IEEE Robotics and Automation Letters最新文献

{"title":"Tissue-View Map for Robotic Carotid Artery Ultrasound Scanning Using Reinforcement Learning","authors":"Kang Su;Guanglong Du;Xueqian Wang;Quanlong Guan","doi":"10.1109/LRA.2025.3555865","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555865","url":null,"abstract":"Ultrasound is an important diagnostic modality in medicine, offering real-time imaging, no radiation and low cost. However, ultrasound is currently highly dependent on the operator's experience and technical skills. Robotic autonomous ultrasound scanning (RAUS) is a sequential decision-making problem, requiring continuous decisions based on the current state and environment. Recently, reinforcement learning (RL) has made significant progress in solving such challenges across various domains. Nevertheless, most studies directly use raw ultrasound images as input to end-to-end networks. The noise and high-dimensional features in these images increase both network complexity and the number of parameters. In this letter, we propose a tissue-view map representation to facilitate model-free deep reinforcement learning for robotic carotid artery scanning. The tissue-view map captures the interaction between the probe and the skin, highlighting the scanned object while considering the surrounding tissues. A variational autoencoder is then employed to encode the features of the tissue-view map and further reduce dimensionality. Finally, we adopted proximal policy optimization to learn the policy for probe adjustment in carotid artery scanning. Our experiments demonstrate that the proposed method outperforms existing approaches and effectively handles the tasks of object search, contact control, and image quality optimization in real-world scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"5178-5185"},"PeriodicalIF":4.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840092","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}

{"title":"Co-Fix3D: Enhancing 3D Object Detection With Collaborative Refinement","authors":"Wenxuan Li;Qin Zou;Chi Chen;Bo Du;Long Chen;Jian Zhou;Hongkai Yu","doi":"10.1109/LRA.2025.3555859","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555859","url":null,"abstract":"3D object detection in driving scenarios is particularly challenging due to factors such as sensor noise, occlusions, and the inherent sparsity of LiDAR point clouds, which can lead to the loss or incompleteness of key features, in turn affecting perception performance. To address these challenges, we propose Co-Fix3D, an advanced detection framework that integrates Local and Global Enhancement (LGE) modules to refine Bird's Eye View (BEV) features. The LGE module employs Discrete Wavelet Transform (DWT) to refine local features at a fine scale, which helps capture frequency details and subtle variations in the environment, and incorporates an attention mechanism to enhance global feature representations across the entire scene. Moreover, we adopt multi-head LGE modules that each concentrate on targets with varying levels of detection difficulty, further improving our overall perception performance. On the nuScenes dataset, Co-Fix3D achieves a new SOTA performance with 69.4% mAP and 73.5% NDS compared to other competing methods, while on the multimodal benchmark, it achieves 72.3% mAP and 74.7% NDS, respectively.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4970-4977"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808922","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}

{"title":"EPIC: A Lightweight LiDAR-Based AAV Exploration Framework for Large-Scale Scenarios","authors":"Shuang Geng;Zelin Ning;Fu Zhang;Boyu Zhou","doi":"10.1109/LRA.2025.3555878","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555878","url":null,"abstract":"Autonomous exploration is a fundamental problem for various applications of autonomous aerial vehicles (AAVs). Recently, LiDAR-based exploration has gained significant attention due to its ability to generate high-precision point cloud maps of large-scale environments. While the point clouds are inherently informative for navigation, many existing exploration methods still rely on additional, often expensive, environmental representations. This reliance stems from two main reasons: the need for frontier detection or information gain computation, which typically depends on memory-intensive occupancy grid maps, and the high computational complexity of path planning directly on point clouds, primarily due to costly collision checking. To address these limitations, we present EPIC, a lightweight LiDAR-based AAV exploration framework that directly exploits point cloud data to explore large-scale environments. EPIC introduces a novel observation map based on the quality of point clouds, treating the environment as a collection of small surface patches and evaluating their observation quality. It maintains and updates this quality using spatial hashing. By guiding the AAV from well-observed to poorly-observed areas, EPIC eliminates the need for global occupancy grid maps, while ensuring robust exploration and effective performance across diverse environments. We also propose an incremental topological graph construction method operating directly on point clouds, enabling real-time path planning in large-scale environments. Leveraging these components, we build a hierarchical planning framework that generates agile and energy-efficient trajectories, achieving significantly reduced memory consumption and computation time compared to most existing methods. Extensive simulations and real-world experiments demonstrate that EPIC achieves faster exploration while significantly reducing memory consumption compared to state-of-the-art methods.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"5090-5097"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856240","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}

