IEEE Robotics and Automation Letters最新文献_第6页

Mechanical Constraint Identification in Model-Mediated Teleoperation 模型介导遥操作中的机械约束识别

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-15 DOI: 10.1109/LRA.2025.3560893

Christophe van der Walt;Stefano Stramigioli;Douwe Dresscher

引用次数: 0

Global Grounding in Flexible Robot Tasks With Object Number Variety 具有目标数量变化的柔性机器人任务的全局接地

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560864

Sascha Sucker;Dominik Henrich

{"title":"Global Grounding in Flexible Robot Tasks With Object Number Variety","authors":"Sascha Sucker;Dominik Henrich","doi":"10.1109/LRA.2025.3560864","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560864","url":null,"abstract":"Automation in flexible environments must account for ambiguities and uncertainties. For example, the number of available objects may vary between different workspaces. To address this, we introduce flexible robot tasks with varieties that incorporate these ambiguities. This approach allows the programmer to estimate the number of required objects without precise knowledge of the world state during execution. With this, we deliberately leverage ambiguities, enabling task reuse across different world states. When executing a task with varieties, physical objects must be mapped to ambiguous object specifications called grounding. This grounding should be globally correct for the entire task and the world state. Rather than establishing a single grounding with fixed object numbers, we examine all the possible object numbers suitable for the task. Exhaustively testing every possibility would require exponential runtime. We overcome this challenge by contributing a global grounding algorithm for tasks with object number variety. Our algorithm uses the Kuhn-Munkres algorithm to establish fixed groundings and efficiently explores the problem space for flexible groundings – achieving a polynomial runtime. Through further optimization with binary search, our prototype demonstrates fast groundings (up to 252 objects in less than one second).","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5879-5886"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901864","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

FreeDOM: Online Dynamic Object Removal Framework for Static Map Construction Based on Conservative Free Space Estimation FreeDOM：基于保守自由空间估计的静态地图构建在线动态目标移除框架

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560881

Chen Li;Wanlei Li;Wenhao Liu;Yixiang Shu;Yunjiang Lou

{"title":"FreeDOM: Online Dynamic Object Removal Framework for Static Map Construction Based on Conservative Free Space Estimation","authors":"Chen Li;Wanlei Li;Wenhao Liu;Yixiang Shu;Yunjiang Lou","doi":"10.1109/LRA.2025.3560881","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560881","url":null,"abstract":"Online map construction is essential for autonomous robots to navigate in unknown environments. However, the presence of dynamic objects may introduce artifacts into the map, which can significantly degrade the performance of localization and path planning. To tackle this problem, a novel online dynamic object removal framework for static map construction based on conservative free space estimation (FreeDOM) is proposed, consisting of a scan-removal front-end and a map-refinement back-end. First, we propose a multi-resolution map structure for fast computation and effective map representation. In the scan-removal front-end, we employ raycast enhancement to improve free space estimation and segment the LiDAR scan based on the estimated free space. In the map-refinement back-end, we further eliminate residual dynamic objects in the map by leveraging incremental free space information. As experimentally verified on SemanticKITTI, HeLiMOS, and indoor datasets with various sensors, our proposed framework overcomes the limitations of visibility-based methods and outperforms state-of-the-art methods with an average F1-score improvement of 9.7%.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5577-5584"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888292","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

Temporal Logic-Guided DQN for Object Delivery and Transportation Using Magnetic Microrobots With Local Control 基于局部控制的磁性微机器人的时间逻辑引导DQN

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560879

Zhe Hou;Yueyue Liu;Qigao Fan

{"title":"Temporal Logic-Guided DQN for Object Delivery and Transportation Using Magnetic Microrobots With Local Control","authors":"Zhe Hou;Yueyue Liu;Qigao Fan","doi":"10.1109/LRA.2025.3560879","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560879","url":null,"abstract":"Microrobots driven by magnetic fields hold significant promise in medical applications, particularly in drug delivery–a key area in the biomedical field. Traditional approaches primarily rely on global magnetic fields to control single microrobots, which makes it challenging to independently control multiple microrobots. To address this limitation, this letter introduces a local magnetic field generation system that enables independent control of multiple microrobots. The proposed system employs a printed circuit board (PCB) array-based magnetic microrobot system, utilizing a microcoil array for precise and localized control. To enhance task execution, we integrate Deep Reinforcement Learning (DRL) with Linear Temporal Logic (LTL) to generate obstacle-avoiding paths for single and dual magnetic microrobots. The system is validated through magnetic droplet transport experiments. Experimental results demonstrate the effectiveness of the proposed system in achieving autonomous multi-task drug delivery and droplet fusion. This work underscores the potential of magnetic field-driven microcoil array systems in advancing transportation and drug delivery technologies in biomedical engineering.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5441-5448"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888417","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

