IEEE Transactions on Robotics最新文献

{"title":"CAT-ORA: Collision-Aware Time-Optimal Formation Reshaping for Efficient Robot Coordination in 3-D Environments","authors":"Vit Kratky;Robert Penicka;Jiri Horyna;Petr Stibinger;Tomas Baca;Matej Petrlik;Petr Stepan;Martin Saska","doi":"10.1109/TRO.2025.3547296","DOIUrl":"10.1109/TRO.2025.3547296","url":null,"abstract":"In this article, we introduce an algorithm designed to address the problem of time-optimal formation reshaping in three-dimensional environments while preventing collisions between agents. The utility of the proposed approach is particularly evident in mobile robotics, where agents benefit from being organized and navigated in formation for a variety of real-world applications requiring frequent alterations in formation shape for efficient navigation or task completion. Given the constrained operational time inherent to battery-powered mobile robots, the time needed to complete the formation reshaping process is crucial for their efficient operation, especially in case of multi-rotor uncrewed aerial vehicles (UAVs). The proposed collision-aware time-optimal formation reshaping algorithm (CAT-ORA) builds upon the Hungarian algorithm for the solution of the robot-to-goal assignment implementing the interagent collision avoidance through direct constraints on mutually exclusive robot-goal pairs combined with a trajectory generation approach minimizing the duration of the reshaping process. Theoretical validations confirm the optimality of CAT-ORA, with its efficacy further showcased through simulations, and a real-world outdoor experiment involving 19 UAVs. Thorough numerical analysis shows the potential of CAT-ORA to decrease the time required to perform complex formation reshaping tasks by up to 49%, and 12% on average compared to commonly used methods in randomly generated scenarios.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2950-2969"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570441","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":"UniphorM: A New Uniform Spherical Image Representation for Robotic Vision","authors":"Antoine N. André;Fabio Morbidi;Guillaume Caron","doi":"10.1109/TRO.2025.3547266","DOIUrl":"10.1109/TRO.2025.3547266","url":null,"abstract":"In this article, we present a new spherical image representation, called uniform spherical mapping of omnidirectional images (UniphorM), and show its strong potential in robotic vision. UniphorM provides an accurate and distortion-free representation of a 360-degree image, by relying on multiple subdivisions of an icosahedron and its associated Voronoi diagrams. The geometric mapping procedure is described in detail, and the tradeoff between pixel accuracy and computational complexity is investigated. To demonstrate the benefits of UniphorM in real-world problems, we applied it to direct visual attitude estimation and visual place recognition (VPR), by considering dual-fisheye images captured by a camera mounted on multiple robotic platforms. In the experiments, we measured the impact of the number of subdivision levels of the icosahedron on the attitude estimation error, time efficiency, and size of convergence domain of an existing visual gyroscope, using UniphorM and three competing mapping algorithms. A similar evaluation procedure was carried out for VPR. Finally, two new omnidirectional image datasets, one recorded with a hexacopter, called <italic>SVMIS</i>+, the other based on the <italic>Mapillary</i> platform, have been created and released for the entire research community.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2322-2339"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570418","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":"Autonomous Synthesis of Self-Aligning Knee Joint Exoskeleton Mechanisms","authors":"Jeonghan Yu;Seok Won Kang;Yoon Young Kim","doi":"10.1109/TRO.2025.3547274","DOIUrl":"10.1109/TRO.2025.3547274","url":null,"abstract":"Self-aligning mechanisms are essential components in facilitating adaptability in wearable robots, but their synthesis from scratch is very challenging. To overcome this hurdle, we propose a so-far-unprecedented autonomous method to synthesize self-aligning knee joint mechanisms, requiring neither a baseline design nor human intervention during synthesis. Our method transforms the synthesis problem into an optimization problem amenable to an efficient gradient-based algorithm using a discretized ground mechanism model. The main challenge in the conversion lies in how to define the objective and constraint functions in order to ensure the fundamental self-aligning capability and also to impose a desired force transmittance profile. Several design cases were considered to show the effectiveness of the newly proposed functions for the optimization-based synthesis formulation, notably in addressing degree-of-freedom requirements. Although this study focuses primarily on knee joint mechanisms assisting gait motion and aligning with the flexion axis, the developed method can be applied to other self-aligning robot mechanisms.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2358-2373"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570354","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":"ESVO2: Direct Visual-Inertial Odometry With Stereo Event Cameras","authors":"Junkai Niu;Sheng Zhong;Xiuyuan Lu;Shaojie Shen;Guillermo Gallego;Yi Zhou","doi":"10.1109/TRO.2025.3548523","DOIUrl":"10.1109/TRO.2025.3548523","url":null,"abstract":"Event-based visual odometry is a specific branch of visual simultaneous localization and mapping (SLAM) techniques, which aims at solving tracking and mapping subproblems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline viewpoint changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping subproblem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this article, we tackle these issues by building an event-based stereo visual-inertial odometry system, which is built upon a direct pipeline known as event-based stereo visual odometry (ESVO). Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via preintegration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods. Compared to ESVO, our new pipeline significantly reduces the camera pose tracking error by 40%–80% and 20%–80% in terms of absolute trajectory error and relative pose error, respectively; at the same time, the mapping efficiency is improved by a factor of five. We release our pipeline as an open-source software for future research in this field.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2164-2183"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570416","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":"PRO-MIND: Proximity and Reactivity Optimization of Robot Motion to Tune Safety Limits, Human Stress, and Productivity in Industrial Settings","authors":"Marta Lagomarsino;Marta Lorenzini;Elena De Momi;Arash Ajoudani","doi":"10.1109/TRO.2025.3547270","DOIUrl":"10.1109/TRO.2025.3547270","url":null,"abstract":"Despite impressive advancements of industrial collaborative robots, their potential remains largely untapped due to the difficulty in balancing human safety and comfort with fast production constraints. To help address this challenge, we present PRO-MIND, a novel human-in-the-loop framework that exploits valuable data about the human coworker to optimize robot trajectories. By estimating human attention and mental effort, our method dynamically adjusts safety zones and enables on-the-fly alterations of the robot path to enhance human comfort and optimal stopping conditions. Moreover, we formulate a multiobjective optimization to adapt the robot's trajectory execution time and smoothness based on the current human psychophysical stress, estimated from heart rate variability and frantic movements. These adaptations exploit the properties of B-spline curves to preserve continuity and smoothness, which are crucial factors in improving motion predictability and comfort. Evaluation in two realistic case studies showcases the framework's ability to restrain the operators' workload and stress and to ensure their safety while enhancing human–robot productivity. Further strengths of PRO-MIND include its adaptability to each individual's specific needs and sensitivity to variations in attention, mental effort, and stress during task execution.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2067-2085"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570482","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":"Propeller Damage Detection, Classification, and Estimation in Multirotor Vehicles","authors":"Claudio Pose;Juan Giribet;Gabriel Torre","doi":"10.1109/TRO.2025.3548536","DOIUrl":"10.1109/TRO.2025.3548536","url":null,"abstract":"This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor unmanned aerial vehicles. Real flight data was collected by substituting one propeller with a damaged counterpart, representing three distinct damage types of varying severity. This data was then used to train a composite model, which included both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis were exclusively sourced from inertial measurements and control command inputs. This strategic choice ensures the adaptability of the proposed methodology across diverse multirotor vehicle platforms.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2213-2229"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570355","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":"Inspection Planning Under Execution Uncertainty","authors":"Shmuel David Alpert;Kiril Solovey;Itzik Klein;Oren Salzman","doi":"10.1109/TRO.2025.3548528","DOIUrl":"10.1109/TRO.2025.3548528","url":null,"abstract":"Autonomous inspection tasks require path-planning algorithms to efficiently gather observations from <italic>points of interest</i> (POIs). However, localization errors in urban environments introduce execution uncertainty, posing challenges to successfully completing such tasks. The existing inspection-planning algorithms do not explicitly address this uncertainty, which can hinder their performance. To overcome this, in this article, we introduce <italic>incremental random inspection-roadmap search (</i><italic><monospace>IRIS</monospace></i><italic>)-under uncertainty</i> (<monospace>IRIS-U<inline-formula><tex-math>$^{2}$</tex-math></inline-formula></monospace>), an inspection-planning algorithm that provides statistical assurances regarding coverage, path length, and collision probability. Our approach builds upon <monospace>IRIS</monospace>—our framework for <italic>deterministic</i>, highly efficient, and provably asymptotically optimal framework. This extension adapts IRIS to uncertain settings using a refined search procedure that estimates POI coverage probabilities through Monte Carlo (MC) sampling. We demonstrate <monospace>IRIS-U<inline-formula><tex-math>$^{2}$</tex-math></inline-formula></monospace> through a case study on bridge inspections, achieving improved expected coverage, reduced collision probability, and increasingly precise statistical guarantees as MC samples grow. In addition, we explore bounded suboptimal solutions to reduce computation time while preserving statistical assurances.