{"title":"Exploring the Simplification Limit of Deep Network Features With Subway Positioning Task","authors":"Jiajie Song;Ningfang Song;Jingchun Cheng;Xiaoxin Liu;Xiong Pan","doi":"10.1109/LRA.2025.3555790","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555790","url":null,"abstract":"This paper addresses vision-based subway positioning, a significant yet challenging task due to the low-lighting and sparse-texture conditions in tunnels. Traditional features struggle with temporal correspondence. While deep network features are effective, their computational and storage demands make them unsuitable for on-board systems. We propose a simple-structured feature extractor, trained via a student-teacher distillation framework to inherit the powerful pattern mining and abstraction capabilities of deep networks. Our goal is to simplify deep network features for fixed-route applications like subway positioning, developing an on-board efficient feature extractor for practical applications. Specifically, we design a single-layer convolution operator as our student network. Through discriminability augmented distillation, we compress the feature extraction capabilities of the state-of-the-art SiLK into this compact model, achieving an optimal balance between descriptive power and computational efficiency. Our method achieves a model size of 2 KB and a processing speed of 1453 FPS, while maintaining high homography estimation accuracy comparable to those of deep network features. Extensive experiments on the vision-based subway positioning dataset show our method offers superior speed and deployability without losing accuracy.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4922-4929"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792792","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}

{"title":"Intent Prediction-Driven Model Predictive Control for UAV Planning and Navigation in Dynamic Environments","authors":"Zhefan Xu;Hanyu Jin;Xinming Han;Haoyu Shen;Kenji Shimada","doi":"10.1109/LRA.2025.3555937","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555937","url":null,"abstract":"Aerial robots can enhance construction site productivity by autonomously handling inspection and mapping tasks. However, ensuring safe navigation near human workers remains challenging. While navigation in static environments has been well studied, navigating dynamic environments remains open due to challenges in perception and planning. Payload limitations restrict the robots to using cameras with limited fields of view, resulting in unreliable perception and tracking during collision avoidance. Moreover, the rapidly changing conditions of dynamic environments can quickly make the generated optimal trajectory outdated.To address these challenges, this letter presents a comprehensive navigation framework that integrates perception, intent prediction, and planning. Our perception module detects and tracks dynamic obstacles efficiently and handles tracking loss and occlusion during collision avoidance. The proposed intent prediction module employs a Markov Decision Process (MDP) to forecast potential actions of dynamic obstacles with the possible future trajectories. Finally, a novel intent-based planning algorithm, leveraging model predictive control (MPC), is applied to generate navigation trajectories. Simulation and physical experiments demonstrate that our method improves the safety of navigation by achieving the fewest collisions compared to benchmarks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4946-4953"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808869","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}

{"title":"Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments","authors":"Minjun Sung;Hunmin Kim;Naira Hovakimyan","doi":"10.1109/LRA.2025.3555877","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555877","url":null,"abstract":"Uncertainties in the environment and behavior model inaccuracies compromise the state estimation of a dynamic obstacle and its trajectory predictions, introducing biases in estimation and shifts in predictive distributions. In this letter, we propose a novel algorithm <monospace>SIED-MPC</monospace>, which synergistically integrates Simultaneous State and Input Estimation (SSIE) and Distributionally Robust Model Predictive Control (DR-MPC) using model confidence evaluation. The SSIE process produces unbiased state estimates and optimal <italic>input gap</i> estimates to assess the confidence of the behavior model, defining the ambiguity radius for DR-MPC to handle predictive distribution shifts. The proposed method produces safe inputs with an adequate level of conservatism. Our algorithm demonstrated a reduced collision rate and computation time in autonomous driving simulations through improved state estimation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4962-4969"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808923","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}

{"title":"A Generic Service-Oriented Function Offloading Framework for Connected Automated Vehicles","authors":"Robin Dehler;Michael Buchholz","doi":"10.1109/LRA.2025.3555939","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555939","url":null,"abstract":"Function offloading is a promising solution to address limitations concerning computational capacity and available energy of Connected Automated Vehicles (CAVs) or other autonomous robots by distributing computational tasks between local and remote computing devices in form of distributed services. This paper presents a generic function offloading framework that can be used to offload an arbitrary set of computational tasks with a focus on autonomous driving. To provide flexibility, the function offloading framework is designed to incorporate different offloading decision making algorithms and quality of service (QoS) requirements that can be adjusted to different scenarios or the objectives of the CAVs. With a focus on the applicability, we propose an efficient location-based approach, where the decision whether tasks are processed locally or remotely depends on the location of the CAV. We apply the proposed framework on the use case of service-oriented trajectory planning, where we offload the trajectory planning task of CAVs to a Multi-Access Edge Computing (MEC) server. The evaluation is conducted in both simulation and real-world application. It demonstrates the potential of the function offloading framework to guarantee the QoS for trajectory planning while improving the computational efficiency of the CAVs. Moreover, the simulation results also show the adaptability of the framework to diverse scenarios involving simultaneous offloading requests from multiple CAVs.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"5098-5105"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856239","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}

