IEEE Transactions on Robotics最新文献

{"title":"SceneFactory: A Workflow-Centric and Unified Framework for Incremental Scene Modeling","authors":"Yijun Yuan;Michael Bleier;Andreas Nüchter","doi":"10.1109/TRO.2025.3562479","DOIUrl":"10.1109/TRO.2025.3562479","url":null,"abstract":"In this article, we present SceneFactory, a workflow-centric and unified framework for incremental scene modeling that conveniently supports a wide range of applications, such as (unposed and/or uncalibrated) multiview depth estimation, LiDAR completion, (dense) RGB-D/RGB-LiDAR (RGB-L)/Mono/Depth-only reconstruction, and simultaneous localization and mapping (SLAM). The workflow-centric design uses multiple blocks as the basis for constructing different production lines. The supported applications, i.e., productions avoid redundancy in their designs. Thus, the focus is placed on each block itself for independent expansion. To support all input combinations, our implementation consists of four building blocks that form SceneFactory: first, tracking, second, flexion, third, depth estimation, and fourth, scene reconstruction. The tracking block is based on Mono SLAM and is extended to support RGB-D and RGB-L inputs. Flexion is used to convert the depth image (untrackable) into a trackable image. For general-purpose depth estimation, we propose an unposed and uncalibrated multiview depth estimation model (U<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-MVD) to estimate dense geometry. U<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-MVD exploits dense bundle adjustment to solve for poses, intrinsics, and inverse depth. A semantic-aware ScaleCov step is then introduced to complete the multiview depth. Relying on U<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-MVD, SceneFactory both supports user-friendly 3-D creation (with just images) and bridges the applications of Dense RGB-D and Dense Mono. For high-quality surface and color reconstruction, we propose dual-purpose multiresolutional neural points for the first surface accessible surface color field design, where we introduce improved point rasterization for point cloud-based surface query. We implement and experiment with SceneFactory to demonstrate its broad applicability and high flexibility. Its quality also competes or exceeds the tightly-coupled state of the art approaches in all tasks.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3183-3201"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849719","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}

{"title":"An Efficient Unified Algorithm for the Minimum Euclidean Distance Between Two Collections of Compact Convex Sets","authors":"Yu Zheng","doi":"10.1109/TRO.2025.3562478","DOIUrl":"10.1109/TRO.2025.3562478","url":null,"abstract":"In this article, we present an efficient unified algorithm for the minimum Euclidean distance between two collections of compact convex sets, each of which can be a collection of convex primitives, such as ellipsoids, capsules, and cylinders, or a collection of triangles (i.e., triangle mesh) or a collection of points (i.e., point cloud) as special cases. The Euclidean distance between two compact convex sets is defined to be the smallest translation to bring them into intersection if they are separated or to separate them if they intersect, which can be computed by the well-known Gilbert–Johnson–Keerthi and expanding polytope algorithms, respectively. While existing algorithms are aimed at computing the minimum Euclidean distance for a specific type of collections, algorithms for mixed situations always remain vacant. We discover that the smallest translation direction between any two compact convex sets determines the planes to bound and separate some other sets in two collections and can help quickly identify sets that do not have the minimum distance. In this way, the minimum distance between two collections can be efficiently computed, hundreds to thousands of times faster than the brute-force search. The computational efficiency of the proposed algorithm is verified with a number of numerical experiments in various scenarios.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3004-3018"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849772","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}

{"title":"A Biomimetic Rigid-Soft Hybrid Underwater Gripper With Compliance, Stability, Precise Control, and High Load Capacity","authors":"Fei Suo;Xiaolong Hui;Peixin Hua;Xuejian Bai;Jin Ma;Min Tan;Yu Wang","doi":"10.1109/TRO.2025.3562458","DOIUrl":"10.1109/TRO.2025.3562458","url":null,"abstract":"The complex underwater environment presents numerous challenges for the design of soft grippers, which often suffer from limited load capacity, poor stability, low portability, and imprecise control. This article proposes a novel rigid-soft hybrid gripper specifically designed for underwater use. The gripper's finger is constructed from silicone, reinforced with a multilink rigid exoskeleton on the outside, and actuated by tendons. This design provides three key advantages: compliance (capable of handling fragile objects such as a piece of tofu), heavy lifting (demonstrated by lifting an 80-kg barbell with three fingers), and precise, stable operation (the hybrid gripper maintains its shape despite water flow disturbances). In addition, the gripper is compact and lightweight, with the driving system powered by just four 23-g servo motors, making it easy to mount on various underwater robots. To enable precise control, both specialized kinematic and mechanics models were developed, allowing accurate predictions of the relationships among tendon displacement, exoskeleton deformation, soft material deformation, and tendon tension. This study thoroughly considers the challenges of underwater environments, offering new insights for advancing the field of underwater soft grasping.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3099-3112"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849755","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}

