IEEE Transactions on Robotics最新文献

{"title":"Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks","authors":"Zirui Xu;Sandilya Sai Garimella;Vasileios Tzoumas","doi":"10.1109/TRO.2025.3567540","DOIUrl":"10.1109/TRO.2025.3567540","url":null,"abstract":"In this article, we provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering such as mapping, surveillance, and target tracking. Our method, resource-aware distributed greedy (<monospace>RAG</monospace>), introduces a new distributed optimization paradigm that enables scalable and near-optimal action coordination. To this end, <monospace>RAG</monospace> requires each robot to make decisions based only on information received from and about their neighbors. In contrast, the current paradigms allow the relay of information about all robots across the network. As a result, <monospace>RAG</monospace>’s decision-time scales linearly with the network size, while state-of-the-art near-optimal submodular optimization algorithms scale cubically. We also characterize how the designed mesh-network topology affects <monospace>RAG</monospace>’s approximation performance. Our analysis implies that sparser networks favor scalability without proportionally compromising approximation performance: while <monospace>RAG</monospace>’s decision-time scales linearly with network size, the gain in approximation performance scales sublinearly. We demonstrate <monospace>RAG</monospace>’s performance in simulated scenarios of area detection with up to 45 robots, simulating realistic robot-to-robot (r2r) communication speeds such as the 0.25 Mb/s speed of the Digi XBee 3 Zigbee 3.0. In the simulations, <monospace>RAG</monospace> enables real-time planning, up to three orders of magnitude faster than competitive near-optimal algorithms, while also achieving superior mean coverage performance. To enable the simulations, we extend the high-fidelity and photo-realistic simulator AirSim by integrating a scalable collaborative autonomy pipeline to tens of robots and simulating r2r communication delays.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3480-3499"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915268","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":"R-FAC: Resilient Value Function Factorization for Multirobot Efficient Search With Individual Failure Probabilities","authors":"Hongliang Guo;Qi Kang;Wei-Yun Yau;Chee-Meng Chew;Daniela Rus","doi":"10.1109/TRO.2025.3567478","DOIUrl":"10.1109/TRO.2025.3567478","url":null,"abstract":"This article investigates the <italic>resilient</i> multirobot efficient search problem (R-MuRES), which aims at coordinating multiple robots to detect a “nonadversarial” moving target with the minimal expected time. One unique characteristic of R-MuRES among others is the possibility of individual robot's malfunction and withdrawal from the team during task execution, which results in a <italic>variable</i> number of searchers in the deployment phase and entails that the possibility of team member failures must be considered during the planning stage, particularly in the training phase. We propose a resilient value function factorization (R-FAC) paradigm, which constructs the central value function from individual ones in a resilient manner, taking into account individual robots' failures, and ensures that the constructed central value function has the minimal mean squared temporal difference error across various team compositions. R-FAC stipulates that the individual global maximum principle is satisfied for whichever team configuration and thus any functioning robot contributes positively to the remaining team, as long as it executes the greedy policy with respect to the factorized individual value function. Subsequently, we introduce the <italic>variational</i> value decomposition network (V2DN) as one of the instantiated R-FAC algorithms. V2DN employs the <inline-formula><tex-math>$log$</tex-math></inline-formula>-sum-<inline-formula><tex-math>$exp$</tex-math></inline-formula> mechanism to construct the central value function from individual ones, enabling it to take a varying number of robots' individual value functions as inputs. Then, we explain why, specifically for the multirobot search task, the <inline-formula><tex-math>$log$</tex-math></inline-formula>-sum-<inline-formula><tex-math>$exp$</tex-math></inline-formula> mechanism is superior to the brute-force summation operation used in the canonical value decomposition network (VDN), and compare V2DN with state-of-the-art MuRES solutions as well as the vanilla VDN algorithm in two canonical MuRES testing environments and show that it achieves the best resiliency score when one or several individual robots quit the team during task execution. Furthermore, we validate V2DN with a real multirobot system in a self-constructed indoor environment as the proof of concept.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3385-3401"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915322","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":"Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction","authors":"Jingtao Tang;Zining Mao;Hang Ma","doi":"10.1109/TRO.2025.3567476","DOIUrl":"10.1109/TRO.2025.3567476","url":null,"abstract":"In this article, we study multirobot coverage path planning (MCPP) on a four-neighbor 2-D grid <inline-formula><tex-math>$G$</tex-math></inline-formula>, which aims to compute paths for multiple robots to cover all cells of <inline-formula><tex-math>$G$</tex-math></inline-formula>. