IEEE Transactions on Robotics最新文献_第9页

Quasi-Static Modeling and Controlling for Planar Pushing of Deformable Objects

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-23 DOI: 10.1109/TRO.2025.3532500

Lijun Han;Yiming Liu;Hesheng Wang

{"title":"Quasi-Static Modeling and Controlling for Planar Pushing of Deformable Objects","authors":"Lijun Han;Yiming Liu;Hesheng Wang","doi":"10.1109/TRO.2025.3532500","DOIUrl":"10.1109/TRO.2025.3532500","url":null,"abstract":"Pushing is an essential nonprehensile manipulation for robots to achieve complex tasks. Until now, object rigidity remains one of the common assumptions in robotic pushing. To endow robots with the advanced capability of pushing deformable objects, we propose a mathematical model and control method for the planar pushing of deformable objects. Given the robotic end-effector velocity or position input, the model predicts the motion and deformation of the pushed object, which is developed based on the quasi-static finite element analysis with reasonable simplification, considering the contact conditions of nodes with both the operator and the contact surface. By combining the designed model to estimate the state of the object and interactions with the environment, we further propose a method based on model predictive control to realize the pushing control. With a specialized simplified model to accelerate prediction, the controller is solved by iterative linear quadratic regulator with a dynamic weight, which balances the object motion and pushing area adjustment. The accuracy and efficiency of the proposed deformable model are validated by comparing the theoretical results with the experimental ones under different conditions, and the controller is verified by simulation and experiments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1296-1315"},"PeriodicalIF":9.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Noncontact Manipulator for Sedimented/Floating Objects via Laser-Induced Thermocapillary Convection

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-23 DOI: 10.1109/TRO.2025.3532503

Xusheng Hui;Jianjun Luo;Haonan You;Hao Sun

{"title":"Noncontact Manipulator for Sedimented/Floating Objects via Laser-Induced Thermocapillary Convection","authors":"Xusheng Hui;Jianjun Luo;Haonan You;Hao Sun","doi":"10.1109/TRO.2025.3532503","DOIUrl":"10.1109/TRO.2025.3532503","url":null,"abstract":"Noncontact manipulation in liquid environments holds significant applications in micro/nanofluidics, microassembly, micromanufacturing, and microrobotics. Achieving compatibility in manipulating both sedimented and floating objects, as well as independently and synergistically manipulating multiple targets, remains a significant challenge. Here, a noncontact manipulator is developed for both sedimented and floating objects using laser-induced thermocapillary convection. Various strategies are proposed based on the distinct responses of sedimented and floating objects. Predefined scanning and “checkpoint” methods facilitate accurate movements of individual and multiple particles, respectively. Ultrafast programmed scanning and laser multiplexing enable independent manipulation and high-throughput ordered distribution of multiple particles. At the air–liquid interface, “laser cage” and “laser wall” are proposed to serve as effective tools for manipulating floating objects, especially with vision-based closed-loop control. Methods and strategies here do not rely on specific features of targets, solvents, and substrates. Multiple examples, including complex path replication, maze traversal, and precise assembly and disassembly, are demonstrated to validate the feasibility of this manipulator. This work provides a versatile platform and a novel methodology for noncontact manipulation in liquid.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1476-1490"},"PeriodicalIF":9.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Shared Control in pHRI: Integrating Local Trajectory Replanning and Cooperative Game Theory

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-23 DOI: 10.1109/TRO.2025.3532510

