Robotics and Computer-integrated Manufacturing最新文献

{"title":"Digital twin driven opti-state control approach for smart warehousing in the synchronous operating environment","authors":"Yongheng Zhang ,&nbsp;Ting Qu ,&nbsp;Zhicong Hong ,&nbsp;Zhongfei Zhang ,&nbsp;George Q. Huang","doi":"10.1016/j.rcim.2025.103099","DOIUrl":"10.1016/j.rcim.2025.103099","url":null,"abstract":"<div><div>Warehouse operations are increasingly subject to internal variability and external disruptions, resulting in unstable task execution, elevated logistics costs, and inefficiencies in inventory management. A major challenge lies in enabling the warehouse system to maintain optimal performance under such dynamic disturbances. However, existing systems often suffer from low levels of data interoperability, asynchronous operations among resources, and the inability to determine and maintain real-time opti-state. To tackle these limitations, this paper proposes a Digital Twin-driven Warehouse Opti-state Control System (DT-WOsCS), which establishes a synchronous operating environment that enables full-dimensional perception and coordination among Smart Warehousing Objects (SWOs). The framework integrates a multi-scale state sensing with an opti-state control strategy to monitor disturbances and adaptively reconfigure operational plans in real time. A Storage Location Assignment Problem with Mixed-Stacking Areas (SLAP-MSA) is developed under this paradigm, capturing both spatial dynamics and operational constraints. To solve the resulting complex scheduling problem, a customized genetic algorithm is introduced, featuring dual-priority chromosome encoding and adaptive perturbation to support efficient opti-state search. A case study in a paint manufacturing warehouse is conducted to evaluate the proposed method. Experimental results show that DT-WOsCS significantly improves warehouse space utilization, reduces operational and transfer costs, and enhances system resilience and stability in the presence of dynamic disturbances.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103099"},"PeriodicalIF":11.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Surface segmentation and weld extraction on noisy point clouds consisting of multiple quadrics","authors":"Bo Yang ,&nbsp;Zhengtuo Wang ,&nbsp;Yuetong Xu ,&nbsp;Songyu Hu ,&nbsp;Guanhua Xu ,&nbsp;Jianzhong Fu","doi":"10.1016/j.rcim.2025.103100","DOIUrl":"10.1016/j.rcim.2025.103100","url":null,"abstract":"<div><div>Quadrics are the most common types of surfaces used in weldments. Extracting multiple welds formed by quadric surfaces from a single 3D point cloud is an essential and challenging step in robotic welding for complex weldments. Relevant studies mostly focus on weld extraction from weldments with a single type of quadric. A weld extraction method for weldments with general quadratic surfaces is required. (1) This paper proposes a quadric fitting method for all kinds of quadrics, efficiently solving the quadric models with linear equations. It can be observed from the test results that the fitting error of the method proposed in this paper grows at a rate of about 1/5 of that of the SVD method in the literature as the point cloud noise grows; and the method proposed in this paper improves the operating efficiency by about 40 %. (2) For noisy point clouds with multiple intersecting quadrics, a quadric segmentation method based on region growing is proposed. The proposed segmentation method reduces 30 % ∼ 50 % of segmentation errors during the tests compared to the ICP registration approach in the literature. (3) A region growing method based on ETVPS (End Tangent Vector Projection Sorting) for weld extraction with the unorganized raw intersection points from the segmented quadrics is proposed. All the mentioned methods are verified with solid experiments with physical weldments. The proposed weld extraction method proves to be robust to noisy and defective point clouds.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103100"},"PeriodicalIF":11.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Task-aware motion planning in constrained environments using GMM-informed RRT planners","authors":"Abdelaziz Shaarawy ,&nbsp;Alireza Rastegarpanah ,&nbsp;Rustam Stolkin","doi":"10.1016/j.rcim.2025.103095","DOIUrl":"10.1016/j.rcim.2025.103095","url":null,"abstract":"<div><div>This paper introduces a novel integration of Task-Parameterized Gaussian Mixture Models (TP-GMM) with sampling-based motion planners, specifically RRT, to improve planning efficiency and path optimality in constrained robotic manipulation tasks. The proposed GMM-RRT and GMR-RRT planners exploit a TP-GMM trained offline on human demonstrations to generate task-adaptive sampling distributions, effectively guiding the search toward feasible and high-quality solutions. The framework is implemented in the MoveIt motion planning framework and evaluated across five simulation experiments and 30 real-world trials, focusing on Electric Vehicle (EV) battery disassembly tasks. Compared to baseline sampling-based planners, the GMM-informed planners demonstrate superior performance in key planning metrics. In the path length aspect, GMM planners yield significantly shorter trajectories, averaging 0.8 meters versus over 2 meters for baseline planners. Similarly, in path simplification time, the near-optimal nature of the generated paths reduces post-processing efforts. While planning time is higher due to TP-GMM inference and projection stages, over 90% of that time is spent outside the RRT search itself, which completes quickly due to guided sampling. Path duration also remains competitive, with GMM-informed planners closely matching RRT*. These results highlight the effectiveness of task-conditioned sampling in unstructured manipulation scenarios. The proposed method maintains 100% success rate while improving efficiency, suggesting strong potential for integration in sequential and adaptive robotic systems. Future work will focus on extending generalization to broader task parameter spaces and addressing inverse kinematics challenges.