A Predictor-Corrector Algorithm for the Fast Determination of the Wrench-Feasible Workspace of Cable-Driven Parallel Robots 一种快速确定缆索驱动并联机器人扳手可行工作空间的预测校正算法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560887

A. Abrous;P. Cardou

{"title":"A Predictor-Corrector Algorithm for the Fast Determination of the Wrench-Feasible Workspace of Cable-Driven Parallel Robots","authors":"A. Abrous;P. Cardou","doi":"10.1109/LRA.2025.3560887","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560887","url":null,"abstract":"In this letter, we present a method of determining the Constant-Orientation Wrench-Feasible Workspace (COWFW) of Cable-Driven Parallel Robots (CDPRs). This workspace is a critical property of CDPRs, as their reach is often limited by their ability to sustain required forces and moments while keeping their cable tensions within acceptable limits. Several manners of determining the COWFW are reported in the literature, among which the brute force method is probably the most popular, the easiest to implement and the slowest, as it consists in determining wrench-feasibility at a grid of points covering the workspace. On the other hand, the ray-tracing method implemented in <monospace>WireX</monospace> software is probably the fastest method available; It consists in sampling the COWFW boundary by performing line-searches from a common starting point along predetermined, evenly-spaced directions. The method proposed in this letter is the application to CDPRs of a predictor-corrector algorithm used in computer graphics to quickly sample implicit surfaces In this work, the implicit surface is defined by equating to zero the “capacity margin” an index developed by one of the authors. The predictor-corrector algorithm then “marches” over the implicit surface, triangulating it as it advances. Simulation results show that the determination of the COWFW surface by this technique can be faster than the ray-tracing method in at least some examples, and often yields a triangulation that is more regular. We should note, however, that the implementation of this method is more demanding, it requiring more lines of code than the ray-tracing method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5569-5576"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888418","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

Online Motion Generation via Tangential Sampling-Based MPC Around Nonconvex Obstacles 基于切线采样的MPC在非凸障碍物周围的在线运动生成

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560885

Guangbao Zhao;Ninglong Jin;Jianhua Wu;Zhenhua Xiong

{"title":"Online Motion Generation via Tangential Sampling-Based MPC Around Nonconvex Obstacles","authors":"Guangbao Zhao;Ninglong Jin;Jianhua Wu;Zhenhua Xiong","doi":"10.1109/LRA.2025.3560885","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560885","url":null,"abstract":"Collision-free motion planning has a well-established research history, but the majority of studies have been centered around Euclidean space and conducted offline. The primary challenge in online motion planning lies in circumventing local minima, which become more pronounced in configuration space. We propose an online method for generating collision-free motion in configuration space that effectively avoids local minima. Our approach decomposes the optimal velocity into nominal and tangential components, with the tangential velocity optimized to facilitate escape from local minima. The tangential velocity is defined in the tangential space, with its normal direction determined by the gradient of the nearest distance between the robot and obstacles, relative to the robot's states. Direct optimization of the tangential velocity is challenging due to its dependence on varying tangent spaces. To address this, we represent the tangential velocity using the orthogonal basis of the tangent space, decoupling it from the varying tangent space. This allows explicit optimization of the tangential velocity by optimizing its components. Additionally, we introduce a warm-start operator in the tangent space to ensure the consistency and convergence. Furthermore, we propose a dynamic weight based on proximity to local minima to balance the tangential and nominal velocities, forming the optimal velocity. The effectiveness of our approach in avoiding local minima is validated through simulations and physical experiments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5537-5544"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888290","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

Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning 基于强化学习的任务参数化动态运动原语改进运动规划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560876

