Journal of Field Robotics最新文献

Back Cover Image, Volume 42, Number 6, September 2025 封底图片，42卷，第6期，2025年9月

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-08-20 DOI: 10.1002/rob.70074

SaiXuan Chen, SaiHu Mu, GuanWu Jiang, Abdelaziz Omar, Zina Zhu, Fuzhou Niu

引用次数: 0

Inside Front Cover Image, Volume 42, Number 6, September 2025 封面内图，42卷，第6期，2025年9月

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-08-20 DOI: 10.1002/rob.70072

Qingxiang Wu, Yu'ao Wang, Yu Fu, Tong Yang, Yongchun Fang, Ning Sun

引用次数: 0

Cover Image, Volume 42, Number 6, September 2025 封面图片，42卷，第6期，2025年9月

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-08-20 DOI: 10.1002/rob.70042

Yan Huang, Jiawei Zhang, Ran Yu, Shoujie Li, Wenbo Ding

引用次数: 0

Inside Back Cover Image, Volume 42, Number 6, September 2025 内页封底图片，42卷，第6期，2025年9月

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-08-20 DOI: 10.1002/rob.70073

Zhenliang Zheng, Chao Wang, Xiaoli Hu, Lun Zhang, Wenchao Zhang, Yongyuan Xu, Pengfei Liu, Xufang Pang, Tin Lun Lam, Ning Ding

引用次数: 0

Cover Image, Volume 42, Number 5, August 2025 封面图片，42卷，第5期，2025年8月

IF 4.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-07-22 DOI: 10.1002/rob.70036

Hongchuan Zhang, Junkai Ren, Junhao Xiao, Hainan Pan, Huimin Lu, Xin Xu

引用次数: 0

Robust High-Order Sliding Mode Control for Collecting Objects by a Wheeled Space Rover With a Multi-Articulated Arm 轮式多关节臂空间漫游者采集目标的鲁棒高阶滑模控制

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-07-20 DOI: 10.1002/rob.22608

Andrei Smirnov, Vladimir Budanov, Konstantin Klimov, Dmitrii Kapytov, Isaac Chairez

{"title":"Robust High-Order Sliding Mode Control for Collecting Objects by a Wheeled Space Rover With a Multi-Articulated Arm","authors":"Andrei Smirnov, Vladimir Budanov, Konstantin Klimov, Dmitrii Kapytov, Isaac Chairez","doi":"10.1002/rob.22608","DOIUrl":"https://doi.org/10.1002/rob.22608","url":null,"abstract":"<div>\u0000 \u0000 <p>This study focuses on implementing a super-twisting controller (STC) to manage autonomous rover navigation. STCs have effectively dealt with inherent uncertainties in real-world applications, making them particularly suitable for tasks such as rover navigation. The study addresses designing and implementing an STC-based controlled system tailored to rover navigation scenarios' dynamic and unpredictable nature. STC uses equivalent control schemes to model complex relationships among input variables, minimizing errors in wheel speed, steer mechanism, and 6-DOF robot arm trajectories. The implementation is validated through simulation studies, using representative scenarios to evaluate the controller's performance in demanding environments. Evaluation metrics include trajectory accuracy, obstacle avoidance, and overall system robustness. In addition, the same control action was tested on a rover that was developed by the authors, whose motion corresponds to that considered in the numerical evaluations. The results of this study are intended to provide valuable information on the application of STC for autonomous rover navigation, with implications for improving the reliability and adaptability of robotic exploration missions.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"4009-4026"},"PeriodicalIF":5.2,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129183","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}

引用次数: 0

Underwater Manipulator Trajectory Planning Based on Improved Particle Swarm Optimization Algorithm 基于改进粒子群优化算法的水下机械臂轨迹规划

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-06-25 DOI: 10.1002/rob.22603

Huawei Jin, Guowen Yue

引用次数: 0

Recurrent Neural Network–Based Nonlinear Orientation Control of Redundant Stewart Platform 基于递归神经网络的冗余Stewart平台非线性定向控制

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-06-16 DOI: 10.1002/rob.22605

Ameer Hamza Khan, Xinwei Cao, Shuai Li

{"title":"Recurrent Neural Network–Based Nonlinear Orientation Control of Redundant Stewart Platform","authors":"Ameer Hamza Khan, Xinwei Cao, Shuai Li","doi":"10.1002/rob.22605","DOIUrl":"https://doi.org/10.1002/rob.22605","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a novel Recurrent Neural Network (RNN) controller for redundancy resolution and orientation control of the Stewart platform. The Stewart platform features six prismatic actuators, making it a six-degrees-of-freedom (6-DOF) system. When imposing three-dimensional orientation control, the platform retains a redundancy of 3-DOF, which can be utilized to achieve secondary goals. The key novelty of this study lies in the formulation of a Jacobian-free, gradient-free control strategy that directly solves a constrained nonlinear optimization problem at the angular level, thereby significantly improving computational efficiency and robustness compared with conventional controllers. Specifically, we propose the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, a biologically inspired metaheuristic framework that bypasses the computationally intensive Jacobian inversion typically required in redundancy resolution. The orientation control problem is formulated as a constrained optimization task, incorporating an energy-efficient actuator usage objective and mechanical constraints modeled as inequalities. Theoretical stability and convergence guarantees are established for the proposed BAORNN framework, ensuring reliable operation across a wide range of configurations. To validate the approach, we developed a high-fidelity simulation environment using the Simscape Multibody library in Simulink and conducted extensive experiments across multiple time-varying reference trajectories. Quantitative performance comparisons against a state-of-the-art inverse kinematics controller demonstrate the superior accuracy, convergence speed, and constraint-handling capabilities of our method. Furthermore, we showcase a realistic application scenario by integrating the controller with a chair-mounted Stewart platform for immersive driving and flight simulations, demonstrating the potential for real-world deployment in motion simulation and training systems. In summary, this paper introduces a computationally lightweight, robust, and highly accurate RNN-based controller tailored for redundant Stewart platforms, with proven advantages over traditional Jacobian–based methods.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3952-3967"},"PeriodicalIF":5.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129123","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}