{"title":"Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles With Deep Reinforcement Learning","authors":"Alejandro Mendoza Barrionuevo;Samuel Yanes Luis;Daniel Gutiérrez Reina;Sergio L. Toral Marín","doi":"10.1109/LRA.2025.3555940","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555940","url":null,"abstract":"This letter presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep reinforcement approach, allowing agents to learn strategies to maximize cleaning efficiency. The primary objective is for the scout team to provide an up-to-date contamination model, while the cleaner team collects as much waste as possible following this model. This strategy leads to heterogeneous teams that optimize fleet efficiency through inter-team cooperation supported by a tailored reward function. Different trainings of the proposed algorithm are compared with other state-of-the-art algorithms in three distinct scenarios, one with moderate convexity, another with narrow corridors and challenging access, and the last one larger, more complex and with more difficult to access shape. According to the obtained results, it is demonstrated that deep reinforcement learning based algorithms outperform baselines, exhibiting superior adaptability. In addition, training with examples of actions from other algorithms further improves performance, especially in scenarios where the search space is larger.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4930-4937"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808868","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}

{"title":"Virtual Masses Description Forming Conservative System for Periodic Trajectory Modification of Robust Intermittent Controller","authors":"Hirofumi Shin;Yuki Koyama;Takumi Kamioka","doi":"10.1109/LRA.2025.3555898","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555898","url":null,"abstract":"An intermittent controller, which alternates between phases of no control and feedback control, provides a robust, computationally and energetically efficient method by leveraging the robot's passive dynamics. However, this approach, especially during rapid motions and when facing disturbances, becomes unstable due to model errors during the single support phase; this is the no-control phase. This letter proposes a novel multi-mass model that enables intermittent controllers to reduce model errors by periodically modifying the trajectory for the no-control phase. To this end, we introduce a virtual masses description that enables trajectory modification, forming a conservative system. Its trajectory is adjusted directly before the no-control phase, ensuring stability against disturbances. As a result, robots using our model demonstrated robust walking in both simulations and hardware experiments, even when faced with varying external disturbances and obstacles, while also exhibiting improved efficiency.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4954-4961"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808884","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}

{"title":"CaRoSaC: A Reinforcement Learning-Based Kinematic Control of Cable-Driven Parallel Robots by Addressing Cable Sag Through Simulation","authors":"Rohit Dhakate;Thomas Jantos;Eren Allak;Stephan Weiss;Jan Steinbrener","doi":"10.1109/LRA.2025.3555886","DOIUrl":"https://doi.org/10.1109/LRA.2025.3555886","url":null,"abstract":"This letter introduces the Cable Robot Simulation and Control (CaRoSaC) Framework, which integratesa realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach seeks to bridge the knowledge gap of the intricacies of CDPRs due to aspects such as cable sag and precision control necessities, which are missing in existing research and often overlooked in traditional models, by establishing a simulation platform that captures the real-world behaviors of CDPRs, including the impacts of cable sag. The framework offers researchers and developers a tool to further develop estimation and control strategies within the simulation for understanding and predicting the performance nuances, especially in complex operations where cable sag can be significant. Using this simulation framework, we train a model-free control policy rooted in Reinforcement Learning (RL). This approach is chosen for its capability to adaptively learn from the complex dynamics of CDPRs. The policy is trained to discern optimal cable control inputs, ensuring precise end-effector positioning. Unlike traditional feedback-based control methods, our RL control policy focuses on kinematic control and addresses the cable sag issues without being tethered to predefined mathematical models. We also demonstrate that our RL-based controller, coupled with the flexible cable simulation, significantly outperforms the classical kinematics approach, particularly in dynamic conditions and near the boundary regions of the workspace. The combined strength of the described simulation and control approach offers an effective solution in manipulating suspended CDPRs even at workspace boundary conditions where traditional approach fails, as proven from our experiments, ensuring that CDPRs function optimally in various applications while accounting for the often neglected but critical factor of cable sag.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5345-5352"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856248","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}