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid <inline-formula><tex-math>$mathcal {H}$</tex-math></inline-formula> and then employ the spanning tree coverage (STC) paradigm to generate paths on <inline-formula><tex-math>$G$</tex-math></inline-formula>, making them inapplicable to grids with partially obstructed <inline-formula><tex-math>$2 times 2$</tex-math></inline-formula> blocks. To address this limitation, we reformulate the problem directly on <inline-formula><tex-math>$G$</tex-math></inline-formula>, revolutionizing grid-based MCPP solving and establishing new NP-hardness results. We introduce extended STC (ESTC), a novel paradigm that extends STC to ensure complete coverage with bounded suboptimality, even when <inline-formula><tex-math>$mathcal {H}$</tex-math></inline-formula> includes partially obstructed blocks. Furthermore, we present LS-MCPP, a new algorithmic framework that integrates ESTC with three novel types of neighborhood operators within a local search strategy to optimize coverage paths directly on <inline-formula><tex-math>$G$</tex-math></inline-formula>. Unlike prior grid-based MCPP work, our approach also incorporates a versatile postprocessing procedure that applies multiagent path finding (MAPF) techniques to MCPP for the first time, enabling a fusion of these two important fields in multirobot coordination. This procedure effectively resolves inter-robot conflicts and accommodates turning costs by solving an MAPF variant, making our MCPP solutions more practical for real-world applications. Extensive experiments demonstrate that our approach significantly improves solution quality and efficiency, managing up to 100 robots on grids as large as <inline-formula><tex-math>$text{256} times text{256}$</tex-math></inline-formula> within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3348-3367"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915266","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":"Multiscale and Uncertainty-Aware Targetless Hand-Eye Calibration via the Gauss–Helmert Model","authors":"Marta Čolaković-Bencerić;Juraj Peršić;Ivan Marković;Ivan Petrović","doi":"10.1109/TRO.2025.3548538","DOIUrl":"10.1109/TRO.2025.3548538","url":null,"abstract":"The operational reliability of an autonomous robot depends crucially on extrinsic sensor calibration as a prerequisite for precise and accurate data fusion. Exploring the calibration of unscaled sensors (e.g., monocular cameras) and the effective utilization of uncertainties are difficult and often overlooked. The development of a solution for the simultaneous calibration of hand-eye sensors and scale estimation based on the Gauss–Helmert model aims to utilize the valuable information contained in the uncertainty of odometry. In this work, we propose a versatile and robust solution for batch calibration based on the analytical on-manifold approach for estimation. The versatility of our method is demonstrated by its ability to calibrate multiple unscaled and metric-scaled sensors while dealing with odometry failures and reinitializations. Importantly, all estimated parameters are provided with their corresponding uncertainties. The validation of our method and its comparison with five competing state-of-the-art calibration methods in both simulations and real-world experiments show its superior accuracy, with particularly promising results observed in high-noise scenarios.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2340-2357"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570477","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":"Physics-Informed Neural Mapping and Motion Planning in Unknown Environments","authors":"Yuchen Liu;Ruiqi Ni;Ahmed H. Qureshi","doi":"10.1109/TRO.2025.3548495","DOIUrl":"10.1109/TRO.2025.3548495","url":null,"abstract":"Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing mapping methods create maps that require computationally expensive motion planning tools to find a path solution. In this article, we propose a new mapping feature called arrival time fields, which is a solution to the Eikonal equation. The arrival time fields can directly guide the robot in navigating the given environments. Therefore, this article introduces a new approach called active neural time fields, which is a physics-informed neural framework that actively explores the unknown environment and maps its arrival time field on the fly for robot motion planning. Our method does not require any expert data for learning and uses neural networks to directly solve the Eikonal equation for arrival time field mapping and motion planning. We benchmark our approach against state-of-the-art mapping and motion planning methods and demonstrate its superior performance in both simulated and real-world environments with a differential drive robot and a six-degree-of-freedom robot manipulator.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2200-2212"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570415","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 Model Predictive Capture Point Control Framework for Robust Humanoid Balancing Via Ankle, Hip, and Stepping Strategies","authors":"Myeong-Ju Kim;Daegyu Lim;Gyeongjae Park;Kwanwoo Lee;Jaeheung Park","doi":"10.1109/TRO.2025.3567546","DOIUrl":"10.1109/TRO.2025.3567546","url":null,"abstract":"The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this article, a robust balance control framework for humanoids is proposed. First, a model predictive control (MPC) framework is proposed for capture point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. In addition, a variable weighting method is introduced that adjusts the weighting parameters of the centroidal angular momentum damping control. Second, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art quadratic programming-based CP controller that employs the ankle, hip, and stepping strategies.