Journal of Field Robotics最新文献

{"title":"Performance Evaluation and Improvement for RGB-D Cameras on High-Throughput Phenotyping Robots","authors":"Zhengqiang Fan,&nbsp;Na Sun,&nbsp;Samy Farid Mohamed Assal,&nbsp;Qinghan Hu,&nbsp;Tao Li","doi":"10.1002/rob.70096","DOIUrl":"10.1002/rob.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>RGB-D cameras are widely used in indoor robots. However, their ranging capability for agricultural robots under natural lighting still needs to be evaluated. Especially in the field of robotics-based high-throughput crop phenotyping, the measurement accuracy of phenotypic parameters is deeply related to the ranging performances of RGB-D cameras. In this paper, we propose a depth-ranging evaluation framework and an online ranging compensation strategy for RGB-D cameras on phenotyping robots. The goal is to acquire high-quality depth-ranging performances for plant phenotyping tasks. First, we evaluate ranging performances of RealSense D435i and Kinect V2 under typical phenotyping scenes with different lighting conditions, verify their feasibility on different maize organ observations in different growth periods, and give the optimal observation ranging areas. Second, we employ image brightness to reflect the lighting situations, and propose a novel ranging compensation strategy to decrease the lighting influences in real-time. The results of sufficient field experiments show that RealSense D435i has better ranging performances than Kinect V2 for crop phenotyping, especially for open-field, in-row, and close-range observations. The optimal ranging area of RealSense D435i is within a region of [0.16–1.2] m. However, Kinect V2 is not suitable for field phenotyping robots due to significant interference from natural sunlight, limited measurement range, and instability in depth measurements under outdoor conditions. In addition, we also verify that our online depth error compensation strategy can effectively reduce the influences of lighting intensity and target distance on the depth ranging of RGB-D cameras. Although we test and verify our ranging evaluation framework and ranging error compensation strategy with two old-fashion cameras, the framework and strategy are generic and applicable to other new RGB-D cameras.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1549-1567"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707856","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":"YOLO v9-S Net: YOLO V9 Squeeze SegNet for Object Detection Using Vehicle Image","authors":"Selvamuthukumar Thirumavalavan,&nbsp;Vijayalakshmi Kaliyaperumal,&nbsp;Abinaya Ramaiyan,&nbsp;Dhanalakshmi Pattusamy","doi":"10.1002/rob.70107","DOIUrl":"10.1002/rob.70107","url":null,"abstract":"<div>\u0000 \u0000 <p>Object detection plays a vital role in autonomous driving vehicular systems and intelligent transportation for better environment perception by understanding and analyzing the scenes. Accurate and real-time object detection is critical for autonomous driving and intelligent transportation systems to perceive and understand complex traffic environments. However, existing object detection techniques often suffer from high computational costs and longer processing times, limiting their efficiency in real-world settings. This creates a need for a more computationally efficient and precise detection method that can robustly identify objects from vehicle images. Thus, this article presented a You Only Live Once v9 Squeeze M-SegNet (YOLO v9-S Net) for the detection of objects from vehicle images. To accurately detect objects, the input vehicle images are initially denoised using an adaptive weighted median filter. The enhancement of the denoised vehicle image is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the image quality. Following this, the segmentation of objects is executed using fast fuzzy clustering, and the objects are accurately detected from the segmented object using the YOLO v9-S Net model. The results obtained from the experiment demonstrate that the YOLO v9-S Net approach attained high detection performance with F1-score, recall, and precision of 92.81%, 92.58%, and 93.04%.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1608-1625"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708226","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":"Real-Time Multi-View Flower Counting With a Ground Mobile Robot","authors":"Daniel Petti,&nbsp;Changying Li,&nbsp;Peng Chee","doi":"10.1002/rob.70093","DOIUrl":"10.1002/rob.