Journal of Field Robotics最新文献

{"title":"Review of Essential Generic Technologies for Visual Perception in Underground Coal Mine Robots","authors":"Yuxin Du,&nbsp;Jianhua Zhang,&nbsp;Liang Liang,&nbsp;Bin Song","doi":"10.1002/rob.70218","DOIUrl":"https://doi.org/10.1002/rob.70218","url":null,"abstract":"<div>\u0000 \u0000 <p>Visual perception technology serves as the core support for establishing the “perception-decision-control” closed-loop system of underground coal mine robots (UCMRs). Research on its key generic technologies is crucial to promoting the large-scale deployment of UCMRs in underground mines. This paper initially reviews the development status of UCMRs and elaborates on the functional roles and application scenarios of five core categories: tunneling, coal mining, auxiliary operations, inspection, and rescue robots, thereby clarifying the fundamental requirements of visual perception across diverse underground scenes. Subsequently, it concentrates on the generic technological system essential for UCMR visual perception, presenting an in-depth analysis of the principles, research advancements, and application challenges related to four technical modules: environmental adaptability under extreme conditions, target detection and recognition in complex scenarios, 3D spatial perception and positioning, and lightweight algorithms suitable for edge computing. Finally, aligned with practical industry needs, the paper projects future development trends and practical challenges, proposing four strategic pathways for breakthroughs, namely human-machine-environment integrated active perception, automated iteration of datasets and models, cluster perception and interaction, and cross-technology integration, and highlighting four core challenges, including environmental adaptability, hardware reliability, communication collaboration, and industry access compatibility. It aims to provide a valuable theoretical reference and technical support for the technological development, achievement transformation, and advancement of mine intellectualization.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3818-3844"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710532","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":"Redefining Optimal Coverage Path Planning for FLS-Equipped AUVs With Deep Reinforcement Learning","authors":"Lorenzo Cecchi,&nbsp;Alberto Topini,&nbsp;Alessandro Bucci,&nbsp;Alessandro Ridolfi","doi":"10.1002/rob.70209","DOIUrl":"https://doi.org/10.1002/rob.70209","url":null,"abstract":"<p>Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage. Within this research activity, we propose a Deep Reinforcement Learning (DRL)-based framework for CPP in underwater environments using a Forward-Looking Sonar (FLS). We validate the proposed methodology through simulation experiments comparing it with the classical lawnmower path and a state-of-the-art sampling-based algorithm. Results demonstrate that our DRL-based solution outperforms these baseline approaches in terms of coverage time per unit area and path length. Additionally, we present on-field deployment outcomes on FeelHippo AUV, showcasing the feasibility and practicality of our framework in real-world underwater missions.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3617-3632"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710433","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":"The Shifting Paradigms of Disaster Robotics Three Decades of Research","authors":"Qin Hu,&nbsp;Komagata Tomoko,&nbsp;Kanbara Sakiko,&nbsp;Ting Yu,&nbsp;Yan Jiang","doi":"10.1002/rob.70223","DOIUrl":"https://doi.org/10.1002/rob.70223","url":null,"abstract":"<p>The increasing frequency and magnitude of natural disasters worldwide have raised the demand for advanced robotic systems in disaster response. In this research, a comprehensive bibliometric analysis of 2,201 Web of Science database papers was investigate systematically global research trends, patterns and emerging frontiers in disaster robotics. CiteSpace, the Bibliometrix R package, and VOSviewer were used for data analysis. The results reveal a fluctuating yet consistently rising publication trends from 1992 to 2025. Asia leads global research output, with Japan as the most productive country but with limited international collaboration, while European countries engage in more extensive international cooperation. The Journal of Field Robotics is identified as the most influential publication in this field. Over time, research on disaster robots has gradually shifted from hardware development to intelligence technologies, such as deep learning and perception-enabled robotic systems. The study provides valuable insights to guide future research directions and strengthen global collaboration in disaster robotics.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3783-3793"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710439","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":"Biomimetic Multifinger Tactile Sensing and Contact-Regulated Palpation for Autonomous Breast Tumor Localization","authors":"Kai Cheng,&nbsp;Yuanyuan Shen,&nbsp;Huanhai Zhang,&nbsp;Baifeng Li,&nbsp;Chao Cheng,&nbsp;Hamid Reza Karimi,&nbsp;Hang Su,&nbsp;Samer Alfayad","doi":"10.1002/rob.70226","DOIUrl":"https://doi.org/10.1002/rob.70226","url":null,"abstract":"<p>Early detection of breast abnormalities remains challenging: manual palpation is subjective and operator-dependent, while imaging modalities may miss small or subtle stiffness anomalies. This paper presents a biomimetic multifinger robotic palpation approach intended to support early breast-cancer screening and follow-up assessment as a proof-of-concept. The system integrates tactile arrays with a contact-regulation scheme under standardized protocols. Each fingertip produces spatial tactile heatmaps, and a human-like multifinger pressing strategy is used to elicit finger-wise normal-interaction responses under controlled contact conditions. The feedback variable is a resultant tactile signal obtained by aggregating taxel readings and is treated as a proxy of normal interaction rather than an absolute force measurement. A real-time Kalman filter is employed to improve signal fidelity during dynamic contact. The platform is validated on breast-inspired silicone phantoms with embedded rigid inclusions at varying depths and orientations. Across the tested scenarios, the system achieves repeatable real-time localization, with stiffness-weighted centroid errors within 10 mm of the nominal inclusion coordinates and a low incidence of spurious detections under the standardized protocol. We clarify that this study is a proof-of-concept focusing on stiffness anomaly localization under controlled phantom conditions rather than clinical diagnosis or benign/malignant classification.