IEEE Robotics and Automation Letters最新文献_第5页

Swarm Navigation Based on Smoothed Particle Hydrodynamics in Complex Obstacle Environments 复杂障碍环境下基于光滑粒子流体力学的群体导航

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579607

Ruocheng Li;Bin Xin;Shuai Zhang;Mingzhe Lyu;Jinqiang Cui

{"title":"Swarm Navigation Based on Smoothed Particle Hydrodynamics in Complex Obstacle Environments","authors":"Ruocheng Li;Bin Xin;Shuai Zhang;Mingzhe Lyu;Jinqiang Cui","doi":"10.1109/LRA.2025.3579607","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579607","url":null,"abstract":"In this letter, we propose a method for the navigation of swarm uncrewed aerial vehicles (UAVs) in complex environments with obstacles. We propose an algorithmic framework based on Smoothed Particle Hydrodynamics (SPH). In this framework, each UAV is considered a particle, computing its motion information through local interactions with surrounding particles. Based on SPH, the UAV swarm can interactively adjust itself, allowing the entire cluster to advance in the flow pattern of an incompressible fluid. We introduce the Euclidean Signed Distance Field (ESDF) as a representation of the environment. The ESDF is constructed based on the obstacle information in the environment, enabling the swarm to deform and avoid obstacles within the environment. Simultaneously, we propose a swarm navigation function based on B-splines, rapidly obtaining executable trajectories by solving an unconstrained gradient optimization problem. Compared with existing methods, our algorithm exhibits significant improvements in success rate, stability, and scalability. Extensive simulations and physical experiments in both 2D and 3D environments have demonstrated the effectiveness of the proposed method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7851-7858"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367027","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

Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light 基于结构光的机器人辅助手术中软组织相互作用的图像-力估计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579640

Jiayin Wang;Mingfeng Yao;Yanran Wei;Xiaoyu Guo;Ayong Zheng;Weidong Zhao

{"title":"Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light","authors":"Jiayin Wang;Mingfeng Yao;Yanran Wei;Xiaoyu Guo;Ayong Zheng;Weidong Zhao","doi":"10.1109/LRA.2025.3579640","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579640","url":null,"abstract":"For Minimally Invasive Surgical (MIS) robots, accurate haptic interaction force feedback is essential for ensuring the safety of interacting with soft tissue. However, the majority of existing MIS robotic systems cannot facilitate direct measurement of the interaction force with hardware sensors due to space limitations. This letter introduces an effective vision-based scheme that utilizes a One-Shot structured light projection with a designed pattern on soft tissue coupled with haptic information processing through a trained image-to-force neural network. The images captured from the endoscopic stereo camera are analyzed to reconstruct high-resolution 3D point clouds for soft tissue deformation. The proposed methodology involves a modified PointNet-based force estimation method, which has demonstrated proficiency in accurately representing the intricate mechanical properties of soft tissue. To validate the efficacy of the proposed methodology, numerical force interaction experiments were conducted on three silicon materials with varying stiffness levels. The experimental results substantiate the efficacy of the proposed methodology.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7795-7802"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367059","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

Playing to the Strengths of High- and Low-Resolution Cues for Ultra-High Resolution Image Segmentation 利用高分辨率和低分辨率线索的优势进行超高分辨率图像分割

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579605

Qi Li;Jiexin Luo;Chunxiao Chen;Jiaxin Cai;Wenjie Yang;Yuanlong Yu;Shengfeng He;Wenxi Liu

{"title":"Playing to the Strengths of High- and Low-Resolution Cues for Ultra-High Resolution Image Segmentation","authors":"Qi Li;Jiexin Luo;Chunxiao Chen;Jiaxin Cai;Wenjie Yang;Yuanlong Yu;Shengfeng He;Wenxi Liu","doi":"10.1109/LRA.2025.3579605","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579605","url":null,"abstract":"In ultra-high resolution image segmentation task for robotic platforms like AAVs and autonomous vehicles, existing paradigms process a downsampled input image through a deep network and the original high-resolution image through a shallow network, then fusing their features for final segmentation. Although these features are designed to be complementary, they often contain redundant or even conflicting semantic information, which leads to blurred edge contours, particularly for small objects. This is especially detrimental to robotics applications requiring precise spatial awareness. To address this challenge, we propose a novel paradigm that disentangles the task into two independent subtasks concerning high- and low-resolution inputs, leveraging high-resolution features exclusively to capture low-level structured details and low-resolution features for extracting semantics. Specifically, for the high-resolution input, we propose a region-pixel association experts scheme that partitions the image into multiple regions. For the low-resolution input, we assign compact semantic tokens to the partitioned regions. Additionally, we incorporate a high-resolution local perception scheme with an efficient field-enriched local context module to enhance small object recognition in case of incorrect semantic assignment. Extensive experiments demonstrate the state-of-the-art performance of our method and validate the effectiveness of each designed component.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7787-7794"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367018","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

Occupation Point Planning and Tracking Control of an Underactuated Multi-Robot System to Capture a Fast Evader 欠驱动多机器人捕获快速逃兵的占领点规划与跟踪控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579606

Haiyan Zhao;Rongxin Cui;Weisheng Yan

引用次数: 0

Object State Estimation Through Robotic Active Interaction for Biological Autonomous Drilling 基于机器人主动交互的生物自主钻井目标状态估计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579609

Xiaofeng Lin;Enduo Zhao;Saúl Alexis Heredia Pérez;Kanako Harada

引用次数: 0

VarWrist: An Anthropomorphic Soft Wrist With Variable Stiffness 可变刚度的拟人化软手腕

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-13 DOI: 10.1109/LRA.2025.3579629

