首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    Reports on Robotics from Chinese Academy of Sciences Provide New Insights (Water-mbsl: Underwater Movable Binocular Structured Light-based High-precision Dense Reconstruction Framework)

    106-107页
    查看更多>>摘要:Current study results on Robotics have been published. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Structured light systems are widely used in underwater dense reconstruction due to their excellent accuracy. However, the current related methods mainly focus on fixed positions.” Financial support for this research came from Beijing Natural Science Foundation. The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The reconstruction performance in motion is insufficient. Therefore, we propose an underwater movable binocular structured light (MBSL) based high-precision dense reconstruction framework, named WaterMBSL, to realize the robot reconstruction while moving. Specifically, an onboard binocular structured light system based on mirror-galvanometer is developed first. Then, a simplified underwater point cloud acquisition algorithm is presented to quickly obtain 3-D information of the scene. Besides, a new underwater motion compensation algorithm combining inertial measurement unit and uniform velocity model is proposed. Moreover, the generalized-ICP point cloud registration algorithm is introduced to achieve accurate motion estimation. Finally, an underwater movable reconstruction platform is developed by integrating the selfdesigned structured light system with the underwater robot BlueROV for validating the performance of our proposed Water-MBSL.”

    New Findings in Robotics Described from Wuhan University of Technology (Predictive Exposure Control for Vision-based Robotic Disassembly Using Deep Learning and Predictive Learning)

    107-108页
    查看更多>>摘要:A new study on Robotics is now available. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “Lighting conditions can affect the performance of vision-based robots in manufacturing. This paper presents a predictive exposure control method that allows the acquisition of high-quality images in real time under poor lighting conditions.” Funders for this research include Engineering & Physical Sciences Research Council (EPSRC), National Natural Science Foundation of China (NSFC), China Scholarship Council. The news correspondents obtained a quote from the research from the Wuhan University of Technology, “This technique is particularly useful in robotic disassembly where a fixed and optimised lighting environment is difficult to construct due to the uncertain conditions of used components, and the optimal exposure conditions for each used component are different. We first develop a region-of-interest (ROI) extraction module capable of identifying ROIs under poor light exposure, in which the states of captured images under various lighting conditions are hypothesised to enhance the extraction ability of a deep learning-based object detector. The extraction results can help a robot obtain an optimal capture position and are incorporated with information about entropy to assess the image quality of ROIs in the proposed ROI quality assessment module. We further design an exposure-entropy prediction model based on predictive learning. This lightweight model is crucial in assisting the exposure time prediction module to achieve real-time searching for the optimal exposure time. The performance of the proposed exposure control method is validated using a screw-removal case study in the application to end-of-life electric vehicle battery disassembly. Together with the ROI extraction module and the ROI quality assessment module, the exposure time prediction module enables the accurate and efficient estimation of optimal exposure time and delivers high-quality images under poor lighting conditions.”

    Zhejiang University Reports Findings in Machine Learning (Identification and assessment of new PIM2 inhibitors for treating hematologic cancers: A combined approach of energy-based virtual screening and machine learning evaluation)

    108-108页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “PIM2, part of the PIM kinase family along with PIM1 and PIM3, is often overexpressed in hematologic cancers, fueling tumor growth. Despite its significance, there are no approved drugs targeting it.” Our news journalists obtained a quote from the research from Zhejiang University, “In response to this challenge, we devised a thorough virtual screening workflow for discovering novel PIM2 inhibitors. Our process includes molecular docking and diverse scoring methods like molecular mechanics generalized born surface area, XGBOOST, and DeepDock to rank potential inhibitors by binding affinities and interaction potential. Ten compounds were selected and subjected to an adequate evaluation of their biological activity. Compound 2 emerged as the most potent inhibitor with an IC of approximately 135.7 nM. It also displayed significant activity against various hematological cancers, including acute myeloid leukemia, mantle cell lymphoma, and anaplastic large cell lymphoma (ALCL). Molecular dynamics simulations elucidated the binding mode of compound 2 with PIM2, offering insights for drug development.”

    Patent Issued for Robot interaction with human co-workers (USPTO 11872702)

    109-112页
    查看更多>>摘要:A patent by the inventors Hebert, Mitchell (Littleton, CO, US), Johnson, David M. S. (Cambridge, MA, US), Lawson, Connor (Cambridge, MA, US), Lines, Steven (Brookline, MA, US), Wagner, Syler (Somerville, MA, US), filed on September 13, 2019, was published online on January 16, 2024, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents. Patent number 11872702 is assigned to Charles Stark Draper Laboratory Inc. (Cambridge, Massachusetts, United States). The following quote was obtained by the news editors from the background information supplied by the inventors: “Traditionally, the food industry employs human labor to manipulate ingredients with the purpose of either assembling a meal such as a salad or a bowl, or packing a box of ingredients such as those used in grocery shopping, or preparing the raw ingredients. Robots have not yet been able to assemble complete meals from prepared ingredients in a food-service setting such as a restaurant, largely because the ingredients are arranged unpredictably and change shape in difficult-to-predict ways rendering traditional methods to move material ineffective without extensive modifications to existing kitchens. Additionally, traditional material handling methods are ill-suited to moving cooked foods without altering their texture and taste-profile. These difficulties arise because the friction, stiction, and viscosity of commonly consumed foods cause auger, conveyor, and suction mechanisms to become clogged and soiled, while these mechanisms simultaneously impart forces on the foodstuffs which alter their texture, consistency, and taste-profile in unappetizing ways.”

