查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news reporting out of Ann Arbor, Michigan, by NewsRx editors, research stated, "The introduction of assistive construction robots ca n significantly alleviate physical demands on construction workers while enhanci ng both the productivity and safety of construction projects. Leveraging a Build ing Information Model (BIM) offers a natural and promising approach to driving r obotic construction workflows." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of M ichigan, "However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construct ion work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes asbuilt information for th e ensuing construction and maintenance tasks. This research introduces an intera ctive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot's functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions , while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes in the robot's plan by changing the task sequenc e or workspace geometry or requesting a new motion plan to help the robot overco me the encountered uncertainties. A drywall installation case study is conducted to verify the proposed workflow. In addition, experiments are carried out to ev aluate the system performance using an industrial robotic arm in a research labo ratory setting that mimics a construction site and in the Gazebo simulation."
查看更多>>摘要：New research on Robotics is the subjec t of a report. According to news reporting out of Saltillo, Mexico, by NewsRx ed itors, research stated, "Designing robotic assistance strategies that prioritize users' effort and minimize robot intervention based on task or physiological pe rformance measures, without mandating precise tracking of a time-dependent traje ctory, poses a significant challenge. This article introduces a new assist-as-ne eded (AAN) robotic training strategy centered on an adaptive velocity field, whi ch guides users smoothly towards a desired path without imposing explicit time c onstraints." Financial supporters for this research include National Council of Humanities, S cience and Technology of Mexico under Grant CONAHCYT. Our news journalists obtained a quote from the research from Center for Research and Advanced Studies, "It promotes participation by reducing assistance based o n task performance and/or muscular effort. Unlike previous works, the low-level controller allows fine-tuning of the robot's accuracy in tracking the velocity f ield and gradually reduces the assistance as the free motion area around the des ired trajectory is approached. This approach facilitates seamless transitions in to and out of the free motion area, where a damping force is provided to ensure stable movements. An additional standout feature is the presence of a move-ahead strategy that avoids shortcuts. Two experiments were conducted to assess the ef fectiveness and advantages of the AAN strategy. Each experiment involved a diffe rent contour-following task with parameters, such as, the shortest distance from the desired path (Experiment 1) and muscular strength (Experiment 2) regulating the level of robotic assistance. In Experiment 1, the proposed strategy was com pared against both a conventional haptic-constraint-based approach and no roboti c assistance. Results indicate that the AAN robotic strategy enables faster task completion, smoother movements, reduced interaction forces, and diminished robo t intervention. Moreover, its real-time adaptability based on task performance a nd physiological data suggests potential benefits for motor learning programs."
查看更多>>摘要：Fresh data on robotics are presented i n a new report. According to news reporting from Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish drive n by LOS." Financial supporters for this research include Gdnrc; Natural Science Foundation of Guangdong Province. Our news editors obtained a quote from the research from South China University of Technology: "First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time conve rgence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsResearchers detail new data in Machine Learning -Computational Intelligence. According to news reporting out of Hefei, People's Republic of China, by NewsRx editors, research stated, "Dynamic community detect ion, which is capable of revealing changes in community structure over time, has garnered increasing attention in research. While evolutionary clustering method s have proven to be effective in tackling this issue, they often have a tendency to favor what are referred to as elite solutions, inadvertently neglecting the potential value of non-elite alternatives." Funders for this research include National Natural Science Foundation of China ( NSFC), Key Projects of University Excellent Talents Support Plan of Anhui Provin cial Department of Education, University Synergy Innovation Program of Anhui Pro vince.
查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting from Melbourne, Austr alia, by NewsRx journalists, research stated, "Geopolymer concrete emerges as a sustainable and durable alternative to conventional concrete, addressing its hig h carbon footprint and enhanced durability. The distinct properties of geopolyme r concrete, governed by supplementary cementitious materials and alkaline activa tors, promise reduced environmental impact and improved structural resilience." The news editors obtained a quote from the research from University of Melbourne : "However, its complex composition complicates the prediction of mechanical pro perties such as the elastic modulus, crucial for structural applications. This s tudy introduces an innovative approach using the eXtreme Gradient Boosting (XGBo ost) technique integrated with the multi-objective grey wolf optimizer to model the elastic modulus of geopolymer concrete. By dynamically selecting influential features and optimizing model accuracy, this methodology advances beyond tradit ional empirical models, which fail to capture the nonlinear interactions intrins ic to geopolymer concrete. Utilizing a comprehensive database gathered from exte nsive literature, 22 potential variables were examined that influence geopolymer concrete's elastic modulus. After mitigating multicollinearity and optimizing h yperparameters via Bayesian optimization, six XGBoost models were developed with different combinations of input variables, revealing compressive strength and t otal water content as pivotal predictors. The findings illustrate the models' pr ecision, with the trade-off between prediction accuracy and model simplicity vis ualized through the relationship between the number of input variables and predi ction error. The study culminates in a user-friendly graphical user interface th at enables easy prediction of geopolymer concrete's elastic modulus and fosters educational engagement."
