Journal of Field Robotics最新文献

{"title":"Optimization of Magnetic Adsorption Units for Wall-Climbing Robots via Integrated Response Surface Methodology and Genetic Algorithm","authors":"Zan Wang,&nbsp;Qixiang Zhang,&nbsp;Jinghua Wu,&nbsp;Shuaikang Li","doi":"10.1002/rob.70215","DOIUrl":"https://doi.org/10.1002/rob.70215","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the requirement for stable adsorption of climbing robots in confined spaces during ship hull cleaning operations, this study proposes a multi-objective optimization design method for arc-shaped magnetic adsorption units based on the response surface method-genetic algorithm. By analyzing the influence relationships among magnetic circuit topology, hull plate thickness, and air gap dimensions, the study reveals the superior performance of a five-loop Hallbarth array within specific air gap ranges and identifies the nonlinear impact of arc angle on magnetic adsorption units. A multi-parameter coupling optimization framework linking arc angle to permanent magnet geometric parameters has been established to determine the optimal configuration of permanent magnet structural parameters and arc angle. Experimental validation demonstrates a 15.37% increase in magnetic adsorption force per unit mass after optimization, with robotic stability confirmed through motion testing. This research holds significant value for achieving stable adsorption and engineering applications of wall-climbing robots on ship hull surfaces.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3699-3710"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Collision Avoidance Methods for Logistics Unmanned Aerial Vehicle Based on Dynamic Controlled Interactive Collaborative Fusion","authors":"Yuetan Zhang,&nbsp;Honghai Zhang,&nbsp;Weibin Tang,&nbsp;Wei Pan,&nbsp;Chenfeng Zhang,&nbsp;Caiyu Dai,&nbsp;Yulin Yuan","doi":"10.1002/rob.70219","DOIUrl":"https://doi.org/10.1002/rob.70219","url":null,"abstract":"<div>\u0000 \u0000 <p>As urban air-traffic density rises, the risk of mid-air collisions among low-altitude logistics drones has become a critical concern. We therefore introduce Dynamic Controlled Interactive Collaborative Fusion (DCICF), a framework that couples multimodal perception with Probabilistic Reciprocal Velocity Obstacles (P-RVO) modeling to enable safe, cooperative flight in dynamic environments. Simulations with 50 static obstacles yielded zero collisions; in dynamic-obstacle scenarios the collision rate was 0.083 while each drone completed an average of 2.400 missions; and in clustered multi-drone tasks the collision rate fell to 0.043 with 2.350 missions per drone. Hardware tests further demonstrated an average decision latency of 120–150 ms, minimum separation distances of &gt; 0.8 m indoors and &gt; 1.2 m outdoors, and a 100% mission-success rate. Relative to state-of-the-art baselines such as DQN, MILP and RRT*, DCICF offers superior safety, real-time responsiveness, and scalability, making it a robust solution for urban logistics drone operations.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3845-3867"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Dual-Functional Flexible Ultrasonic Sensor Array of Proximity Sensing and Material Recognition for Safety Control of Robot","authors":"Lijie Zhou,&nbsp;Yuan Li,&nbsp;Zhan Duan,&nbsp;Zhentao Zhou,&nbsp;Tiezhu Liu,&nbsp;Jia Zhang","doi":"10.1002/rob.70225","DOIUrl":"https://doi.org/10.1002/rob.70225","url":null,"abstract":"<div>\u0000 \u0000 <p>The operational safety of robots in complex environments is a critical consideration in contemporary robotics applications, since it directly impacts both task completion and the robot's own integrity. Currently, safety control in robots is predominantly achieved through multimodal sensor fusion technologies involving vision, LiDAR, and radar systems. However, these approaches still present limitations such as high hardware costs, large data volumes, complex data processing requirements, and slow response times. Herein, we present a dual-modal ultrasonic sensing system that simultaneously measures object proximity and identifies material properties, thereby enhancing robotic perception. The system addresses key challenges mentioned above and achieves a 98% classification accuracy for 13 common industrial materials and maintains a proximity sensing error within 3 mm. Meanwhile, the system also maintains a good real-time performance with milliseconds of response. These outcomes contribute to safer robot control and improve environmental suitability.</p></div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3741-3751"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"4SWLR: A Switched System and Skid Steer Integrated Whole-Body Control Framework for Wheeled-Legged Robots","authors":"Mingfan Xu,&nbsp;Ziyi Yang,&nbsp;Chuyan Xu,&nbsp;Jing Zhao,&nbsp;Yechen Qin","doi":"10.1002/rob.70216","DOIUrl":"https://doi.org/10.1002/rob.70216","url":null,"abstract":"<div>\u0000 \u0000 <p>Inspired by mammalian locomotion and vehicle skid-steering principles, this paper proposes a real-time motion planning and tracking control framework for wheeled-legged robots, integrating the obstacle-crossing capability of legged robots with the skid-steering mechanism of wheeled platforms. Unlike conventional wheeled-legged robot control methods that rely on external swing joints, the proposed framework leverages differential wheel actuation while comprehensively accounting for the robot-environment coupling effects under high-speed conditions, enabling efficient and stable high-speed steering. First, a hierarchical wheel-terrain contact dynamics model