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3939-3954"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710459","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":"Combining Neural Network and RRT*: A Novel Path Planning Method for Hyper-Redundant Manipulators With 2N + 1 DOF","authors":"Zhe Wang,&nbsp;Dean Hu,&nbsp;Detao Wan,&nbsp;Chang Liu,&nbsp;Wei Xiao","doi":"10.1002/rob.70228","DOIUrl":"https://doi.org/10.1002/rob.70228","url":null,"abstract":"<div>\u0000 \u0000 <p>Path planning has long been a research focus for hyper-redundant manipulators. However, in complex environments, the process often entails substantial computational costs. This issue is compounded by kinematic constraints. Therefore, achieving efficient path planning under these conditions remains a significant challenge. To address this issue, this paper proposes a novel path planning method for hyper-redundant manipulators with 2n + 1 degrees of freedom (DOF), which integrates a Neural Network model with the Rapidly-exploring Random Tree Star (RRT*) algorithm. First, a general kinematic model of the manipulator is established. Based on this model, the relationship between the manipulator's joint angles and the path corner angle is analyzed. This relationship is then incorporated into the RRT* algorithm to ensure that the generated paths comply with the kinematic constraints of the manipulator. Next, a Neural Network model is developed and trained using a dataset generated by the improved RRT* algorithm. The trained model predicts path points toward a designated goal. To address potential non-ideal path points in the predictions, a modification strategy combining the Artificial Potential Field (APF) method and a spatial meshing strategy is introduced. This strategy employs a dual adjustment mechanism to reposition non-ideal path points, enabling obstacle avoidance and mitigating the issue of bilateral distribution in the predicted path points. The predicted path points are then incorporated into the sampling process of the improved RRT* algorithm to guide tree expansion, thereby accelerating the path search. Additionally, the APF method is integrated into the search mechanism to further enhance planning efficiency. A series of experiments was conducted to evaluate the performance of the proposed method. The results demonstrate that the manipulator successfully reaches designated targets along the generated paths while avoiding obstacles and adhering to kinematic constraints. Compared to the RRT and RRT* algorithms, the proposed method shows superior overall performance, particularly in computation time, where it achieves a reduction of over 50% compared to RRT*. Prototype experiments further confirm the feasibility and safety of the paths generated by the proposed approach.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3893-3916"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710536","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":"Soft Computing Techniques Applied to Adaptive Hybrid Navigation Methods for Tethered Robots in Dynamic Environments","authors":"Chandan Sheikder,&nbsp;Weimin Zhang,&nbsp;Xiaopeng Chen,&nbsp;Fangxing Li,&nbsp;Yichang Liu,&nbsp;Xiaohai He,&nbsp;Zhengqing Zuo,&nbsp;Xinyan Tan","doi":"10.1002/rob.70222","DOIUrl":"https://doi.org/10.1002/rob.70222","url":null,"abstract":"<div>\u0000 \u0000 <p>Tethered robots face unique constraints: the physical tether can cause mission failure through snagging or excessive tension. Existing methods either require global maps (Tethered A*) or lack tether awareness (Bug algorithms). We present the first hybrid framework combining reactive obstacle avoidance with predictive tether management. Our system integrates (1) a tether-aware cost function for 3–5 step snag prediction, (2) fuzzy logic for dynamic tension adaptation, and (3) genetic algorithms for tether routing optimization. Validation on a humanoid robot (ATLAS-T) with instrumented sensors demonstrates 52% fewer snag incidents versus state-of-the-art methods (Tethered A*, TSLAM), 94.7% success in tether-critical scenarios, and 43% better odometry-drift resilience. Real-world experiments confirm practical viability.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3752-3782"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710438","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":"Intelligent Autonomy: A Novel Hybrid Navigation System for Autonomous Load-Haul-Dump Vehicles","authors":"Yuanjian Jiang,&nbsp;Pingan Peng,&nbsp;Xiaofeng Huo,&nbsp;Jiaheng Wang,&nbsp;Liguan Wang","doi":"10.1002/rob.70214","DOIUrl":"https://doi.org/10.1002/rob.70214","url":null,"abstract":"<div>\u0000 \u0000 <p>The automation and intelligence of underground mining vehicles are vital for ensuring safety and improving production efficiency, representing an essential trend in the evolution of the mining industry. However, achieving autonomous navigation for load-haul-dump (LHD) vehicles in GPS-denied underground environments poses significant challenges. To address these challenges, we introduce a novel hybrid navigation (HN) strategy that combines the strengths of absolute navigation (AN), which relies on precise localization using pre-mapped environments, with reactive navigation (RN), which utilizes real-time sensor data for immediate navigation decisions. In this strategy, the AN facilitates map-referenced positioning during turns, while the RN dynamically adjusts the trajectory on straight segments through real-time sensor feedback, independent of absolute localization. This integration enhances the robustness of navigation. We conducted simulation experiments to compare RN, AN, and HN systems. The results demonstrate that the HN system effectively merges the adaptability of RN with the precision of AN, ensuring reliable navigation through narrow intersections and stable performance on straight paths. Field trials further validated the HN system's ability to operate an LHD vehicle at a linear speed of approximately 1.8 m/s and a turning speed of 0.6 m/s, underscoring its practical applications in real-world scenarios. These findings highlight the HN system's potential for robust autonomous operation in complex underground environments.