Lijun Han;Jinyu Zhang;Hesheng Wang

{"title":"Shared Control in pHRI: Integrating Local Trajectory Replanning and Cooperative Game Theory","authors":"Lijun Han;Jinyu Zhang;Hesheng Wang","doi":"10.1109/TRO.2025.3532510","DOIUrl":"10.1109/TRO.2025.3532510","url":null,"abstract":"In this article, we propose a two-stage shared control framework for physical human–robot interaction (pHRI) that addresses the inconsistency of human–robot commands and consider the influence of environmental information. In the human–robot–environment system, based on the human intention measured by the interaction force, autonomy will actively initiate the replanning when the human control intention is strong, generating a feasible local desired trajectory of the robot. At the same time, we define an index called predicted safety index (PSI) to measure the safety of the system status. When the human has control intention but does not reach the threshold, we propose a shared controller based on cooperative-game theory and PSI. Specially, it is designed within the model predictive control framework, utilizing cooperative game theory to analyze human–robot interaction behavior and treating the Pareto optimal solution as the control input. We conduct comparative experiments to evaluate the assistive performance of the proposed shared control algorithm through a waypoint tracking task with naive human users. User study with objective and subjective measures demonstrate that the algorithm effectively reduces human effort while maintaining tracking accuracy, thus enhancing both performance and safety.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1263-1277"},"PeriodicalIF":9.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems iKalibr：弹性集成惯性系统的统一无目标时空校准

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-21 DOI: 10.1109/TRO.2025.3532506

Shuolong Chen;Xingxing Li;Shengyu Li;Yuxuan Zhou;Xiaoteng Yang

{"title":"iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems","authors":"Shuolong Chen;Xingxing Li;Shengyu Li;Yuxuan Zhou;Xiaoteng Yang","doi":"10.1109/TRO.2025.3532506","DOIUrl":"10.1109/TRO.2025.3532506","url":null,"abstract":"The integrated inertial system, typically integrating an IMU and an exteroceptive sensor, such as radar, light detection and ranging (LiDAR), and camera, has been widely accepted and applied in modern robotic applications for ego-motion estimation, motion control, or autonomous exploration. To improve system accuracy, robustness, and further usability, both multiple and various sensors are generally resiliently integrated, which benefits the system performance regarding failure tolerance, perception capability, and environment compatibility. For such systems, accurate and consistent spatiotemporal calibration is required to maintain a unique spatiotemporal framework for multisensor fusion. Considering that most existing calibration methods first, are generally oriented to specific integrated inertial systems, second, often focus on spatial-only determination, and third, usually require artificial targets, lacking convenience and usability, we propose <italic>iKalibr:</i> a unified targetless spatiotemporal calibration framework for resilient integrated inertial systems, which overcomes the above issues, and enables both accurate and consistent calibration. Altogether four commonly employed sensors are supported in <italic>iKalibr</i> currently, namely, IMU, radar, LiDAR, and camera. The proposed method starts with a rigorous and efficient dynamic initialization, where all parameters in the estimator would be accurately recovered. Subsequently, several continuous-time batch optimizations are conducted to refine the initialized parameters toward better states. Sufficient real-world experiments were conducted to verify the feasibility and evaluate the calibration performance of <italic>iKalibr</i>. The results demonstrate that <italic>iKalibr</i> can achieve accurate resilient spatiotemporal calibration.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1618-1638"},"PeriodicalIF":9.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Tactile Ergodic Coverage on Curved Surfaces 曲面上的触觉尔格覆盖范围

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-21 DOI: 10.1109/TRO.2025.3532513

Cem Bilaloglu;Tobias Löw;Sylvain Calinon

引用次数: 0

Industrial Robots Energy Consumption Modeling, Identification and Optimization Through Time-Scaling 基于时间尺度的工业机器人能耗建模、辨识与优化

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-21 DOI: 10.1109/TRO.2025.3532509

Zuoxue Wang;Pei Jiang;Xiaobin Li;Huajun Cao;Xi Vincent Wang;Xiangfei Li;Min Cheng

