Journal of Field Robotics最新文献

{"title":"Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF-IMOSO)","authors":"Qilin Li,&nbsp;Qihua Ma,&nbsp;Xin Weng","doi":"10.1002/rob.22354","DOIUrl":"10.1002/rob.22354","url":null,"abstract":"<p>With the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta-heuristic algorithms have gained popularity owing to their efficiency and problem-specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF-IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF-IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time-savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta-heuristics, APF-IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF-IMOSO can generate multiple viable collision-free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1843-1863"},"PeriodicalIF":4.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832092","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 41, Number 4, June 2024","authors":"Jian Wang,&nbsp;Yuangui Tang,&nbsp;Shuo Li,&nbsp;Yang Lu,&nbsp;Jixu Li,&nbsp;Tiejun Liu,&nbsp;Zhibin Jiang,&nbsp;Cong Chen,&nbsp;Yu Cheng,&nbsp;Deyong Yu,&nbsp;Xingya Yan,&nbsp;Shuxue Yan","doi":"10.1002/rob.22363","DOIUrl":"https://doi.org/10.1002/rob.22363","url":null,"abstract":"<p>The cover image is based on the Research Article <i>The Haidou-1 hybrid underwater vehicle for the Mariana Trench science exploration to 10,908 m depth</i> by Jian Wang et al., https://doi.org/10.1002/rob.22307\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":"41 4","pages":"i"},"PeriodicalIF":8.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814186","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":"VERO: A vacuum-cleaner-equipped quadruped robot for efficient litter removal","authors":"Lorenzo Amatucci,&nbsp;Giulio Turrisi,&nbsp;Angelo Bratta,&nbsp;Victor Barasuol,&nbsp;Claudio Semini","doi":"10.1002/rob.22350","DOIUrl":"10.1002/rob.22350","url":null,"abstract":"<p>Litter nowadays presents a significant threat to the equilibrium of many ecosystems. An example is the sea, where litter coming from coasts and cities via gutters, streets, and waterways, releases toxic chemicals and microplastics during its decomposition. Litter removal is often carried out manually by humans, which inherently lowers the amount of waste that can be effectively collected from the environment. In this paper, we present a novel quadruped robot prototype that, thanks to its natural mobility, is able to collect cigarette butts (CBs) autonomously, the second most common undisposed waste worldwide, in terrains that are hard to reach for wheeled and tracked robots. The core of our approach is a convolutional neural network for litter detection, followed by a time-optimal planner for reducing the time needed to collect all the target objects. Precise litter removal is then performed by a visual-servoing procedure which drives the nozzle of a vacuum cleaner that is attached to one of the robot legs on top of the detected CB. As a result of this particular position of the nozzle, we are able to perform the collection task without even stopping the robot's motion, thus greatly increasing the time-efficiency of the entire procedure. Extensive tests were conducted in six different outdoor scenarios to show the performance of our prototype and method. To the best knowledge of the authors, this is the first time that such a design and method was presented and successfully tested on a legged robot.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1829-1842"},"PeriodicalIF":4.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831962","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":"An online hyper-volume action bounding approach for accelerating the process of deep reinforcement learning from multiple controllers","authors":"Ali Aflakian,&nbsp;Alireza Rastegarpanah,&nbsp;Jamie Hathaway,&nbsp;Rustam Stolkin","doi":"10.1002/rob.22355","DOIUrl":"10.1002/rob.22355","url":null,"abstract":"<p>This paper fuses ideas from reinforcement learning (RL), Learning from Demonstration (LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control algorithms (experts) are used to constrain the action space of the agent, enabling faster RL refining of a control policy, by avoiding unnecessary explorative actions. Domain-specific knowledge of each expert is exploited. However, the resulting policy is robust against errors of individual experts, since it is refined by a RL reward function without copying any particular demonstration. Our method has the potential to supplement existing RLfD methods when multiple algorithmic approaches are available to function as experts, specifically in tasks involving continuous action spaces. We illustrate our method in the context of a visual servoing (VS) task, in which a 7-DoF robot arm is controlled to maintain a desired pose relative to a target object. We explore four methods for bounding the actions of the RL agent during training. These methods include using a hypercube and convex hull with modified loss functions, ignoring actions outside the convex hull, and projecting actions onto the convex hull. We compare the training progress of each method using expert demonstrators, employing one expert demonstrator with the DAgger algorithm, and without using any demonstrators. Our experiments show that using the convex hull with a modified loss function not only accelerates learning but also provides the most optimal solution compared with other approaches. Furthermore, we demonstrate faster VS error convergence while maintaining higher manipulability of the arm, compared with classical image-based VS, position-based VS, and hybrid-decoupled VS.