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>Although season-long cotton flowering time characterization has value to breeders and growers, a manual data collection process is too laborious to be practical in most cases. In recent years, several fully automated flower counting approaches have been proposed. However, such approaches are typically designed to run offline and require a significant amount of computation. Furthermore, little thought has gone into developing convenient interfaces and integrations so that a layperson can use such systems without extensive training. The goal of this study is to develop a flower tracking system that is deployable on a ground robot and can operate in real time. A previous GCNNMatch++ approach was modified to increase the inference speed. Additionally, data from multiple cameras were fused to avoid canopy occlusions, and three-dimensional flower locations were extracted by integrating GPS data from the robot. It is shown that the approach significantly outperforms UAV-based counting and single-camera counting while running at above 40 FPS on an edge device, achieving a counting error of 15. Overall, it is believed that the highly integrated, automated, and simplified flower counting solution makes significant strides toward a practical commercial cotton phenotyping platform.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1484-1510"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708333","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 and Adaptation of Robotic Vision in the Real World: The Challenge of Door Detection","authors":"Michele Antonazzi,&nbsp;Matteo Luperto,&nbsp;N. Alberto Borghese,&nbsp;Nicola Basilico","doi":"10.1002/rob.70084","DOIUrl":"10.1002/rob.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous service robots are becoming increasingly common in human-centric, long-term deployments in unstructured indoor environments. <i>Robotic vision</i> is a crucial capability, enabling robots to perceive and interpret high-level environmental features from visual input. While data-driven approaches based on deep learning have advanced the capabilities of vision systems, applying these techniques in real robotic scenarios still presents unique methodological challenges. Conventional datasets often do not represent the object categories that a service robot needs to detect. More importantly, state-of-the-art models struggle to address the demanding perception constraints faced by service robots, posing the need for adaptations to the specific environments in which the robots operate. We devise a method that addresses these challenges by leveraging photorealistic simulations to create synthetic visual datasets from a robot's perspective. This approach balances data quality with acquisition costs, enabling the training of deep, general-purpose detectors tailored for service robots. We then demonstrate the benefits of qualifying a general detector for the domain in which the robot is deployed, studying the trade-off between data-acquisition efforts and performance improvement. We evaluate our method using a representative selection of prominent deep-learning object detectors for the challenge of recognizing, in real time, the presence and traversability of doorways. This task, which we refer to as <i>door detection</i>, is fundamental to numerous significant robotic tasks, such as tracking the changing topology of dynamic environments. We conduct an extensive experimental campaign in the field, considering different real-world setups while emulating the typical challenges encountered in long-term deployments of service robots. Our key findings demonstrate that simulation and qualification techniques can significantly reduce costs associated with domain adaptation for service robots. While simulation allows embedding the robot's perspective during the training of end-to-end robotic vision modules, qualification is essential to improve their robustness over challenging detection instances, thus reaching the performance level typically required by realistic long-term deployments of service robots.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1299-1331"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708050","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":"Optimizing Resource Allocation in Multi-Robot Systems Through Game-Theoretic Strategies: A Two-Stage Model Approach","authors":"Sun Xiaoyao,&nbsp;Shen Yuong Wong","doi":"10.1002/rob.70098","DOIUrl":"10.1002/rob.70098","url":null,"abstract":"<div>\u0000 \u0000 <p>In complex and dynamic environments, the decision-making sequence of individual robots significantly influences the effectiveness of collaboration and cooperation among multi-robot systems in completing tasks. This paper focuses on the division of labor in autonomous multi-robot systems, aiming to find optimal strategies for resource allocation among robots operating in complex scenarios. Each robot makes independent yet interacting decisions in relatively isolated dynamic environments. We propose a model that applies game theory from economics, classifying the robots into resource-providing robots and resource-consuming robots. Resource providers acquire resources and compete to determine the optimal strategy, while resource consumers purchase resources, making decisions based on the pricing set by providers. The problem is formulated as a two-stage game. In the first stage, resource providers engage in resource games, abstracted into Cournot or Stackelberg models, where optimal decisions are made based on available resources and estimated strategies of other participants. The second stage involves price games between providers and consumers, analogous to market supply and demand relationships. Price adjustments and demand changes lead to the discovery of Nash equilibria in the price game. Simulations were conducted to compare the system-wide benefits when providers adopt different strategies in the first stage. Results indicate that using the Stackelberg model yields higher overall benefits, further demonstrating the practicality and effectiveness of the proposed strategies. This highlights the importance of strategic model selection in optimizing the performance and resource efficiency of multi-robot systems operating in dynamic environments.