and a skid-steering kinematics model are established for wheeled-legged robots with skid-steering. By combining switched-system skid-steering kinematics with refined wheel–environment interaction dynamics, the framework effectively addresses active wheel torque control during high-speed steering. Second, a skid-steering-based motion paradigm is introduced, which co-optimizes legged dynamics and wheeled skid-steering kinematics, eliminating the need for continuous leg-lifting maneuvers to generate lateral forces and ensuring smooth high-speed steering. Finally, extensive experiments conducted in challenging environments—including staircases, trenches, ramps, single-side bridges, and unpaved terrains—validate the robustness and efficacy of the proposed approach. Comparative studies with state-of-the-art wheeled-legged control methods further demonstrate the superior mobility performance and enhanced wheel–terrain interaction dynamics achieved by our framework.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3711-3726"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover Image, Volume 43, Number 6, September 2026","authors":"Shaocong Wang,&nbsp;Ting Wang,&nbsp;Shiliang Shao,&nbsp;Cunyi Pan,&nbsp;Kai Zhang,&nbsp;Jinguo Liu","doi":"10.1002/rob.70313","DOIUrl":"https://doi.org/10.1002/rob.70313","url":null,"abstract":"<p>The cover image is based on the article <i>TriRock6W: Autonomous Mobile Robot with Six Wheels, Three Rocker Arms in Complex Environments</i> by shao shiliang et al., https://doi.org/10.1002/rob.70229.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":""},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault Tolerant Attitude Control for Spacecraft Considering Input Delay and Actuator Saturation: Theory and Experiment","authors":"Yasamin Naderi Koupai,&nbsp;Maryam Malekzadeh,&nbsp;Shahram Hadian Jazi","doi":"10.1002/rob.70211","DOIUrl":"https://doi.org/10.1002/rob.70211","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents an advanced fault estimation and control strategy for spacecraft attitude control systems operating under time-varying input delays and actuator saturation. An adaptive fast terminal sliding mode fault-tolerant control (AFTSMFTC) framework is proposed to mitigate actuator faults, estimation errors, and input constraints. Two adaptive laws are designed to dynamically tune the control gains, thereby enhancing performance and robustness under fault conditions. A multipurpose sliding surface is formulated to ensure that the reaction wheels come to rest after maneuvering, improving momentum management and energy efficiency. Furthermore, a nonlinear disturbance observer (NDO) is developed to estimate external disturbances, unmodeled inertia variations, and time-varying input delays, while an integrated fault detection mechanism identifies actuator failures in real time. The closed-loop stability is rigorously proven using Lyapunov theory. The proposed controller and observer are validated through a high-fidelity spacecraft attitude control simulator, with results demonstrating superior performance and robustness compared to state-of-the-art methods.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3955-3967"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Six-Dimensional Digital Twin System for Autonomous Underwater Vehicles: Conceptualization and Twin Experiments","authors":"Lin Yu,&nbsp;Lei Qiao","doi":"10.1002/rob.70220","DOIUrl":"https://doi.org/10.1002/rob.70220","url":null,"abstract":"<div>\u0000 \u0000 <p>To promote the efficient, comprehensive, reliable, and low-cost testing and application of intelligent algorithms for autonomous underwater vehicles (AUVs), this paper proposes an innovative six-dimensional digital twin (6D DT) conceptual model and provides detailed engineering implementation strategies of this twin system. This model integrates six core dimensions, including physical entity, virtual entity, virtual native entity (VNE), twin data, services, and communication connection. The concept of VNE is introduced to significantly enhance the practicability, security, and reliability of AUV testing by constructing diversified test scenarios. To implement the proposed model, a high-fidelity underwater Cyberspace visualization is developed using Unreal Engine 5, which improves the granularity of virtual–real mapping and enhances human–computer interaction. An efficient data bridge plugin is implemented to ensure real-time, stable bidirectional communication. The DT system (DTS) supports both offline simulation and online DT modes, enabling flexible testing from pure software simulation to real-time virtual–physical interaction, thereby enhancing the credibility of algorithm validation. Two experimental cases conducted on this DTS demonstrate the technical feasibility and reliability of the proposed conceptual model. The approach provides a valuable reference for applying digital twin technology in underwater unmanned systems and accelerates the development of autonomous intelligent AUVs.