{"title":"Industrial Robots Energy Consumption Modeling, Identification and Optimization Through Time-Scaling","authors":"Zuoxue Wang;Pei Jiang;Xiaobin Li;Huajun Cao;Xi Vincent Wang;Xiangfei Li;Min Cheng","doi":"10.1109/TRO.2025.3532509","DOIUrl":"10.1109/TRO.2025.3532509","url":null,"abstract":"Industrial robots (IRs) have considerable energy-saving potential due to their vast application scale and wide range of applications. Although substantial work on the energy consumption (EC) optimization of IRs has emerged, most optimization approaches require prior knowledge of the IRs' dynamic characteristics and the electro-mechanical parameters of their drive systems, which are typically not provided by IR manufacturers. Therefore, this article proposes an EC modeling and optimization method based on the time-scaling technique and custom identification experimental data without joint torque information. Specifically, this article develops an energy characteristic parameter submodel (ECPSM) to formulate the EC resulting from configuration transitions. In addition, theoretical proof demonstrates that all coefficients in the proposed ECPSM can be identified based on the data of a finite number of identification experiments. Building upon the proposed EC model, a bidirectional dynamic programming (BDP) algorithm optimizes the IR's trajectory for energy-saving, while utilizing parallel processing significantly reduces the time required for the optimization process. Experimental results on the KUKA KR60-3 demonstrate that the proposed method achieves an average relative error of 1.59% for predicting the EC of linear scaling trajectories and 6.19% for nonlinear scaled trajectories. Moreover, the BDP-based optimization method dramatically reduces the computational time required to obtain the optimal scaling trajectory and its EC.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1456-1475"},"PeriodicalIF":9.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Soft Robotic Fish Actuated by Bionic Muscle With Embedded Sensing for Self-Adaptive Multiple Modes Swimming 由嵌入式传感仿生肌肉驱动的软体机器鱼实现自适应多模式游泳

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-21 DOI: 10.1109/TRO.2025.3532520

Ruiqian Wang;Chuang Zhang;Wenjun Tan;Yiwei Zhang;Lianchao Yang;Wenyuan Chen;Feifei Wang;Jiandong Tian;Lianqing Liu

{"title":"Soft Robotic Fish Actuated by Bionic Muscle With Embedded Sensing for Self-Adaptive Multiple Modes Swimming","authors":"Ruiqian Wang;Chuang Zhang;Wenjun Tan;Yiwei Zhang;Lianchao Yang;Wenyuan Chen;Feifei Wang;Jiandong Tian;Lianqing Liu","doi":"10.1109/TRO.2025.3532520","DOIUrl":"10.1109/TRO.2025.3532520","url":null,"abstract":"Fish can adaptively adjust their body kinematics and swimming modes by sensing to realize optimal propulsion. However, most soft robotic fish have an unchangeable swimming mode through simple structure design, making them difficult to adapt to dynamic and complex fluid environments. Here, inspired by the multiple muscle synergy and lateral line sensing function of fish, we developed a soft robotic fish with multiple actuating units and embedded sensing elements. By collaboratively controlling the amplitude and phase of excitation from the multiple flexible actuating units, the soft robotic fish can successfully realize various swimming modes very similar to those of natural fish. Additionally, the embedded flexible sensing elements enable the robotic fish to sense the swimming state and the surrounding fluid environment in real time. The multiple actuation and embedded sensing allow the soft robotic fish to adaptively switch to an optimal swimming mode in a certain fluid environment. The multimode swimming and perception capabilities proposed in this work not only make soft robotic fish more intelligent and adaptable to complex fluid environments, but also contribute to the future implementation of autonomous control capabilities for robotic fish.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1329-1345"},"PeriodicalIF":9.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC 面向可扩展多机器人控制：分布式MPC中的快速策略学习

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-20 DOI: 10.1109/TRO.2025.3531818

Xinglong Zhang;Wei Pan;Cong Li;Xin Xu;Xiangke Wang;Ronghua Zhang;Dewen Hu

{"title":"Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC","authors":"Xinglong Zhang;Wei Pan;Cong Li;Xin Xu;Xiangke Wang;Ronghua Zhang;Dewen Hu","doi":"10.1109/TRO.2025.3531818","DOIUrl":"10.1109/TRO.2025.3531818","url":null,"abstract":"Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on numerical optimization tools to periodically calculate local control sequences online. This process is computationally demanding and lacks scalability for large-scale, nonlinear MRS. This article proposes a novel distributed learning-based predictive control framework for scalable multirobot control. Unlike conventional DMPC methods that calculate open-loop control sequences, our approach centers around a computationally fast and efficient distributed policy learning algorithm that generates explicit closed-loop DMPC policies for MRS without using numerical solvers. The policy learning is executed incrementally and forward in time in each prediction interval through an online distributed actor–critic implementation. The control policies are successively updated in a receding-horizon manner, enabling fast and efficient policy learning with the closed-loop stability guarantee. The learned control policies could be deployed online to MRS with varying robot scales, enhancing scalability and transferability for large-scale MRS. Furthermore, we extend our methodology to address the multirobot safe learning challenge through a force field-inspired policy learning approach. We validate our approach's effectiveness, scalability, and efficiency through extensive experiments on cooperative tasks of large-scale wheeled robots and multirotor drones. Our results demonstrate the rapid learning and deployment of DMPC policies for MRS with scales up to 10 000 units.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1491-1512"},"PeriodicalIF":9.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Predictive Visuo-Tactile Interactive Perception Framework for Object Properties Inference 预测视觉-触觉交互感知框架的对象属性推理