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1814-1828"},"PeriodicalIF":4.2,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831758","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":"ASV station keeping under wind disturbances using neural network simulation error minimization model predictive control","authors":"Jalil Chavez-Galaviz,&nbsp;Jianwen Li,&nbsp;Ajinkya Chaudhary,&nbsp;Nina Mahmoudian","doi":"10.1002/rob.22346","DOIUrl":"10.1002/rob.22346","url":null,"abstract":"&lt;p&gt;Station keeping is an essential maneuver for autonomous surface vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a model predictive controller using neural network simulation error minimization (NNSEM–MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the robotics operating system and the multipurpose simulation environment Gazebo. A set of six tests was conducted by combining two varying wind speeds that are modeled as the Harris spectrum and three wind directions (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${0}^{^circ }$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;90&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${90}^{^circ }$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 \u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;180&lt;/mn&gt;\u0000 \u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${180}^{^circ }$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). The simulation results clearly show the advantage of the NNSEM–MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD-MPC), neural ordinary differential equation MPC (NODE-MPC), and knowledge-based NODE MPC. The proposed NNSEM–MPC approach performs better than the rest in five out of the six test conditions, and it is the second best in the remaining test case, reducing the mean position and heading error by at least &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;27.08&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 ","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1797-1813"},"PeriodicalIF":4.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806374","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":"Learning-based monocular visual-inertial odometry with \u0000 \u0000 \u0000 \u0000 S\u0000 \u0000 E\u0000 2\u0000 \u0000 \u0000 (\u0000 3\u0000 )\u0000 \u0000 \u0000 \u0000 $S{E}_{2}(3)$\u0000 -EKF","authors":"Chi Guo,&nbsp;Jianlang Hu,&nbsp;Yarong Luo","doi":"10.1002/rob.22349","DOIUrl":"10.1002/rob.22349","url":null,"abstract":"<p>Learning-based visual odometry (VO) becomes popular as it achieves a remarkable performance without manually crafted image processing and burdensome calibration. Meanwhile, the inertial navigation can provide a localization solution to assist VO when the VO produces poor state estimation under challenging visual conditions. Therefore, the combination of learning-based technique and classical state estimation method can further improve the performance of pose estimation. In this paper, we propose a learning-based visual-inertial odometry (VIO) algorithm, which consists of an end-to-end VO network and an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>S</mi>\u0000 \u0000 <msub>\u0000 <mi>E</mi>\u0000 \u0000 <mn>2</mn>\u0000 </msub>\u0000 \u0000 <mrow>\u0000 <mo>(</mo>\u0000 \u0000 <mn>3</mn>\u0000 \u0000 <mo>)</mo>\u0000 </mrow>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $S{E}_{2}(3)$</annotation>\u0000 </semantics></math>-Extended Kalman Filter (EKF). The VO network mainly combines a convolutional neural network with a recurrent neural network, taking advantage of two consecutive monocular images to produce relative pose estimation with associated uncertainties. The <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>S</mi>\u0000 \u0000 <msub>\u0000 <mi>E</mi>\u0000 \u0000 <mn>2</mn>\u0000 </msub>\u0000 \u0000 <mrow>\u0000 <mo>(</mo>\u0000 \u0000 <mn>3</mn>\u0000 \u0000 <mo>)</mo>\u0000 </mrow>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $S{E}_{2}(3)$</annotation>\u0000 </semantics></math>-EKF, which is proved to overcome the inconsistency issues of VIO, propagates inertial measurement unit kinematics-based states, and fuses relative measurements and uncertainties from the VO network in its update step. The extensive experimental results on the KITTI data set and the EuRoC data set demonstrate the superior performance of the proposed method compared to other related methods.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1780-1796"},"PeriodicalIF":4.2,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661451","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":"Sparus Docking Station: A current aware docking station system for a non-holonomic AUV","authors":"Joan Esteba,&nbsp;Patryk Cieślak,&nbsp;Narcís Palomeras,&nbsp;Pere Ridao","doi":"10.1002/rob.22310","DOIUrl":"10.1002/rob.22310","url":null,"abstract":"<p>This paper presents the design and development of a funnel-shaped Sparus Docking Station intended for the non-holonomic torpedo-shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with sensors and batteries, allowing for a stand-alone long-term deployment of the vehicle. An inverted Ultra Short Base-Line system is used to locate the Docking Station as well as to provide long-term drift-less vehicle navigation. The Sparus Docking Station is able to observe the ocean currents using a Doppler Velocity Log, being motorized to allow its self-alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports consecutive successful experimental results of the docking maneuver in sea trials in two different countries.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1765-1779"},"PeriodicalIF":4.2,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664104","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":"Puttybot: A sensorized robot for autonomous putty plastering","authors":"Zhao Liu,&nbsp;Dayuan Chen,&nbsp;Mahmoud A. Eldosoky,&nbsp;Zefeng Ye,&nbsp;Xin Jiang,&nbsp;Yunhui Liu,&nbsp;Shuzhi Sam Ge","doi":"10.1002/rob.22351","DOIUrl":"10.1002/rob.22351","url":null,"abstract":"<p>Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force-controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large-scale wall was processed without human involvement.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1744-1764"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670849","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":"Motion planning and contact force distribution for heavy-duty hexapod robots walking on unknown rugged terrains","authors":"Liang Ding,&nbsp;Xiao Gong,&nbsp;Lei Hu,&nbsp;Guanyu Wang,&nbsp;Zhongxi Shao,&nbsp;Huaiguang Yang,&nbsp;Haibo Gao,&nbsp;Zongquan Deng","doi":"10.1002/rob.22338","DOIUrl":"10.1002/rob.22338","url":null,"abstract":"<p>Heavy-duty hexapod robots have impressive stability, high load-bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming-based Contact Force Distribution (DQP-based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1680-1701"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670600","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":"Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation","authors":"Yuchun Wang,&nbsp;Cheng Gong,&nbsp;Jianwei Gong,&nbsp;Peng Jia","doi":"10.1002/rob.22345","DOIUrl":"10.1002/rob.22345","url":null,"abstract":"<p>Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multilayer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network-long short-term memory network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1702-1723"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670109","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}