Chaozhou Zhang;Min Li;Zhanshuo Yang;Xiangrui Kong;Jiayi Luo;Yushen Liu;Jian Fu;Guanghua Xu;Shan Luo

{"title":"VarWrist: An Anthropomorphic Soft Wrist With Variable Stiffness","authors":"Chaozhou Zhang;Min Li;Zhanshuo Yang;Xiangrui Kong;Jiayi Luo;Yushen Liu;Jian Fu;Guanghua Xu;Shan Luo","doi":"10.1109/LRA.2025.3579629","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579629","url":null,"abstract":"Robotic wrists play a crucial role in enhancing the dexterity and stability of robotic end-effectors. Existing rigid robotic wrists tend to be complex and lack flexibility, while soft robotic wrists often struggle with limited load-bearing capacity and lower accuracy. Human wrists feature multi-degrees of freedom and variable stiffness, which help human hands to accomplish daily tasks. This study presents an innovative anthropomorphic soft robotic wrist, VarWrist, equipped with a fiber jamming variable stiffness module, enabling stiffness adjustment through vacuuming. VarWrist consists of three parallel bellows, utilizing a positive-negative pneumatic actuation strategy to mimic human wrist motion. In addition, the trajectory equation of the rotation center was fitted through modeling. We developed a prototype of VarWrist and assessed its performance. Results indicate that the soft wrist surpasses the motion range of human wrists, achieving flexion (81.9°), extension (78.5°), ulnar deviation (70.5°), and radial deviation (70.5°). The bending motion trajectory showed a 73% increase in similarity to human motion compared to fixed-axis rotation, with VarWrist exhibiting a significant range of variable stiffness (resting state: 206%, working state: 155%). Demonstration experiments confirm that this wrist facilitates a dexterous hand in completing grasping tasks that would be unattainable by the hand alone.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7883-7890"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481940","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

Estimation of Gait Phase of Human Stair Descent Walking Based on Phase Variable Approach 基于相位变量法的人下楼梯步态相位估计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-12 DOI: 10.1109/LRA.2025.3579634

Myeongju Cha;Pilwon Hur

{"title":"Estimation of Gait Phase of Human Stair Descent Walking Based on Phase Variable Approach","authors":"Myeongju Cha;Pilwon Hur","doi":"10.1109/LRA.2025.3579634","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579634","url":null,"abstract":"Synchronization between a wearer and a lower limb powered prosthesis is important for effective control. Typically, phase variable-based phase estimation methods are employed. However, there is a noticeable lack of studies focusing on estimating the gait phase during stair descent, likely due to the difficulty in generating a reliable phase variable. In most studies, the thigh angle is used to generate phase variables for level walking because it follows a sinusoidal pattern. However, during stair descent, the thigh angle exhibits only a partially sinusoidal shape, making it challenging to apply the methods used for level walking. In this study, we propose a novel phase variable generation method to address the difficulty of using only the thigh angle for stair descent. To estimate the gait phase reliably, the phase variable is defined differently for the stance and swing phases: the hip position is used to generate the phase variable during the stance phase, and the thigh angle is used during the swing phase. These phase variables are then unified into a single phase variable (PV-ENT) for the entire gait cycle of stair descent. During this unification process, a non-smooth transition occurs around the phase transition point. To address this, a blending method is applied. The proposed method was validated using the data from 12 healthy subjects, collected through a motion capture system and IMU sensors. The results demonstrate a reliable phase estimation performance. Moreover, the blending method successfully improves the smoothness of the phase variable around the phase transition point without reducing the overall phase estimation performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7691-7698"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331655","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

Learning Fast, Tool-Aware Collision Avoidance for Collaborative Robots 快速学习、工具感知的协作机器人避碰

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-12 DOI: 10.1109/LRA.2025.3579207

Joonho Lee;Yunho Kim;Seokjoon Kim;Quan Nguyen;Youngjin Heo

{"title":"Learning Fast, Tool-Aware Collision Avoidance for Collaborative Robots","authors":"Joonho Lee;Yunho Kim;Seokjoon Kim;Quan Nguyen;Youngjin Heo","doi":"10.1109/LRA.2025.3579207","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579207","url":null,"abstract":"Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7731-7738"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331812","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

Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution 基于分布式策略进化的大规模仓库多智能体寻径强化学习

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-12 DOI: 10.1109/LRA.2025.3579647

Qinru Shi;Meiqin Liu;Senlin Zhang;Xuguang Lan

{"title":"Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution","authors":"Qinru Shi;Meiqin Liu;Senlin Zhang;Xuguang Lan","doi":"10.1109/LRA.2025.3579647","DOIUrl":"https://doi.org/10.1109/LRA.2025.3579647","url":null,"abstract":"Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches struggle with challenges such as inefficient exploration, poor generalization and inadequate deadlock resolution. To address these issues, we propose a novel evolutionary reinforcement learning (ERL) framework to address the MAPF problem in large-scale warehouse environments. Specifically, the framework leverages distributed policy evolution methods to provide diverse experiences, thereby improving policy training efficiency and policy performance. We further integrate curriculum learning into this framework to improve the generality of the policy and make it scalable to larger environments. Additionally, we introduce a deadlock-breaking mechanism based on expert experience, helping to mitigate deadlock issues in large-scale and high-density scenarios. Experiments show that our method outperforms existing methods across various environments, particularly excelling in complex scenarios with over 1,000 agents.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"7843-7850"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367063","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

CNN-Based Electromagnetic Tomographic Approach for Simultaneous Tactile Imaging of Pressure and Temperature 基于cnn的压力和温度同步触觉成像电磁层析方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-06-12 DOI: 10.1109/LRA.2025.3579014

Zhinan Zhang;Shunsuke Yoshimoto;Akio Yamamoto

引用次数: 0