查看更多>>摘要：New research on Surgery -Arthroplasty is the subject of a report. According to news originating from Detroit, United States, by NewsRx correspondents, research stated, "Robotic-assisted devices hel p provide precise component positioning in conversion of unicompartmental knee a rthroplasty (UKA) to total knee arthroplasty (TKA). A few studies offer surgical techniques for CT-based roboticassisted conversion of UKA to TKA, however no s tudies to date detail this procedure utilizing a non-CT based robotic assisted d evice." Our news journalists obtained a quote from the research from the Wayne State Uni versity School of Medicine, "This paper introduces a novel technique employing a non-CT based robotic assisted device (ROSA? Knee System, Zimmer Biomet, Warsaw, IN) for converting UKA to TKA with a focus on its efficacy in gap balancing. We present three patients (ages 46 to 66) who were evaluated for conversion of UKA to TKA for aseptic loosening, stress fracture, and progressive osteoarthritis. Each patient underwent roboticassisted conversion to TKA. Postoperative assessm ents at 6 months revealed improved pain, function, and radiographic stability. P reoperative planning included biplanar long leg radiographs to determine the ana tomic and mechanical axis of the leg. After arthrotomy with a standard medial pa rapatellar approach, infrared reflectors were pinned into the femur and tibia, f ollowed by topographical mapping of the knee with the UKA in-situ. The intraoper ative software was utilized to evaluate flexion and extension balancing and plan bony resections. Then, the robotic arm guided placement of the femoral and tibi al guide pins and the UKA components were removed. After bony resection of the d istal femur and proximal tibia, the intraoperative software was used to reassess the extension gap, and plan posterior condylar resection to have the flexion ga p match the extension gap."
查看更多>>摘要：Investigators publish new report on Ar tificial Intelligence. According to news reporting from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "River ecosystem health ass essment (REHA), an effective approach for identifying river ecosystem health, is crucial for achieving sustainable river management and ensuring water security. However, existing REHA methods still fail to consider the cumulated influences of uncertain inputs, stochastic environment and limited rationality of decision makers on REHA." Financial supporters for this research include Major Key Technology Research on Water Resources in China, Health Evaluation and Protection Strategy of Water Bod ies in the Middle and Lower Reaches of Yangtze River, National Natural Science F oundation of China (NSFC), Lalo Water Conservancy Center and Supporting Irrigati on District Project in Tibet: Monitoring and Evaluation of Fish Passage Efficien cy.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsNew research on Robotics is the subject of a repo rt. According to news reporting originating from Chengdu, People's Republic of C hina, by NewsRx correspondents, research stated, "In this paper a novel discrete multi-objective cross-entropy optimization (CrMOCEO) algorithm is proposed to s olve the path planning problem of dual-robot cooperative arc welding. We strive to find a low-cost, fast and more efficient solution for robotic welding of larg e complex components." Financial support for this research came from Sichuan Province Science and Techn ology Support Program. Our news editors obtained a quote from the research from Southwest Jiaotong Univ ersity, "Firstly, an optimization model of dualrobot welding path planning is es tablished by considering various variables and constraints in the actual welding process. Then, three strategies are introduced to improve the multiobjective c ross-entropy optimization (MOCEO) algorithm to better solve the discrete path pl anning problem. Finally, in order to verify feasibility and effectiveness of the proposed algorithm, it is used to solve the 2-, 3-, 5-and 7-objective WFG2-9 p roblems and plan some typical welding seams of a large complex component, the MO CEO, NSGA-II, MOPSO and MOGWO are used for comparison. The simulation demonstrat es that the CrMOCEO can obtain better solutions for multiple objectives than the other four algorithms, and the path solved by the CrMOCEO is tested in the Gaze bo physical model and workshop site, the results further verified the effectiven ess of the CrMOCEO algorithm."
查看更多>>摘要：Fresh data on Robotics are presented i n a new report. According to news reporting from Trento, Italy, by NewsRx journa lists, research stated, "Autonomous robots and their applications are becoming p opular in several different fields, including tasks where robots closely interac t with humans. Therefore, the reliability of computation must be paramount." Financial supporters for this research include Italian Ministry for University a nd Research (MUR) through the "Departments of Excellence2023-2027" Program, Euro pean Union (EU), SMART-ER Project through European Union, Coordenacao de Aperfei coamento de Pessoal de Nivel Superior (CAPES). The news correspondents obtained a quote from the research from the University o f Trento, "In this work, we measure the reliability of Google's Coral Edge tenso r processing unit (TPU) executing three deep reinforcement learning (DRL) models through an accelerated neutrons beam. We experimentally collect data that, when scaled to the natural neutron flux, account for more than 5 million years. Base d on our extensive evaluation, we quantify and qualify the radiation-induced cor ruption on the correctness of DRL. Crucially, our data show that the Edge TPU ex ecuting DRL has an error rate that is up to 18 times higher the limit imposed by international reliability standards. We found that despite the feedback and int rinsic redundancy of DRL, the propagation of the fault induces the model to fail in the vast majority of cases or the model manages to finish but reports wrong metrics (i.e., speed, final position, and reward)."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsInvestigators publish new report on Machine Learn ing. According to news reporting from Changsha, People's Republic of China, by N ewsRx journalists, research stated, "Cobalt (Co) has been recognized as one of t he most hazardous elements by the United Nations Environmental Program; however, it has received limited attention in previous studies of identifying heavy meta l contamination and has been limited to small, site-scale datasets and few machi ne learning algorithms. To fill this research gap, eight machine learning algori thms were combined with visible and near-infrared reflectance spectroscopy in th is study to develop a large-scale model for classifying Co content in soil." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province, Young Elite Scientists Sponsorship Progra m by CAST, Unveiling and Commanding Project from Fankou Lead-Zinc Mine, High-Per formance Computing Center of Central South University.
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