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3297-3316"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915329","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":"Night-Voyager: Consistent and Efficient Nocturnal Vision-Aided State Estimation in Object Maps","authors":"Tianxiao Gao;Mingle Zhao;Chengzhong Xu;Hui Kong","doi":"10.1109/TRO.2025.3548540","DOIUrl":"10.1109/TRO.2025.3548540","url":null,"abstract":"Accurate and robust state estimation at nighttime is essential for autonomous robotic navigation to achieve nocturnal or round-the-clock tasks. An intuitive question arises: can low-cost standard cameras be exploited for nocturnal state estimation? Regrettably, most existing visual methods may fail under adverse illumination conditions, even with active lighting or image enhancement. A pivotal insight, however, is that streetlights in most urban scenarios act as stable and salient prior visual cues at night, reminiscent of stars in deep space aiding spacecraft voyage in interstellar navigation. Inspired by this, we propose Night-Voyager, an object-level nocturnal vision-aided state estimation framework that leverages prior object maps and keypoints for versatile localization. We also find that the primary limitation of conventional visual methods under poor lighting conditions stems from the reliance on pixel-level metrics. In contrast, metric-agnostic, nonpixel-level object detection serves as a bridge between pixel-level and object-level spaces, enabling effective propagation and utilization of object map information within the system. Night-Voyager begins with a fast initialization to solve the global localization problem. By employing an effective two-stage cross-modal data association, the system delivers globally consistent state updates using map-based observations. To address the challenge of significant uncertainties in visual observations at night, a novel matrix Lie group formulation and a feature-decoupled multistate invariant filter are introduced, ensuring consistent and efficient estimation. Through comprehensive experiments in both simulation and diverse real-world scenarios (spanning approximately 12.3 km), Night-Voyager showcases its efficacy, robustness, and efficiency, filling a critical gap in nocturnal vision-aided state estimation.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"2105-2126"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570475","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":"Meta-Learning Enhanced Model Predictive Contouring Control for Agile and Precise Quadrotor Flight","authors":"Mingxin Wei;Lanxiang Zheng;Ying Wu;Ruidong Mei;Hui Cheng","doi":"10.1109/TRO.2025.3567491","DOIUrl":"10.1109/TRO.2025.3567491","url":null,"abstract":"In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the quadrotor, especially during high-speed maneuvers. Traditional control models based on first principles struggle to capture these dynamics due to the complexity and variability of aerodynamic effects, which are challenging to model accurately. To address these challenges, this study proposes a meta-learning-based control strategy for accurately modeling quadrotor dynamics under varying speeds, treating each velocity condition as an independent learning task with a specifically trained neural network to ensure precise dynamic predictions. The meta-learning framework rapidly generates task-specific parameters adapted to speed variations by solving an optimization problem and employs an online incremental learning strategy to integrate real-time data for continuous model updates, enhancing system robustness. Regularization is introduced to prevent overfitting and improve generalizability. The integration of the meta-learned model into Model Predictive Contouring Control (MPCC) allows the system to achieve optimal control across different velocity levels, ensuring efficient and accurate flight control even during sharp turns and high-speed maneuvers. Extensive simulations and real-world experiments confirm that the proposed algorithm maintains a high level of control precision despite the nonlinear effects of rapid speed changes, complex flight trajectories and wind disturbances. The results highlight the advantages of combining meta-learning with adaptive control strategies, providing a robust framework for quadrotors operating in diverse and dynamic environments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3590-3608"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915270","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":"Embedded Hierarchical MPC for Autonomous Navigation","authors":"Dennis Benders;Johannes Köhler;Thijs Niesten;Robert Babuška;Javier Alonso-Mora;Laura Ferranti","doi":"10.1109/TRO.2025.3567529","DOIUrl":"10.1109/TRO.2025.3567529","url":null,"abstract":"To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3556-3574"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915331","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":"Formulating the Unicycle on the Sphere Path Planning Problem as a Linear Time-Varying System","authors":"Federico Thomas;Jaume Franch","doi":"10.1109/TRO.2025.3567525","DOIUrl":"10.1109/TRO.2025.3567525","url":null,"abstract":"The kinematics, dynamics, and control of a unicycle moving without slipping on a plane has been extensively studied in the literature of nonholonomic mechanical systems. However, since planar motion can be seen as a limiting case of the motion on a sphere, we focus our analysis on the more general spherical case. This article introduces a novel approach to path planning for a unicycle rolling on a sphere while satisfying the nonslipping constraint. Our method is based on a simple yet effective idea: first, we model the system as a linear time-varying dynamic system. Then, leveraging the fact that certain such systems can be integrated under specific algebraic conditions, we derive a closed-form expression for the control variables. This formulation includes three free parameters, which can be tuned to generate a path connecting any two configurations of the unicycle. Notably, our approach requires no prior knowledge of nonholonomic system analysis, making it accessible to a broader audience.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3335-3347"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915332","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}