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2406-2423"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570417","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":"Hierarchical Diffusion Policy: Manipulation Trajectory Generation via Contact Guidance","authors":"Dexin Wang;Chunsheng Liu;Faliang Chang;Yichen Xu","doi":"10.1109/TRO.2025.3547272","DOIUrl":"10.1109/TRO.2025.3547272","url":null,"abstract":"Decision-making in robotics using denoising diffusion processes has increasingly become a hot research topic, but end-to-end policies perform poorly in tasks with rich contact and have limited interactivity. This article proposes Hierarchical Diffusion Policy (HDP), a new robot manipulation policy of using contact points to guide the generation of robot trajectories. The policy is divided into two layers: the high-level policy predicts the contact for the robot's next object manipulation based on 3-D information, while the low-level policy predicts the action sequence toward the high-level contact based on the latent variables of observation and contact. We represent both-level policies as conditional denoising diffusion processes, and combine behavioral cloning and Q-learning to optimize the low-level policy for accurately guiding actions towards contact. We benchmark Hierarchical Diffusion Policy across six different tasks and find that it significantly outperforms the existing state-of-the-art imitation learning method Diffusion Policy with an average improvement of 20.8% . We find that contact guidance yields significant improvements, including superior performance, greater interpretability, and stronger interactivity, especially on contact-rich tasks. To further unlock the potential of HDP, this article proposes a set of key technical contributions including one-shot gradient optimization, trajectory augmentation, and prompt guidance, which improve the policy's optimization efficiency, spatial awareness, and interactivity respectively. Finally, real-world experiments verify that HDP can handle both rigid and deformable objects.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2086-2104"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570414","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":"Human-Inspired Active Compliant and Passive Shared Control Framework for Robotic Contact-Rich Tasks in Medical Applications","authors":"Junling Fu;Giorgia Maimone;Elisa Iovene;Jianzhuang Zhao;Alberto Redaelli;Giancarlo Ferrigno;Elena De Momi","doi":"10.1109/TRO.2025.3548493","DOIUrl":"10.1109/TRO.2025.3548493","url":null,"abstract":"This work presents a compliant and passive shared control framework for teleoperated robot-assisted tasks. Inspired by the human operator's capability of continuously regulating the arm impedance to perform contact-rich tasks, a novel control schema, exploiting the variable impedance control framework for force tracking is proposed. Moreover, bilateral teleoperation and shared control strategies are implemented to alleviate the human operator's workload. Furthermore, a global energy tank-based approach is integrated to enforce the system's passivity. The proposed framework is first evaluated to assess the force-tracking capability when the robot autonomously performs contact-rich tasks, e.g., in an ultrasound scanning scenario. Then, a validation experiment is conducted utilizing the proposed shared control framework. Finally, the system's usability is investigated with 12 users. The experiment results in system assessment revealed a maximum median error of 0.25 N across all the force-tracking experiment setups, i.e., constant and time-varying ones. Then, the validation experiment demonstrated significant improvements regarding the force tracking tasks compared to conventional control methods, and the system passivity was preserved during the task execution. Finally, the usability experiment shows that the human operator workload is significantly reduced by <inline-formula><tex-math>$54.6 %$</tex-math></inline-formula> compared to the other two control modalities. The proposed framework holds significant potential for the execution of remote robot-assisted medical procedures, such as palpation and ultrasound scanning, particularly in addressing deformation challenges while ensuring safety, compliance, and system passivity.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2549-2568"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570442","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":"CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration","authors":"Daniele Cattaneo;Abhinav Valada","doi":"10.1109/TRO.2025.3546784","DOIUrl":"10.1109/TRO.2025.3546784","url":null,"abstract":"Light detection and rangings (LiDARs) are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in LiDAR maps using inexpensive cameras is a cost-effective alternative for large-scale deployment. Nevertheless, most existing approaches struggle to generalize to new sensor setups and environments, requiring retraining or fine-tuning. In this article, we present CMRNext, a novel approach for camera-LiDAR matching that is independent of sensor-specific parameters, generalizable, and can be used in the wild for monocular localization in LiDAR maps and camera-LiDAR extrinsic calibration. CMRNext exploits recent advances in deep neural networks for matching cross-modal data and standard geometric techniques for robust pose estimation. We reformulate the point-pixel matching problem as an optical flow estimation problem and solve the perspective-n-point problem based on the resulting correspondences to find the relative pose between the camera and the LiDAR point cloud. We extensively evaluate CMRNext on six different robotic platforms, including three publicly available datasets and three in-house robots. Our experimental evaluations demonstrate that CMRNext outperforms existing approaches on both tasks and effectively generalizes to previously unseen environments and sensor setups in a zero-shot manner.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1995-2013"},"PeriodicalIF":9.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526044","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}