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3794-3817"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710454","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":"Actuation Strategies for Underwater Jet-Propelled Soft Robots","authors":"Angel Kitone,&nbsp;Mohammed Anteet,&nbsp;Pawandeep S. Matharu,&nbsp;Yara Almubarak","doi":"10.1002/rob.70224","DOIUrl":"https://doi.org/10.1002/rob.70224","url":null,"abstract":"<p>This review article examines jet-propulsion mechanisms in underwater soft robotic systems, focusing exclusively on physically fabricated and experimentally validated robots. Covering research published from 2013 to 2025, this study classifies and evaluates jet-propulsion robots based on their actuation mechanisms. This review outlines the fundamental working principles, discusses the key advantages and limitations, and assesses the practicality of these mechanisms for underwater applications. Additionally, a list of robots employing each actuation method is presented, illustrating the diversity of approaches within the field. By systematically comparing these studies, this review identifies critical performance trade-offs, potential challenges, and opportunities for innovation in jet-propulsion-based underwater robotics. Ultimately, this work serves as a valuable resource for researchers and engineers, facilitating advancements in the design and application of bioinspired underwater jetting robots.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 6","pages":"3868-3892"},"PeriodicalIF":5.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710534","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":"Bio-Inspired Pneumatic Modular Soft Robots","authors":"Zilong Xie,&nbsp;Jialiang Sun,&nbsp;Li Zhang,&nbsp;Shengyu Zhang,&nbsp;Qiu Chen,&nbsp;Xinbao Liu","doi":"10.1002/rob.70183","DOIUrl":"https://doi.org/10.1002/rob.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional rigid robots struggle to adapt to complex settings such as post-disaster rescue and pipeline inspection. To address this limitation, we propose a spiral actuator and a bending actuator based on the extensile McKibben artificial muscles. By adjusting the wrapping angle of the reinforcing fibers, the actuator can switch between bending deformation and spiral deformation. In addition, theoretical models are proposed to predict the deformation behavior of the actuators and verified by experiments. These actuators function as modular components for constructing soft robots with diverse capabilities. In this paper, four different modular soft robots are designed including a vine-inspired spiral gripper, an octopus-inspired soft gripper, a snake-inspired crawling robot, and a monkey-inspired double-rods crawling robot. Experiments demonstrate the significant potential of modular soft robots for performing tasks such as maintenance, detection, surveillance, and manipulation in diverse unstructured environments.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 5","pages":"3043-3061"},"PeriodicalIF":5.1,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533558","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 ORB–SLAM2 Deep-Sea Visual Localization Technique Based on Image Enhancement","authors":"Jichao Yang,&nbsp;Qing Zhao,&nbsp;Yong Xu,&nbsp;Zhongjun Ding,&nbsp;Tongbo Xu,&nbsp;Jinghao Zhu,&nbsp;Wenhui Dong,&nbsp;Dali Chen,&nbsp;Mengwei Zhao","doi":"10.1002/rob.70194","DOIUrl":"https://doi.org/10.1002/rob.70194","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the poor performance of underwater visual localization caused by image degradation, this paper proposes an improved ORB–SLAM2 underwater visual localization scheme based on image enhancement. First, an improved MSR–CLAHE algorithm is presented, which integrates the global consistency of multiscale Retinex (MSR) with the local contrast enhancement capability of contrast-limited adaptive histogram equalization (CLAHE). Exponential mapping and multiplicative fusion are introduced to balance multiscale enhancement, while guided filtering and HSV correction are employed to suppress halo artifacts and restore natural color balance. Experimental evaluations on the EUVP, SUIM, and Jiaolong deep-sea data sets demonstrate that the proposed algorithm achieves the highest average UCIQE and UIQM scores, significantly improving image clarity, contrast, and color fidelity. Subsequently, an improved version of the ORB–SLAM2 algorithm based on the proposed MSR–CLAHE algorithm is proposed. Experimental results of deep-sea visual localization show that the proposed method greatly increases the number of matched feature points and enhances both trajectory tracking accuracy and system robustness compared with the original approach. In summary, the improved ORB–SLAM2 underwater visual localization scheme based on image enhancement proposed in this paper can effectively track the motion trajectory of underwater robots, meeting the requirements for accurate and robust underwater autonomous localization.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 5","pages":"3312-3331"},"PeriodicalIF":5.1,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148532845","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}