{"title":"Fast and Accurate 6-D Object Pose Refinement via Implicit Surface Optimization","authors":"Bo Pang;Deming Zhai;Jianan Zhen;Long Wang;Xianming Liu","doi":"10.1109/TRO.2025.3562484","DOIUrl":"10.1109/TRO.2025.3562484","url":null,"abstract":"Aligning a point cloud to a fixed 3-D model is a crucial task in many applications, such as 6-D pose estimation for robotic grasping. Typically, an initial pose is estimated by analyzing both the point cloud and the 3-D model, after which the iterative closest point (ICP) algorithm is used to refine the pose, reducing large errors and improving accuracy. In this article, we propose an accurate and efficient alternative to the ICP. Our method encodes the fixed 3-D model into an implicit neural network, which is trained offline as a one-time process in just a few minutes, requiring only the CAD model of the object. The network takes the point cloud and pose as inputs and outputs the signed distance field (SDF) value. By minimizing the absolute SDF value with the fixed point cloud and network weights, while optimizing the pose, we obtain the final precise alignment. The key advantage of our method is that it eliminates the need to explicitly establish one-to-one correspondences between the point cloud and the 3-D model, a necessary step in the ICP and its variants. This enables our framework to avoid local optima and makes it more robust to challenging conditions such as large initial pose gaps, noisy data, variations in scale, occlusions, and reflections. Furthermore, the end-to-end network of our framework offers significant runtime efficiency. We validate the superior performance of our approach through extensive comparisons with various ICP variants on both synthetic and real-world datasets.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3129-3142"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849775","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}

{"title":"High-Order Regularization Dealing With ILL-Conditioned Robot Localization Problems","authors":"Xinghua Liu;Ming Cao","doi":"10.1109/TRO.2025.3562487","DOIUrl":"10.1109/TRO.2025.3562487","url":null,"abstract":"In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable when the problems are ill-conditioned. A typical way to solve ill-conditioned problems is regularization, and a classical regularization method is the Tikhonov regularization. It is shown that the Tikhonov regularization is a low-order case of our method. We find that the proposed method is superior to the Tikhonov regularization in approximating some ill-conditioned inverse problems, such as some basic robot localization problems. The proposed method overcomes the oversmoothing problem in the Tikhonov regularization as it uses more than one term in the approximation of the matrix inverse, and an explanation for the oversmoothing of the Tikhonov regularization is given. Moreover, one a priori criterion, which improves the numerical stability of the ill-conditioned problem, is proposed to obtain an optimal regularization matrix. As most of the regularization solutions are biased, we also provide two bias-correction techniques for the proposed high-order regularization. The simulation and experimental results using an ultra-wideband sensor network in a 3-D environment are discussed, demonstrating the performance of the proposed method.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3539-3555"},"PeriodicalIF":9.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849770","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}

{"title":"ACSim: A Novel Acoustic Camera Simulator With Recursive Ray Tracing, Artifact Modeling, and Ground Truthing","authors":"Yusheng Wang;Yonghoon Ji;Hiroshi Tsuchiya;Jun Ota;Hajime Asama;Atsushi Yamashita","doi":"10.1109/TRO.2025.3562048","DOIUrl":"10.1109/TRO.2025.3562048","url":null,"abstract":"We present a novel acoustic camera simulator that generates realistic sonar images by incorporating recursive ray tracing and sonar artifact modeling and provides various ground truth labels, enabling benchmarking and learning purposes. The 2-D forward-looking sonar, also known as the acoustic camera, produces high-quality 2-D images. Conducting real-world underwater experiments is challenging, making realistic sonar image simulation a necessary alternative. However, existing simulators often lack sufficient realism or are limited to specific scenes and phenomena. As a result, training on simulations and testing on real sonar images (i.e., sim-to-real) remain open problems for deep learning-based applications. Our work introduces a novel sonar simulator with a customized rendering engine. We use recursive ray tracing to model multipath reflections in arbitrary scenes and propose physics-based shading for intensity computation. We propose a resampling method for antialiasing and model significant artifacts, such as rolling shutter distortions and crosstalk noise. The simulator provides various ground truths for benchmarking and deep learning applications. We tested several tasks by training on synthetic images and demonstrated that the models also work on real images. We developed a Blender add-on for an enhanced user interface and will make the simulator open-source to advance future research.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2970-2989"},"PeriodicalIF":9.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841836","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}