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1511-1523"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708334","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":"Inverse Kinematics Solution and Application of a 5-DoF Robotic Arm Based on an Improved Dung Beetle Optimization Algorithm","authors":"Ziru Zhou,&nbsp;Yaohua Wu,&nbsp;Wenkai Ma,&nbsp;Yingying Wu,&nbsp;Zhiyong Han,&nbsp;Changsheng Yang","doi":"10.1002/rob.70095","DOIUrl":"10.1002/rob.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents an improved Dung Beetle Optimization (DBO) algorithm assisted by a neural network to solve the inverse kinematics of a 5-DoF (degree-of-freedom) warehouse loading and unloading robot. The neural network was trained on pose data of the robot arm's end-effector and generated predicted joint angles as output. A genetic algorithm was employed to enhance the design of the network. The selected network structure has been demonstrated to reduce the mean Euclidean Distance (ED) of the end-effector positions to below 9 mm. To achieve more accurate inverse kinematics predictions, experimental tuning was conducted to determine the optimal configuration for the DBO algorithm. Then we developed a hybrid model that integrated the neural network and the DBO algorithm. Experimental results indicated that the positioning errors of the DBO and hybrid algorithms were significantly reduced compared to those of the neural network. Moreover, the proposed model reduced the total inference time by 20.7% and the mean ED by 16.4% compared to the DBO algorithm. In trajectory planning validation experiments, all fitting errors were less than 5 mm, thereby meeting the practical requirements for warehouse handling. Therefore, the proposed neural network-assisted evolutionary algorithm outperforms both the neural network and DBO algorithms, providing a fast and accurate solution to the inverse kinematics problem of the warehouse manipulator.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1363-1374"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708091","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":"A Cascaded Strategy With Embodied Artificial Intelligence: Forward Kinematics Solutions for CCRobot-S","authors":"Zhenliang Zheng,&nbsp;Yongyuan Xu,&nbsp;Xuchun He,&nbsp;Tin Lun Lam,&nbsp;Ning Ding","doi":"10.1002/rob.70140","DOIUrl":"10.1002/rob.70140","url":null,"abstract":"&lt;p&gt;This paper presents a novel cable-climbing mechanism: the Collaborative Climbing Robot Squad (CCRobot-S), a variant of Reconfigurable Cable-Driven Parallel Robots (R-CDPR), specifically designed for the inspection and maintenance of stay cables. The forward kinematics of the CCRobot-S robotic system, however, is inherently mathematically intractable. This research proposes a novel cascaded strategy with Embodied Artificial Intelligence (EAI) to effectively tackle the forward kinematics problem. In this proposed strategy, a lightweight deep learning-based model integrated with numerical method optimization supplants traditional methods, providing feedback on the poses of the flying platform to the control loop of the CCRobot-S robotic system. It provides an approximate solution as initial values through a deep neural network by learning from physical or simulated interactive experiences of CCRobot-S, and then transfers the suitable initial values with kinematic constraints or physical constraints that are near the real solution to the numerical method. This process achieves a stable and robust solution for the forward kinematics of CCRobot-S. This article includes the foundational kinematic analysis of CCRobot-S, the formulation of the CCRobot-S model, a comprehensive introduction and analysis of the cascaded strategy, including the dataset preparation, the training configuration, the solution inference, and the numerical method optimization. Comprehensive evaluations and experiments were undertaken to examine the proposed strategy. The results reveal and confirm that the deep-learning neural network implemented in the CCRobot-S robotic system is effective. Additionally, the proposed cascaded strategy achieves higher prediction accuracy than the standalone neural network approach under the condition of real-time execution (position error reduced from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.43&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;1.88&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; mm to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0.16&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;0.73&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; mm in the X direction, from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;2.72&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 \u0000 &lt;mn&gt;4.","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1255-1271"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708090","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":"Improved Ivy Algorithm for an Enhanced Fractional Order Proportional-Integral-Derivative Controller: Applications From Winding Tension Control to Hybrid System","authors":"Xinlei Fang,&nbsp;Jianglin Liu,&nbsp;Jiaquan Xie,&nbsp;Zhongwei Feng,&nbsp;Yinhui Li,&nbsp;Xiaoxiang Zhang,&nbsp;Yuhang Fan,&nbsp;Jianguo Liang","doi":"10.1002/rob.70101","DOIUrl":"10.1002/rob.