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3727-3740"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation","authors":"Kang Xiangli,&nbsp;Yage He,&nbsp;Xianwu Gong,&nbsp;Zehan Liu,&nbsp;Yuru Bai","doi":"10.1002/rob.70205","DOIUrl":"https://doi.org/10.1002/rob.70205","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a grasping method for objects with uneven mass distribution by leveraging diffusion models to localize the center of gravity (CoG) on unknown objects. In robotic grasping, CoG deviation often leads to postural instability, where existing keypoint-based or affordance-driven methods exhibit limitations. We constructed a dataset of 790 images featuring unevenly distributed objects with keypoint annotations for CoG localization. A vision-driven framework based on foundation models was developed to achieve CoG-aware grasping. Experimental evaluations across real-world scenarios demonstrate that our method achieves a 49% higher success rate compared to conventional keypoint-based approaches and an 11% improvement over state-of-the-art affordance-driven methods. The system exhibits strong generalization with a 76% CoG localization accuracy on unseen objects. This provides an innovative solution for precise and stable grasping tasks, with its scientific validity further validated in complex and dynamic scenarios.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3648-3669"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion-Guided and Distillation-Optimized Framework for Freespace Detection in Off-Road Environments","authors":"Jing Lian,&nbsp;Duo Sui,&nbsp;Linhui Li,&nbsp;Xiaofang Yuan,&nbsp;Yaonan Wang,&nbsp;Haoyuan Kang,&nbsp;Shi Chen","doi":"10.1002/rob.70208","DOIUrl":"https://doi.org/10.1002/rob.70208","url":null,"abstract":"<div>\u0000 \u0000 <p>In response to the challenges posed by the dynamic and diverse terrain of off-road environments, the limited scale of data samples, and the insufficient generalization capability of models, this paper proposes an off-road freespace detection framework called OFF-LIP SAM. This framework employs a cascaded fusion approach, using dense point clouds as prompts to interactively guide the inference of the Segment Anything Model (SAM) vision large model. The framework first constructs a point cloud densification algorithm, calculating multi-frame point cloud pose transformation matrices through a lidar inertial odometer and building a dense local point cloud map using a dynamic adaptive keyframe strategy. A dynamic keypoint sampling algorithm is then designed to construct feature point clouds, which are used as prior information to interactively generate regions of interest, enabling knowledge and data to jointly drive SAM model segmentation inference. Finally, this study proposes and implements a two-stage knowledge distillation framework based on dynamic sampling prompts. This significantly improves the SAM inference speed while maintaining accuracy and generalization, making it applicable directly in resource-constrained real-vehicle environments. The experimental results demonstrate that the proposed OFF-LIP SAM detection framework achieves competitive performance on the ORFD, RELLIS-3D, and WildScenes data sets, outperforming current state-of-the-art algorithms and strong baseline models. Real-vehicle experiments conducted on the ORIN vehicle-mounted computing platform validate the feasibility and effectiveness of the OFF-LIP SAM detection framework.</p></div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3670-3698"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual 3D Spatiotemporal Fields-Driven Obstacle Avoidance Using Model Predictive Control for Nursing Robots in Unstructured Environments","authors":"Guoqiang Fu,&nbsp;Yina Wang","doi":"10.1002/rob.70217","DOIUrl":"https://doi.org/10.1002/rob.70217","url":null,"abstract":"<div>\u0000 \u0000 <p>Monocular detectors typically provide 2D features, which limit a nursing robot's ability to perceive and adapt to changes in complex 3D environments. To address this challenge, this paper proposes a monocular 3D object detection framework capable of estimating objects' 3D locations, sizes, velocities, and predicted trajectories. The framework employs a Nursing Robot Convolutional Neural Network (NRCNN) to generate 2D bounding boxes for objects, followed by a 2D-pixel-to-3D-world coordinate transformation algorithm to recover their 3D spatial positions. Subsequently, an ID-assignment-based tracking module associates detections across sequential D435i video frames, enabling prediction of objects' spatiotemporal dynamics. To enhance 2D detection accuracy, a multibranch efficient channel attention (MECA) module is embedded within a cross-stage partial network in the backbone of the NRCNN detector. This design enhances interchannel and cross-layer information interactions to adaptively capture the cross-channel and cross-layer dimensional features. It departs from the traditional approach of improving feature extraction solely through increasingly deep dilated convolutional network structures, thereby enabling more effective feature extraction. Additionally, a bi-directional channel spatial feature fusion pyramid network (Bi-CSFFPN) is integrated into the neck of the NRCNN detector to fuse multi-level, cross-channel, and spatial features. This approach overcomes the limitation of conventional feature fusion networks that only fuse features along a single dimension and results in substantial performance improvements. Using outputs from the monocular 3D object detector, a dynamic lattice map is constructed that integrates real-time object volume, position, and velocity information. This map allows the nursing robot to plan the shortest feasible path from its initial position to the target location while avoiding moving obstacles via a model predictive control (MPC)-based trajectory tracking controller. Extensive experimental results demonstrate that the proposed MECA-CSP and Bi-CSFFPN modules significantly improve NR-CNN detection performance and enhance downstream tasks, including 3D object localization and dynamic lattice-map-based MPC obstacle avoidance control.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3917-3938"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}