引用次数: 0

Neural LiDAR Odometry With Feature Association and Reuse for Unstructured Environments 基于特征关联和重用的非结构化环境神经激光雷达里程测量

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-06-16 DOI: 10.1002/rob.22607

Liangshu Qian, Wei Li, Yu Hu

{"title":"Neural LiDAR Odometry With Feature Association and Reuse for Unstructured Environments","authors":"Liangshu Qian, Wei Li, Yu Hu","doi":"10.1002/rob.22607","DOIUrl":"https://doi.org/10.1002/rob.22607","url":null,"abstract":"<div>\u0000 \u0000 <p>Odometry plays a crucial role in autonomous tasks of field robots, providing accurate position and orientation derived from sequential sensor observations. Odometry based on Light Detection and Ranging (LiDAR) sensors has demonstrated widespread applicability in environments with rich structured features, such as urban and indoor settings. However, for unstructured environments like scrubland and rural roads, the extraction, description, and correct matching of LiDAR features between frames become challenging. Due to the lack of flat surfaces and straight lines, the existing odometry approaches, whether using hand-crafted features such as edge and planar points or learned features through networks, will face the problem of decreased positioning accuracy and potential failure. Therefore, we propose a neural LiDAR odometry based on Trans-frame Association to extract more effective features for pose estimation in unstructured environments. The Trans-frame Association module contains a fully interactive frame Transformer and a scan-aware Swin Transformer. The former applies cross-attention to features extracted from two consecutive frames, thus enhancing the accuracy and robustness of feature correspondences by considering the contextual information. The latter restricts the attention mechanism to shift along the scan lines of LiDAR, thereby leveraging the sensor's inherent higher horizontal resolution. Our Transformer has linear complexity, which guarantees the module can meet real-time requirements. Additionally, we design a Reuse Refinement Pyramid architecture to further improve the accuracy of pose estimation by reusing multiresolution features. We conducted extensive experiments on the RELLIS-3D data set and our Matian Ridge data set collected in a representative unstructured scene. The results demonstrate that our network outperforms recent learning-based LiDAR odometry methods in terms of accuracy. The code is available at https://github.com/qlsinori/FAR-LO.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3968-3985"},"PeriodicalIF":5.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129122","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}

引用次数: 0

Learning-Based Rapid Phase-Aberration Correction and Control for Robot-Assisted MRI-Guided Low-/High-Intensity Focused Ultrasound Treatments 基于学习的机器人辅助mri引导低/高强度聚焦超声治疗的快速相位像差校正和控制

IF 5.2 2区计算机科学

Journal of Field Robotics Pub Date : 2025-06-11 DOI: 10.1002/rob.22606

Jing Dai, Xiaomei Wang, Bohao Zhu, Liyuan Liang, Hing-Chiu Chang, James Lam, Xiaochen Xie, Ka-Wai Kwok

{"title":"Learning-Based Rapid Phase-Aberration Correction and Control for Robot-Assisted MRI-Guided Low-/High-Intensity Focused Ultrasound Treatments","authors":"Jing Dai, Xiaomei Wang, Bohao Zhu, Liyuan Liang, Hing-Chiu Chang, James Lam, Xiaochen Xie, Ka-Wai Kwok","doi":"10.1002/rob.22606","DOIUrl":"https://doi.org/10.1002/rob.22606","url":null,"abstract":"<p>Magnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effective and noninvasive procedure for treating diseases such as neurological disorders. Phase adjustment on ultrasound transducers can only achieve a limited focal-spot steering range. When treating large abdominopelvic targets, mechanical adjustment on the transducers' position and orientation is the prerequisite for enlarging the steering range. Therefore, we previously designed an MRI-guided robot to manipulate the transducers to offer sufficient focal-spot movement range. This could provide more modulation solutions to constructive ultrasound interference. However, full-wave ultrasound propagation inside a patient's heterogeneous abdominal media is complex and nonlinear, posing significant challenges in ultrasound modulation and beam motion control. Here, we propose a novel learning-based phase-aberration correction and model-free control framework for robot-assisted MRg-FUS treatments. The correction policy guarantees rapid aberration compensation within 5.0 ms. Submillimeter refocusing accuracy is achieved in both the liver (0.32 mm) and pancreas (0.51 mm), meeting clinical requirements for focal targeting. Our controller can accommodate nonlinear phase actuation with fast convergence (< 5.7 ms) and ensure accurate positional tracking with a mean error of 0.26 mm, without prior knowledge of inhomogeneous media. Compared with the conventional model-based method, it contributes to 61.77%–70.39% mean error reduction without requiring model parameter tuning.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3935-3951"},"PeriodicalIF":5.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145129064","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}

引用次数: 0