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>The multi-filament carbon fiber winding technique is an advanced and novel process, where tension control and regulation during winding play a crucial role in determining product performance. Based on this, this paper introduces fractional-order modeling to derive the physical model of the fiber winding process and optimizes the design of the traditional fractional-order proportional-integral-derivative (FOPID) controller, to obtain the self-coupling time delay fractional-order proportional-integral-derivative control strategy (ST-FOPID). The stability region of the tension control system is numerically computed and visually analyzed. Furthermore, this study improves the metaheuristic Ivy algorithm (IVYA) and proposes an improved version, the improved Ivy algorithm (IIVYA). Large-scale benchmark function experiments and performance comparisons with various algorithms, including the original Ivy algorithm, demonstrate significant improvements in convergence speed, global search capability, and avoidance of local optima. As a result, we propose a self-coupling time delay fractional-order proportional-integral-derivative control strategy (IIVYA-ST-FOPID) based on the improved Ivy algorithm, which is successfully applied to industrial systems such as simple mathematical model, DC motor speed control system, servo tension control system, and hybrid multi-area power system. Comparative analyses with various existing control strategies confirm the superior performance and broad applicability of the proposed method. Finally, both numerical simulations and experimental results show that the tension control system has excellent dynamic performance and stability under the proposed control strategy, with a significant performance improvement. Tension fluctuations can be controlled within 5% in slow winding conditions, while in fast winding scenarios, they remain around 6%. Moreover, the proposed control strategy reduces the settling time by more than 0.1 s, demonstrating the excellent performance of the proposed control strategy in complex industrial control systems.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1375-1414"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708218","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 3, May 2026","authors":"Zhenliang Zheng,&nbsp;Yongyuan Xu,&nbsp;Xuchun He,&nbsp;Tin Lun Lam,&nbsp;Ning Ding","doi":"10.1002/rob.70212","DOIUrl":"10.1002/rob.70212","url":null,"abstract":"<p>The cover image is based on the article <i>A Cascaded Strategy With Embodied Artificial Intelligence: Forward Kinematics Solutions for CCRobot-S</i> by Zhenliang Zheng et al., https://doi.org/10.1002/rob.70140.\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 3","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.70212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708098","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":"Formation Control and Experiment for Propeller-Driven Car-Like Robots With Amplitude and Rate Saturation Under Jointly Connected Topology","authors":"Sun Yang,&nbsp;Guo Yong,&nbsp;Li Aijun","doi":"10.1002/rob.70100","DOIUrl":"10.1002/rob.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a formation controller for propeller-driven car-like robots is developed, which is subject to input amplitude, rate saturation, and the jointly connected topology. First, the model of the propeller-driven car-like robot is established, where actuator dynamics, amplitude, and rate saturation are considered. Second, the Gauss integration function is used to approximate the input saturation. Rate saturation will be converted into command input saturation and will be achieved by an auxiliary system. Third, the formation controller is developed based on the backstepping control method, where the adaptive robust controller and neural networks are combined to deal with unmodeled dynamics and external disturbances. According to the Lyapunov stability theory, it is proved that the propeller-driven multirobot system will be stable under the developed controller, while signals in the closed-loop system are ultimately uniformly bounded. Finally, simulation and experiment results verify the effectiveness of the proposed formation control scheme.</p>\u0000 </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"43 3","pages":"1432-1453"},"PeriodicalIF":5.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708322","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}