{"title":"Heterogeneous Collaborative Pursuit via Coverage Control Driven by Fokker-Planck Equations","authors":"Ruoyu Lin, Soobum Kim, Magnus Egerstedt","doi":"10.1109/tro.2025.3559420","DOIUrl":"https://doi.org/10.1109/tro.2025.3559420","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"75 1","pages":"1-20"},"PeriodicalIF":7.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813765","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}

{"title":"Ultrarobust and Lightweight Electro-Pneumatic Actuators for Soft Robotics","authors":"Zean Yuan;Jiaxing Li;Lifu Liu;Xinyu Zhu;Wenbiao Wang;Michael D. Dickey;Guo Zhan Lum;Pakpong Chirarattananon;Jun Luo;Rui Chen","doi":"10.1109/TRO.2025.3559430","DOIUrl":"10.1109/TRO.2025.3559430","url":null,"abstract":"Rigid robots can achieve precise motions but expose shortcomings in system complexity, fabrication cost, and human–robot interaction, which motivates researchers to develop various soft robots to fill these gaps. Electro-hydraulic actuators (EHAs) have received widespread attention and been used in many soft robots due to impressive high-strain, fast-speed, and rapid-response characteristics. However, existing EHAs face challenges in achieving large-deformation, high-robustness, and low-weight simultaneously. This limits the application of EHAs in robotic systems that are weight-sensitive or require fail-safe and fault-tolerant behavior. Here, we present a lightweight (0.98 g) electro-pneumatic actuator (EPA) filled with air and only 0.1-mL liquid dielectric, which achieves high-speed bending from 11° to 93.5° in 60 ms, large-angle bending from 11° to 104° in 2 s (the largest in current EHAs), and high-frequency swing at 20 Hz. The EPA is ultrarobust and can operate properly after being punctured by four needles or crushed twice by a 1500-kg vehicle. Furthermore, to validate the above features of EPAs, three applications are demonstrated at a voltage of 6 kV, including four-finger grippers, fast-crawling robots, and water-walking robots. This work pushes the boundaries of robustness and lightweight for EHAs, providing a foundation for the application of electro-pneumatic actuation in soft robotics.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2894-2910"},"PeriodicalIF":9.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813762","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}

{"title":"A Learning-Based Method for Computing Self-Motion Manifolds of Redundant Robots for Real-Time Fault-Tolerant Motion Planning","authors":"Charles L. Clark;Biyun Xie","doi":"10.1109/TRO.2025.3559404","DOIUrl":"10.1109/TRO.2025.3559404","url":null,"abstract":"The focus of this research is to develop a learning-based method that computes self-motion manifolds (SMMs) efficiently and accurately to enable real-time global fault-tolerant motion planning. The proposed method first develops a learnable, closed-form representation of SMMs based on Fourier series. A cellular automaton is then applied to cluster workspace locations having the same number of SMMs and group SMMs with similar shape by homotopy classes, such that the SMMs of each homotopy class can be accurately learned by a neural network. To approximate the SMMs of an arbitrary workspace location, a neural network is first trained to predict the set of homotopy classes belonging to this workspace location. For each set of homotopy classes, another neural network is trained to approximate the Fourier series coefficients of the SMMs, and the joint configurations along the SMMs can be retrieved using the inverse Fourier transform. The proposed method is validated on planar 3R positioning, spatial 4R positioning, and spatial 7R positioning and orienting robots, using 10 000 randomly sampled workspace locations each. The results show that the proposed method can approximate SMMs with high accuracy and is much faster than the traditionally used nullspace projection method, a sampling-based method, and a grid-based method. The performance of the proposed method in real-time fault-tolerant motion planning applications is also demonstrated using the simulation of the spatial 7R robot and physical experiments on a planar 3R robot. Due to the computational efficiency of the proposed method, both robots are able to quickly plan trajectories which maximize the likelihood of task completion after the failure of one arbitrary joint.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2879-2893"},"PeriodicalIF":9.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813526","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}

{"title":"NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning","authors":"Ruihua Han;Shuai Wang;Shuaijun Wang;Zeqing Zhang;Jianjun Chen;Shijie Lin;Chengyang Li;Chengzhong Xu;Yonina C. Eldar;Qi Hao;Jia Pan","doi":"10.1109/TRO.2025.3554252","DOIUrl":"10.1109/TRO.2025.3554252","url":null,"abstract":"Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2804-2824"},"PeriodicalIF":9.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723294","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}