-2829"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396277","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

Greedy-DAgger - A Student Rollout Efficient Imitation Learning Algorithm

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-29 DOI: 10.1109/LRA.2025.3536297

Mitchell Torok;Mohammad Deghat;Yang Song

{"title":"Greedy-DAgger - A Student Rollout Efficient Imitation Learning Algorithm","authors":"Mitchell Torok;Mohammad Deghat;Yang Song","doi":"10.1109/LRA.2025.3536297","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536297","url":null,"abstract":"Sampling-based model predictive control algorithms can be computationally expensive and may not be feasible for restricted platforms such as quadcopters. Comparatively speaking, lightweight learned controllers are computationally cheaper and may be more suited for these platforms. Expert control samples provided by a remote model predictive control algorithm could be used to rapidly train a student policy. We present Greedy-DAgger, a hybrid-policy imitation learning approach that leverages expert simulations to improve the student rollout efficiency during the training of a student policy. Our approach builds on the DAgger algorithm by employing a greedy strategy, that selects isolated states from a student trajectory. These states are used to generate expert trajectory samples before supervised learning is performed and the process is repeated. The effectiveness of the Greedy-DAgger algorithm is evaluated on two simulated robotic systems: a cart pole and a quadcopter. For these environments, Greedy-DAgger was shown to be up to ten times more rollout efficient than conventional DAgger. The introduced improvements enable expert-level quadcopter control to be achieved within 8 seconds of wall time. The Crazyflie quadcopter platform was then utilised to validate the simulation results and demonstrate the potential for real-world training with Greedy-DAgger on a constrained platform, leveraging access to a remote GPU-accelerated server.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2878-2885"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403970","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

Delayed Dynamic Model Scheduled Reinforcement Learning With Time-Varying Delays for Robotic Control

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-29 DOI: 10.1109/LRA.2025.3536291

Zechang Wang;Dengpeng Xing;Yiming Yang;Peng Wang

{"title":"Delayed Dynamic Model Scheduled Reinforcement Learning With Time-Varying Delays for Robotic Control","authors":"Zechang Wang;Dengpeng Xing;Yiming Yang;Peng Wang","doi":"10.1109/LRA.2025.3536291","DOIUrl":"https://doi.org/10.1109/LRA.2025.3536291","url":null,"abstract":"Reinforcement learning (RL) typically presupposes instantaneous agent-environment interactions, but in real-world scenarios such as robotic control, overlooking observation delays can significantly impair performance. While existing studies consider stationary, known delays, real-world applications frequently encounter unpredictable delay variations. To address this problem, this letter presents a novel algorithm for scheduling delayed dynamic models. Specifically, We propose using multiple truncated delay distributions to effectively model time-varying delays, with each distribution tailored to learn a specific delayed dynamic model. These models map delayed observations and historical actions to the current state, integrating seamlessly with existing RL algorithms to facilitate optimal decision-making. Since the delay is unknown to the agent, we propose an effective delay estimation method to detect delay and their corresponding distributions in real-time, thereby adaptively selecting the most appropriate delayed dynamic model to manage delays. To reduce instability caused by abrupt changes in delay distribution and enhance responsiveness to such variations, we apply Bayesian online changepoint detection to enable rapid sensing of alterations in the delay distribution within a finite number of time-steps. To the best of our knowledge, our approach is the first effective solution to the non-stationary time-varying delay problem in RL. Empirical results demonstrate the robust performance of our method in scenarios characterized by non-stationary observation delays, highlighting its strong potential for robotic control applications.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2646-2653"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361295","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