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103095"},"PeriodicalIF":11.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Batch EOL products human-robot collaborative remanufacturing process planning and scheduling for industry 5.0","authors":"Yangkun Liu ,&nbsp;Guangdong Tian ,&nbsp;Haowen Sheng ,&nbsp;Xuesong Zhang ,&nbsp;Gang Yuan ,&nbsp;Chaoyong Zhang","doi":"10.1016/j.rcim.2025.103098","DOIUrl":"10.1016/j.rcim.2025.103098","url":null,"abstract":"<div><div>With the advancement of technology, the growing volume of end-of-life (EOL) products in manufacturing poses significant challenges to achieving sustainability and green transformation. Proper treatment of EOL components is a critical step toward green and sustainable manufacturing development. Remanufacturing, capable of transforming EOL products into items with performance comparable to or surpassing new products, has become a vital method for achieving sustainable manufacturing. Recently, Industry 5.0 has introduced a \"human-centric\" philosophy, elevating workers from passive participants to indispensable elements in enterprises. As a critical bridge between EOL products and remanufactured goods, integrating this human-centric philosophy into remanufacturing processing planning is of profound significance. Human-robot collaborative processing modes serve as a key means to realize this philosophy; However, research on human-robot collaborative strategies in remanufacturing processing remains underexplored. To address this gap, this study proposes a batch-oriented EOL human-robot collaborative remanufacturing process planning and job-shop scheduling (BHRCRPS) model based on two-layer coding theory. The upper-level model selects human-robot collaboration modes aligned with the human-centric philosophy by minimizing worker fatigue and maximizing robot task completion. The lower-level model, constrained by the upper-level results, establishes a multi-objective model incorporating time, cost, and energy consumption for remanufacturing process planning and shop floor scheduling, aiming to enhance efficiency, profitability, and energy savings. To efficiently solve the BHRCRPS model, a hybrid algorithm integrating the Grey Wolf Optimizer (GWO) and Rat Swarm Optimizer (RSO) is developed. Finally, the applicability of the model is validated through a case study on worn worm gear remanufacturing, and the superiority of the proposed GWO-RSO algorithm is demonstrated by comparisons with state-of-the-art algorithms.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103098"},"PeriodicalIF":11.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"LLM-driven symbolic planning and hierarchical imitation learning for long-horizon deformable object assembly","authors":"Jiaming Qi ,&nbsp;Liang Lu ,&nbsp;Fangyuan Wang ,&nbsp;Hoi-Yin Lee ,&nbsp;David Navarro-Alarcon ,&nbsp;Zeqing Zhang ,&nbsp;Peng Zhou","doi":"10.1016/j.rcim.2025.103096","DOIUrl":"10.1016/j.rcim.2025.103096","url":null,"abstract":"<div><div>Long-horizon assembly tasks involving deformable objects pose substantial challenges for autonomous robots, stemming from infinite-dimensional state spaces, complex sequential dependencies, and high variability in real-world conditions. In this work, we propose a novel and robust framework that tightly integrates Large Language Model (LLM)-driven symbolic planning with hierarchical imitation learning to enable reliable and generalizable solutions for deformable object assembly. Our approach leverages the advanced reasoning capabilities of LLMs to translate natural language task instructions into structured symbolic task plans. This decomposition is initiated by a visual-language model (VLM) that generates descriptive subgoals from key visual frames of a human demonstration. Each subgoal is then automatically grounded in the robot’s perception via a VLM query mechanism, ensuring precise and task-relevant state estimation. For execution, a 3D diffusion policy (DP3) conditioned on visual input and symbolic subgoals generates smooth, low-level action trajectories, bridging the gap between high-level symbolic reasoning and dexterous manipulation. We validate our hierarchical framework on a real-world round belt drive assembly benchmark, demonstrating significant improvements in success rates, error recovery, and generalization across diverse and perturbed initial conditions, compared to existing approaches. Our results highlight the potential of integrating LLM-based symbolic abstraction, targeted state querying, and diffusion-based visuomotor control for robust, autonomous assembly of deformable objects in unstructured environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103096"},"PeriodicalIF":9.