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-20 DOI: 10.1109/TRO.2025.3531816

Anirvan Dutta;Etienne Burdet;Mohsen Kaboli

{"title":"Predictive Visuo-Tactile Interactive Perception Framework for Object Properties Inference","authors":"Anirvan Dutta;Etienne Burdet;Mohsen Kaboli","doi":"10.1109/TRO.2025.3531816","DOIUrl":"10.1109/TRO.2025.3531816","url":null,"abstract":"Interactive exploration of unknown objects' properties, such as stiffness, mass, center of mass, friction coefficient, and shape, is crucial for autonomous robotic systems operating in unstructured environments. Precise identification of these properties is essential for stable and controlled object manipulation and for anticipating the outcomes of (prehensile or nonprehensile) manipulation actions, such as pushing, pulling, and lifting. Our study focuses on autonomously inferring the physical properties of a diverse set of homogeneous, heterogeneous, and articulated objects using a robotic system equipped with vision and tactile sensors. We propose a novel predictive perception framework to identify object properties by leveraging versatile exploratory actions: nonprehensile pushing and prehensile pulling. A key component of our framework is a novel active shape perception mechanism that seamlessly initiates exploration. In addition, our dual differentiable filtering with graph neural networks learns the object–robot interaction and enables consistent inference of indirectly observable, time-invariant object properties. Finally, we develop a N-step information gain approach to select the most informative actions for efficient learning and inference. Extensive real-robot experiments with planar objects show that our predictive perception framework outperforms state-of-the-art baselines and showcases it in three major applications for object tracking, goal-driven task, and environmental change detection.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1386-1403"},"PeriodicalIF":9.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10847911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Impedance Learning-Based Adaptive Force Tracking for Robot on Unknown Terrains 基于阻抗学习的未知地形机器人自适应力跟踪

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-01-15 DOI: 10.1109/TRO.2025.3530345

Yanghong Li;Li Zheng;Yahao Wang;Erbao Dong;Shiwu Zhang

{"title":"Impedance Learning-Based Adaptive Force Tracking for Robot on Unknown Terrains","authors":"Yanghong Li;Li Zheng;Yahao Wang;Erbao Dong;Shiwu Zhang","doi":"10.1109/TRO.2025.3530345","DOIUrl":"10.1109/TRO.2025.3530345","url":null,"abstract":"Aiming at the robust force tracking challenge for robots in continuous contact with uncertain environments, a novel adaptive variable impedance control policy based on deep reinforcement learning (DRL) is proposed in this article. The policy includes a neural network feedforward controller and a variable impedance feedback controller. Based on the DRL algorithm, the iterative network feedforward controller explores and prelearns the optimal policy for impedance tuning in simulation scenarios with randomly generated terrain. The converged results are then used as feedforward inputs in the variable impedance feedback controller to improve the force-tracking performance of the robot during contact. A simplified dynamic contact model between the robot and the uncertain environment called the “couch model,” which satisfies the Lipschiz continuity condition, is developed to provide boundary conditions for the safe transfer of capabilities learned in simulation to real robots. Unlike the exhaustive example that relies on the completeness of the learning samples, this article gives theoretical proofs of the stability and convergence of the proposed control policy via Lyapunov’s theorem and contraction mapping principle. The control method proposed in this article is more interpretable and shows higher sample utilization efficiency and generalization ability in simulations and experiments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1404-1420"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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