Kaiqi Huang;Xiaochun Ji;Jianhua Su;Xiaoyi Qu

{"title":"Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning","authors":"Kaiqi Huang;Xiaochun Ji;Jianhua Su;Xiaoyi Qu","doi":"10.1109/LRA.2025.3560876","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560876","url":null,"abstract":"Online trajectory planning in unstructured environments poses significant challenges for mobile robots, particularly when navigating complex obstacles. Traditional learning-from-demonstration (LfD) methods depend on offline datasets, limiting their ability to adapt to varying obstacle shapes and dynamic conditions. To address these limitations, we propose a novel motion planning framework that combines global trajectory generation with local adaptability. Dynamic Movement Primitives (DMPs) are employed to generate global trajectories based on demonstrations, while Task-Parameterized Potential Fields (TPPFs) enhance local adaptability. The Policy Improvement through Path Integrals (PI<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>) algorithm is utilized to optimize model parameters. The TPPF framework consists of two key components: (a) an obstacle avoidance field, which accounts for the robot's size, obstacle dimensions, and relative distances, allowing effective volumetric avoidance without extensive modeling; and (b) an attractive field, which directs the robot toward task-specific goals while steering it away from undesirable paths. By leveraging the PI<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> algorithm, model parameters are optimized to produce trajectories that preserve the characteristics of demonstrated motions, while improving obstacle avoidance and task-oriented navigation. Experiments conducted in both simulations and dynamic real-world scenarios validate the proposed framework's effectiveness, demonstrating smoother trajectories and enhanced obstacle avoidance compared to baseline approaches.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5457-5464"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888361","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

Agile Plane Transition of a Hexapod Climbing Robot 六足爬行机器人的敏捷平面过渡

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560894

Chengzhang Gong;Li Fan;Chao Xu;Dacheng Wang

{"title":"Agile Plane Transition of a Hexapod Climbing Robot","authors":"Chengzhang Gong;Li Fan;Chao Xu;Dacheng Wang","doi":"10.1109/LRA.2025.3560894","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560894","url":null,"abstract":"Traversing across adjacent planes is an important ability for legged climbing robots. While many robots can achieve autonomous ground-to-wall transitions, most are limited to scenarios where the angle between the planes has a certain value. In some cases, however, the robot needs to traverse planes with a wide variety of angles. To enhance the adaptability of the robot in such diverse scenarios, we analyze the plane transition process and propose a universal methodology for hexapod climbing robots with a two-stage workflow. In the first stage, we plan a trajectory of body without considering configuration of legs, within a reachable map. This low-dimensional map can be efficiently sampled and explored to identify feasible transitions. In the second stage, we use a motion prediction to generate landing points, as well as swing and stance trajectories for each leg. By tracking these trajectories, the robot can autonomously transition from one plane to another. Guided by this methodology, we design a hexapod climbing robot capable of autonomously traversing planes with angles ranging from 30° to 270°. For further validation, we build the physical prototype of the robot and conduct a series of plane transition experiments. The results demonstrate the feasibility of both our methodology and the robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5959-5966"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901865","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

SkateDuct: Utilizing Vector Thrust of Ducted Fan UAVs for Terrestrial-Aerial Locomotion SkateDuct：利用矢量推力的管道风扇无人机的地面空中运动

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560882

Zhong Yin;Hailong Pei

{"title":"SkateDuct: Utilizing Vector Thrust of Ducted Fan UAVs for Terrestrial-Aerial Locomotion","authors":"Zhong Yin;Hailong Pei","doi":"10.1109/LRA.2025.3560882","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560882","url":null,"abstract":"Ducted fan UAVs (DFUAVs), characterized by vector thrust, vertical takeoff and landing (VTOL) capabilities, and high safety, have found widespread applications in both military and civilian scenarios. However, their limited endurance remains a significant constraint on their broader applications. To address this challenge, in this letter we explore a novel approach that exploits the vector thrust capabilities of DFUAVs to enable terrestrial-aerial locomotion through simple modifications without the need for additional actuators. The design of a DFUAV employing passive wheels for continuous ground and aerial operation is presented. This configuration allows for unchanged attitude and static stability during ground movement, with only a 10.3% increase in weight. Fluid simulations were conducted to analyze the variation in control vane aerodynamic efficiency under ground effect, leading to the development of a ground-effect-adjusted aerodynamic model based on experimental data. Furthermore, the dynamics of ground movement are analyzed, and a corresponding controller is developed, establishing a complete framework for seamless transition between terrestrial and aerial modes. Extensive real-world flight experiments validate the proposed structural design and control methods. By utilizing terrestrial locomotion, the UAV's energy consumption is reduced to just 33.9% of that during flight, effectively extending its operational duration by more than ten times.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6047-6054"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908440","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

A Champion-Level Vision-Based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7 《Gran Turismo 7》中基于视觉的强化学习代理

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560873

Hojoon Lee;Takuma Seno;Jun Jet Tai;Kaushik Subramanian;Kenta Kawamoto;Peter Stone;Peter R. Wurman

引用次数: 0