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Enhancing remanufacturing efficiency: a genetic teaching-learning-based optimisation algorithm for human-robot shared-workstation disassembly line balancing problem","authors":"Qiyao Duan ,&nbsp;Zeqiang Zhang ,&nbsp;Junyi Hu ,&nbsp;Lei Guo ,&nbsp;Wei Liang","doi":"10.1016/j.rcim.2025.103094","DOIUrl":"10.1016/j.rcim.2025.103094","url":null,"abstract":"<div><div>Human-robot collaborative technology leverages the complementary capabilities of both agents, offering diversified operational scenarios for the remanufacturing industry. In this study, the human-robot shared-workstation disassembly line balancing problem (HRSW-DLBP) is addressed, where humans and robots operate concurrently. The HRSW-DLBP facilitates the rapid release of precedence constraints on components while processing both hazardous and complex components. Recognising the prevalence of sequence-dependent setup times (SDSTs) in practical applications, this study extends the HRSW-DLBP with SDST to more accurately model real-world scenarios. The HRSW-DLBP-SDST presents a more complex challenge than its predecessors, which do not consider such setup times. Therefore, devising an effective method for solving this problem is crucial. Given the NP-hard nature of the HRSW-DLBP-SDST, this study introduces a genetic teaching-learning-based optimisation (GTLBO) algorithm tailored for large-scale problem solving, incorporating a double-layer encoding and decoding strategy informed by the characteristics of the problem and enhancing local search operator to better align with the GTLBO structure. The performance of the proposed GTLBO algorithm was benchmarked against established optimisation algorithms across four cases, demonstrating its superiority. Finally, the HRSW-DLBP-SDST was applied to a liquid crystal display TV disassembly scenario, yielding multiple optimal allocation schemes. These case studies confirm the efficacy of the proposed method in resolving the HRSW-DLBP-SDST.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103094"},"PeriodicalIF":9.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 survey on learning an autonomous dynamic system for human–robot skills transfer from demonstration","authors":"Jiayun Fu ,&nbsp;Haotian Huang ,&nbsp;Zhehao Jin ,&nbsp;Andong Liu ,&nbsp;Wen-An Zhang ,&nbsp;Li Yu ,&nbsp;Weiyong Si ,&nbsp;Chenguang Yang","doi":"10.1016/j.rcim.2025.103092","DOIUrl":"10.1016/j.rcim.2025.103092","url":null,"abstract":"<div><div>Autonomous dynamic systems (ADS) have become a key area of research in the field of robotics, aiming to enable robots to acquire human-like operational skills and perform complex tasks in dynamic environments without external intervention. Despite significant progress, current technologies have yet to enable robots to fully achieve autonomous skill transfer in real-world applications. The prevailing approach to bridge this gap is Learning from Demonstration (LfD), where robots learn by observing and imitating expert demonstrations. Dynamic systems-based methods, particularly those utilizing Lyapunov stability theory, have shown great potential in effectively encoding human motor skills, ensuring the stability, accuracy, and generalization of learned behaviors during the learning process. This survey provides an overview of the recent advancements in dynamic systems for skill transfer, focusing on methods that enable robots to replicate human actions, as demonstrated by experts. We present a classification of existing dynamic systems approaches, highlight landmark studies, and discuss their key features, advantages, and limitations. This paper also explores the applications of these methods and identifies major challenges that remain in both theoretical and practical aspects of robot skill learning.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103092"},"PeriodicalIF":9.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Sustainable scheduling for job shops with joint maintenance, machine speed scaling, and uncertain processing times","authors":"M.N. Darghouth ,&nbsp;A.M. Attia","doi":"10.1016/j.rcim.2025.103091","DOIUrl":"10.1016/j.rcim.2025.103091","url":null,"abstract":"<div><div>This paper addresses the job shop scheduling problem by integrating preventive and corrective maintenance, uncertain processing times, and machine speed scaling under an environmental constraint. A comprehensive mixed-integer nonlinear programming (MINLP) model is formulated to optimize job scheduling, considering machine reliability, energy consumption, and carbon emissions. The model captures uncertainty in processing times, influenced by variations in machine speed, and integrates machine degradation using a Weibull distribution. A global carbon footprint constraint ensures compliance with environmental targets. This study examines the trade-offs between minimizing makespan, scheduling maintenance, and achieving sustainability objectives. Given the NP-hard nature of the job shop scheduling problem, a two-fold approach is applied for efficient solving. First, the Relax-and-Fix heuristic generates a high-quality initial solution by iteratively relaxing and fixing subsets of variables, thereby significantly