查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating in Constantine, Algeria, by NewsRx journalists, research stated, “This paper presents an innovative appr oach to control upper-limb rehabilitation robots for both passive and active-ass istive rehabilitation therapy. In contrast to conventional model-based impedance control strategies, which may compromise controller stability and robustness du e to model uncertainties, unmodeled dynamics, and external disturbances, our pro posed model-free impedance control (MFIC) strategy eliminates the requirement fo r prior knowledge about the controlled system dynamics.” The news reporters obtained a quote from the research from the University Mentou ri of Constantine, “MFIC is achieved by incorporating model-free control into co nventional impedance control, employing time delay estimation (TDE) to estimate unknown dynamics. Numerical simulations confirm that MFIC outperforms traditiona l impedance control in terms of tracking performance and robustness. Furthermore , model-free variable impedance control (MFVIC) is introduced by enhancing MFIC with online impedance parameters adaptation using fuzzy logic control. The desir ed impedance model adapts according to motion and contact torque measurements. M FVIC employs two fuzzy systems to adjust the desired impedance model for two sta ges of rehabilitation: passive and active-assistive rehabilitation training.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on robotics have bee n published. According to news reporting from the University of Washington by Ne wsRx journalists, research stated, “During manufacturing processes, such as clam ping and drilling of elastic structures, it is essential to maintain tool-workpi ece normality to minimize shear forces and torques, and thereby preventing damag e to the tool or the workpiece.” Our news reporters obtained a quote from the research from University of Washing ton: “The challenge arises in making precise model-based predictions of the rela tively large deformations that occur as the applied normal force (e.g., clamping force) is increased. However, precision deformation predictions are essential f or selecting the optimal robot pose that maintains force normality. Therefore, r ecent works have employed force-displacement measurements at each work location to determine the robot pose for maintaining tool normality. Nevertheless, this a pproach, which relies on local measurements at each work location and at each gr adual increment of the applied normal force, can be slow and consequently, time prohibitive. The main contributions of this work are to use: Gaussian Process me thods to learn the robot-pose map for force normality at unmeasured workpiece lo cations; active learning to optimally select and minimize the number of measurem ent locations needed for accurate learning of the robot-pose map.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on robotics is now availab le. According to news originating from Miyagi, Japan, by NewsRx editors, the res earch stated, “Tunnels and long corridors are challenging environments for LiDAR -based odometry estimation algorithms because a LiDAR point cloud should degener ate (i.e., point cloud matching cannot work properly) in such environments. To t ackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU- wheel odometry algorithm incorporating online calibration of a kinematic model f or skid-steering robots.” Financial supporters for this research include Japan Society For The Promotion o f Science (Jsps) Kakenhi; Project Commissioned; New Energy And Industrial Techno logy Development Organization.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on robotics have been published. According to news reporting originating from the University Car los III of Madrid by NewsRx correspondents, research stated, “This paper introdu ces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++.” The news journalists obtained a quote from the research from University Carlos I II of Madrid: “The solution enhances accessibility by addressing the needs of in dividuals with injuries or disabilities who may require help with common manipul ation tasks. The aim of this paper is to propose a method involving vision, mani pulation, and learning techniques to effectively address the task of bottle open ing. The process begins with the acquisition of bottle and cap positions using a n RGB-D camera and computer vision. Subsequently, the robot picks the bottle wit h one gripper and grips the cap with the other, each by planning safe trajectori es. Then, the opening procedure is executed via a position and force control sch eme that ensures both grippers follow the unscrewing path defined by the cap thr ead. Within the control loop, force sensor information is employed to control th e vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal ang le increments for rotation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Changchun, Peo ple’s Republic of China, by NewsRx journalists, research stated, “The applicatio n of the simulation-optimization method for groundwater contamination source ide ntification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identificat ion results due to the complexity, nonlinearity, and ill-posed nature of the inv erse problem. To address these issues, we have innovatively developed an inversi on framework based on ensemble learning strategies.” The news reporters obtained a quote from the research from Jilin University, “Th is framework comprises a stacking ensemble model (SEM), which integrates three d istinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Sh ark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared t o the original simulation model, it significantly reduces time cost while mainta ining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimi zation model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework thro ugh two hypothetical scenarios derived from an actual coal gangue pile. The resu lts are as follows. (1) The SEM exhibits improved fitting performance compared t o single machine learning models when dealing with high-dimensional nonlinear da ta from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the id entification results of GCSI than individual optimizers.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting originating in Chengdu, Peopl e’s Republic of China, by NewsRx journalists, research stated, “PurposeThis pape r aims to explore the effectiveness of customer inoculation strategies in the co ntext of AI service failures in the hospitality and tourism industries. Furtherm ore, Funders for this research include China Postdoctoral Science Foundation, Nationa l Natural Science Foundation of China (NSFC), Sichuan Philosophy and Social Scie nce Planning Project, Western Rural Revitalization Research Center.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Naples, Italy, by NewsRx correspondents, research stated, “Hematologic malign ancies are a group of heterogeneous neoplasms which originate from hematopoietic cells.” The news correspondents obtained a quote from the research from CTO Hospital: “T he most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. We performed a narr ative review about recent applications of ML in the most common hematological ma lignancies. We focused on the most recent scientific literature about this topic . ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Chihuahua, Mexico, by NewsRx editors, research stated, “Form deviation generated during the milling profile process challenges the precision and functionality of industria l fixtures and product manufacturing across various sectors. Inspecting contour profile quality relies on commonly employed contact methods for measuring form d eviation.” Financial supporters for this research include Autonomus University of Juarez Ci ty An The National Council For Humanities, Sciences, And Technologies.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news reporting originating from Wichita, United States, by NewsRx correspondents, research stated, “Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on androids have been published. According to news originating from Nanjing, People’s Republic o f China, by NewsRx editors, the research stated, “Interaction errors are hard to avoid in the process of human-robot interaction (HRI). User emotions toward int eraction errors could further affect the user’s attitudes to robots and experien ces of HRI and so on.” Financial supporters for this research include National Natural Science Foundati on of China.
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