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    Research from Carinthia University of Applied Sciences Has Provided New Data on Machine Learning (Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations In Human-Robot Collaboration)

    39-39页
    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 artificial intell igence have been published. According to news reporting originating from Villach , Austria, by NewsRx correspondents, research stated, "Following the performance and force limitation method of the ISO/TS 15066 standard, safety of a human-rob ot collaboration task is assessed for critical situations assuming quasi-static impact." The news correspondents obtained a quote from the research from Carinthia Univer sity of Applied Sciences: "To this end, impact forces and pressures are experime ntally measured and compared with limit values specified by ISO/TS 15066. Conseq uently, such a safety assessment must be repeated whenever something changes in the collaborative workspace or the task, which severely limits the flexibility o f collaborative systems. To overcome this problem, in this paper a physics guide d machine learning (ML) method for prediction of peak impact forces, within pred efined modification dimensions of collaborative applications, is proposed. Along with a pose-dependent linearized model, an ensemble of boosted decision tree (B DT) in combination with a feed-forward neural network (NN) is trained with peak impact forces measured at a UR10e robot covering the range of interest."
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    Kansas State University Researcher Advances Knowledge in Machine Learning (An Ex ploratory Study of Body Measurement Prediction Using Machine Learning and 3D Bod y Scans)

    39-40页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of Manhattan, Kansas, by NewsR x editors, research stated, “Obtaining accurate body measurements is a critical step when designing products to fit the human body.”Funders for this research include Kansas State University; Human Solutions of No rth America Inc..Our news correspondents obtained a quote from the research from Kansas State Uni versity: “Compared to traditional manual methods, 3D body scanning has fundament ally enhanced the accessibility of the body, however, the datasets extracted fro m 3D body scans often have missing values. Recently, the applications of data-dr iven machine learning (ML) methods in anthropometrics studies and clothing-relat ed work have been increasing.”
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    Study Results from Federal University Pernambuco Update Understanding of Machine Learning (Quantum Machine Learning for Drowsiness Detection With Eeg Signals)

    40-41页
    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 Ma chine Learning. According to news reporting originating in Recife, Brazil, by Ne wsRx journalists, research stated, "Human reliability is an increasingly importa nt area in various fields for accident prevention. Monitoring human biological p arameters, such as metabolic agents, through techniques like an electroencephalo gram (EEG), data analysis can help detect patterns indicating drowsiness, a majo r cause of fatigue that may impact tasks in various industries, including oil an d gas, aviation, naval, railway, and others that involve shift work." Financial supporters for this research include Fundacao de Amparo a Ciencia e Te cnologia do Estado de Pernambuco (FACEPE), Fundacao de Amparo a Pesquisa do Esta do de Alagoas (FAPEAL), Conselho Nacional de Desenvolvimento Cientifico e Tecnol ogico (CNPQ), National Institute for Science and Technology on Quantum Informati on (INCT-IQ), Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES ), Agencia Nacional de Petroleo, Gas Natural e Biocombustiveis (ANP), Serrapilhe ira Institute, Simons Foundation, Brazilian Ministry of Science, Technology, and Innovation (MCTIC), National Health & Medical Research Council (N HMRC) of Australia, Financiadora de Inovacao e Pesquisa (Finep).
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    Researchers from Indian Institute for Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Unlocking the Efficiency of Nonaqu eous Li-air Batteries Through the Synergistic Effect of Dual Metal Site Catalyst s: ...)

    41-42页
    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 Machine Learn ing have been published. According to news reporting originating in Madhya Prade sh, India, by NewsRx journalists, research stated, "The recent growing attention towards non-aqueous Li-air batteries (LABs) stems from their high energy densit y, positioning them as a key solution to the surging demand for electrical energ y driven by portable electronics. Despite the potential of LABs, sluggish cathod e kinetics and large overpotentials, coupled with storage challenges from insolu ble discharge products, impede their commercialization." Financial supporters for this research include Science and Engineering Research Board, IIT Indore,Science Engineering Research Board (SERB), India, Council of Scientific & Industrial Research (CSIR)-India, University Grants Commission, India, Ministry of Education.
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    Reports on Robotics from South China University of Technology Provide New Insigh ts (Friction and Deformation Behavior of Human Skin During Robotic Sliding Massa ge Operation)

    42-43页
    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 Ro botics. According to news reporting out of Guangzhou, People's Republic of China , by NewsRx editors, research stated, "This study investigates the friction and deformation behavior of the skin in contact with a rigid massage ball and its in fluencing factors. Pressing and stretching experiments were conducted using a co llaborative robot experimental platform." Funders for this research include Basic and Applied Basic Research Foundation of Guangdong Province, Guangdong Basic and Applied Basic Research Foundation.
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    Recent Findings from Russian Academy of Sciences Provides New Insights into Mach ine Learning (Mobile Network Traffic Analysis Based On Probability-informed Mach ine Learning Approach)