accelerating the optimization process. The second step employs a branch-and-bound algorithm, which systematically explores the solution space by solving relaxed subproblems and refining integer variables. Numerical experiments validate the model, offering insights into balancing maintenance strategies, speed scaling, and energy efficiency in complex manufacturing environments. For a maximum permissible carbon footprint of 550 kg CO2, the model achieves a makespan of 18.98 h with a total carbon footprint of 514.56 kg CO2 when processing time variability is set at 5%. Results show that preventive maintenance (PM) reduces repair times, mitigates the impact of failures on makespan, and enhances machine speed adjustments, thereby optimizing energy consumption to meet environmental constraints. Sensitivity analysis reveals that higher failure rates significantly increase repair times and overall makespan. The model dynamically adjusts machine speeds to balance environmental targets with operational reliability under varying conditions. This study also derives key managerial insights for a sustainable scheduling model for job shop operating environments, emphasizing the integration of maintenance, machine speed adjustments, and the effective handling of processing time variability.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103091"},"PeriodicalIF":9.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Improved deep Lagragian network-enabled momentum observer for collision detection during human-robot collaboration","authors":"Yudie Hu ,&nbsp;Weidong Li ,&nbsp;Yong Zhou ,&nbsp;Duc Truong Pham","doi":"10.1016/j.rcim.2025.103093","DOIUrl":"10.1016/j.rcim.2025.103093","url":null,"abstract":"<div><div>During human-robot collaboration (HRC), robots share workplaces with humans, and there may be frequent contact between them. It is crucial to be able to detect unexpected collisions in real-time so that appropriate safety measures should be taken to avoid injuries to humans and damage to robots. However, there are challenges with existing collision detection strategies, such as the additional costs incurred in deploying sensors in robots to implement pre-collision safety surveillance solutions or conducting complicated experiments to develop post-collision compliance solutions. To address these challenges, this paper presents a new momentum observer-based collision detection approach in which the external torques caused by collisions on robots can be efficiently identified. The approach involves integrating an improved deep Lagrangian network (DeLaN) to model robot dynamics without dynamic parameter identification experiments and prior knowledge of the robot’s physical and structural parameters. Another innovation of this approach is that a compensatory safety threshold is designed to enhance collision detection accuracy. Three robot datasets were used to train the improved DeLaN model. Simulation and real-world experiments were further carried out on the proposed approach to validate the effectiveness of the approach. Comparative experiments showed that the proposed approach outperformed other momentum observers in terms of both speed and efficiency. Moreover, experiments showed that the compensatory safety threshold proposed in this approach mitigated false positives caused by friction errors in robot joints to prevent the misdetection of collisions.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103093"},"PeriodicalIF":9.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Vision-based extraction of industrial information from legacy Programmable Logic Controllers","authors":"Zhengyang Ling,&nbsp;Sam Brooks,&nbsp;Duncan McFarlane,&nbsp;Alan Thorne,&nbsp;Gregory Hawkridge","doi":"10.1016/j.rcim.2025.103088","DOIUrl":"10.1016/j.rcim.2025.103088","url":null,"abstract":"<div><div>Technological advancements in manufacturing are increasingly driven by connectivity and information that can be collected about manufacturing processes. Programmable Logic Controllers (PLCs) are a valuable source of process information which can help inform operations. However, many factories use legacy PLCs with restricted connection and data extraction capabilities. This paper presents a novel vision-based PLC monitoring method for extracting the input and output (I/O) states of a PLC in real time. Four case studies in industry and laboratory settings are presented; in each case study, vision-based PLC monitoring was used to extract I/O data successfully and provide data for applications such as operation monitoring, process monitoring, production counting and fault detection. Vision-based monitoring is evaluated and compared to other PLC monitoring methods using a set of key requirements. The vision-based monitoring method showed several improvements over existing PLC data extraction methods; these include no PLC control system interference, minimal disruption during installation, system security, and cost-effective design. This new vision-based PLC monitoring method has the potential to provide manufacturers with a method to retrofit PLCs to access new valuable sources of information that can be used to improve their operation or create a smart factory at a lower cost.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103088"},"PeriodicalIF":9.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}