    43-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting out of Moscow, Russia, by NewsRx e ditors, research stated, "The paper proposes an approach to the joint use of sta tistical and machine learning (ML) models to solve the problems of the precise r econstruction of historical events, real-time detection of ongoing incidents, an d the prediction of future quality of service -related occurrences for prospecti ve development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced." Financial support for this research came from RUDN University Scientific Project s Grant System. Our news journalists obtained a quote from the research from the Russian Academy of Sciences, "First, the preliminary clustering based on the finite normal mixt ures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability -informed ML approach in the field of telecommunications networks. Using the real -world datasets from a Portuguese mobile operator as well as public cellular traffic data, the artic le compares this approach with methods such as random forests, support vector ma chine regression, gradient boosting and LSTM. Vector autoregression,informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchm ark. We demonstrate that DGMM-based regression is 6.82-22.8 times faster than LS TM for the dataset. Moreover, DGMM-based regression can achieve better results f or the most important traffic characteristics (average and total traffic, the nu mber of users). For metrics MAPE and RMSE, it surpasses the results of statistic al methods up to 46.7% (RMSE) and 91.5% (MAPE) (medi an increases are 28.0% and 80.1%, respectively), as w ell as for ML methods up to 13.0% (RMSE) and 35.7% ( MAPE) (median increases are 0.39% and 2.5%, respectiv ely). Thus, the use of a probability -informed approach for telecommunication da ta seems optimal for the computational speed and accuracy trade-off."
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    Institute of Photogrammetry and Remote Sensing Researchers Describe New Findings in Robotics (Novel View Synthesis with Neural Radiance Fields for Industrial Ro bot Applications)

    44-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in robotic s. According to news reporting out of the Institute of Photogrammetry and Remote Sensing by NewsRx editors, research stated, "Neural Radiance Fields (NeRFs) hav e become a rapidly growing research field with the potential to revolutionize ty pical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as we ll as the interior orientation." The news correspondents obtained a quote from the research from Institute of Pho togrammetry and Remote Sensing: "In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, the accurac y of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongl y depend on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. To start with, we propose an alternative to SfM pre-processing: we capture the input imag es with a calibrated camera that is attached to the end effector of an industria l robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing the m to ground truth, and by computing an internal quality measure based on ensembl e methods. For evaluation purposes, we acquire multiple datasets that pose chall enges for reconstruction typical of industrial applications, like reflective obj ects, poor texture, and fine structures. We show that the robot-based pose deter mination reaches similar accuracy as SfM in non-demanding cases, while having cl ear advantages in more challenging scenarios."
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    New Findings from Imperial College London in Androids Provides New Insights (A F ramework for Trust-related Knowledge Transfer In Human-robot Interaction)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics-Androids h ave been presented. According to news reporting out of London, United Kingdom, b y NewsRx editors, research stated, "Trustworthy humanrobot interaction (HRI) du ring activities of daily living (ADL) presents an interesting and challenging do main for assistive robots, particularly since methods for estimating the trust l evel of a human participant towards the assistive robot are still in their infan cy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot's functionality, the task and the environmental state." Financial support for this research came from UK Research & Innova tion (UKRI). Our news journalists obtained a quote from the research from Imperial College Lo ndon, "In this paper, we are concerned with the challenge of trust transfer, i.e . whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative ta sk. This has the potential of avoiding re-computing trust levels from scratch fo r every new situation.The key challenge here is to automatically evaluate the s imilarity between the original and the novel situation, then adapt the robot's b ehaviour to the novel situation using previous experience with various objects a nd tasks. To achieve this, we measure the semantic similarity between concepts i n knowledge graphs (KGs) and adapt the robot's actions towards a specific user b ased on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human-robot handover tasks in different kitchen scene contexts."
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    Data on Robotics Discussed by Researchers at King Mongkut's Institute of Technol ogy (Localization for Outdoor Mobile Robot Using Lidar and Rtk-gnss/ins)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news reporting originating in Bangkok, Thailand, by NewsRx journalists, research stated, "Two types of sensors, light detection and rangin g (LiDAR) and real-time kinematic of global navigation satellite system with ine rtial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning." Financial support for this research came from School of Engineering, King Mongku t's Institute of Technology Ladkrabang, Bangkok, Thailand. The news reporters obtained a quote from the research from the King Mongkut's In stitute of Technology, "Therefore, a sensor fusion approach based on an adaptive -network-based fuzzy inference system (ANFIS) was implemented to enhance reliabi lity. In this research, data from both sensors were collected to create a datase t for training with ANFIS. The findings indicated that the model derived from th e fusion of these two sensors provided results that were much closer to the actu al values obtained using each sensor independently."
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    Researcher from Doshisha University Discusses Findings in Robotics (The Effect o f a Limited Underactuated Posterior Joint on the Speed and Energy Efficiency of a Fish Robot)

    47-48页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news originating from Kyoto, Japan, by NewsRx corresponden ts, research stated, "Autonomous underwater vehicles (AUVs) commonly use screw p ropellers to move in a water environment." Our news correspondents obtained a quote from the research from Doshisha Univers ity: "However, compared to the propeller-driven AUV, bio-inspired AUVs feature a higher energy efficiency, longer lifespan (due to a lack of cavitation), and be tter eco-friendliness (due to lower noise, a lack of vibrations, and a weaker wa ke). To generate propulsion, the design of fish robots-viewed as a special case of a bio-inspired AUV-comprise multiple actuated joints. Underactuated joints ha ve also been adopted in bio-inspired AUVs, primarily for the purpose of achievin g a simpler design and more realistic and biologically plausible locomotion. In our work, we propose a limitedly underactuated posterior (tail) joint of a fish robot with the intention of achieving a higher swimming speed and better energy efficiency of the robot."
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