    Patent Issued for Systems and methods for interpreting high energy interactions (USPTO 11874240)

    112-114页
    查看更多>>摘要:From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Drake, Brandon Lee Goodchild (Greeley, CO, US), filed on October 4, 2019, was published online on January 16, 2024. The patent’s assignee for patent number 11874240 is Decision Tree LLC (Greeley, Colorado, United States). News editors obtained the following quote from the background information supplied by the inventors: “The miniaturization of analytical instrumentation has enabled non-destructive analysis of objects, often by way of using portable systems. This shift contrasts with the past use of this type of equipment in which analytes were destroyed during the analysis process, in some instances by being prepared into matrices such as solutions and fused glass beads. Destructive sample preparation enabled quantification based on models which used the intensities of diagnostic fluorescent peaks, reflectively scattered angles, or emitted radiation following ionization. These signals are defined and interpreted via empirical calibration or estimation through physical and geometric parameters. This approach has largely been adopted for non- or minimally-destructive analyzers, though this type of analysis is greatly complicated by sample heterogeneity, chemical transformation, and uneven surfaces. Performance of non-destructive analyzers is thus generally inferior relative to their destructive counterparts.

    Patent Issued for Collaborative device with optimised control (USPTO 11872697)

    115-117页
    查看更多>>摘要:From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Devy, Roland (Joue-les-Tours, FR), Foucault, Paul (Chinon, FR), filed on October 6, 2020, was published online on January 16, 2024. The patent’s assignee for patent number 11872697 is Fts Welding (Joue-les-Tours, France). News editors obtained the following quote from the background information supplied by the inventors: “By collaborative work device, it should be understood a device comprising a robot which evolves in the middle of humans. In particular, such a collaborative device can adopt an automatic mode in which it is autonomous and works without human intervention, and a manual mode in which a technician uses the device. In the manual mode, the technician can in particular collaborate with the robot, in particular to position parts before the robot acts on said parts.

    Patent Application Titled 'Apparatus And Method For Forming Plastic Preforms Into Plastic Containers With Changeover Robot' Published Online (USPTO 20240017466)

    117-121页
    查看更多>>摘要:According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors GELTINGER, Florian (Donaustauf, DE); SCHLAGENHAUFER, Martin (Regensburg, DE), filed on July 17, 2023, was made available online on January 18, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “The present document relates to an apparatus and a method for forming plastic preforms into plastic containers. Such apparatuses and methods have long been known from the prior art. Usually, heated plastic preforms are formed into plastic containers by means of applying a flowable medium, in particular compressed air. More recently, however, it has also been known to use a liquid, such as in particular but not exclusively the beverage to be filled, to expand the plastic preforms.

    'Control Device, Control Method, And Program' in Patent Application Approval Process (USPTO 20240017412)

    121-124页
    查看更多>>摘要:A patent application by the inventors MATSUMOTO, Shinya (Kyoto-shi, KYOTO, JP); TONOGAI, Norikazu (Kyoto-shi, KYOTO, JP), filed on September 14, 2021, was made available online on January 18, 2024, according to news reporting originating from Washington, D.C., by NewsRx correspondents. This patent application has not been assigned to a company or institution. The following quote was obtained by the news editors from the background information supplied by the inventors: “For safe operation at manufacturing or other sites, an area including an object (contact area) within a predetermined area is determined in advance. “Patent Literature 1 describes a robot control device that determines an area (contact area) including an object by obtaining depth data at each point on the surface of the object using a depth sensor (a three-dimensional or 3D sensor) on a robot.”

    Patent Application Titled 'Online Test Time Adaptive Semantic Segmentation With Augmentation Consistency' Published Online (USPTO 20240020848)

    125-128页
    查看更多>>摘要:According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors AZARIAN YAZDI, Kambiz (San Diego, CA, US); BORSE, Shubhankar Mangesh (San Diego, CA, US); CAI, Hong (San Diego, CA, US); DAS, Debasmit (San Diego, CA, US); GARREPALLI, Risheek (San Diego, CA, US); PARK, Hyojin (San Diego, CA, US); PORIKLI, Fatih Murat (San Diego, CA, US), filed on July 10, 2023, was made available online on January 18, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “The increasing versatility of digital camera products has allowed digital cameras to be integrated into a wide array of devices and has expanded their use to different applications. For example, phones, drones, cars, computers, televisions, and many other devices today are often equipped with camera devices. The camera devices allow users to capture images and/or video from any system. The images and/or videos can be captured for recreational use, professional photography, surveillance, and automation, among other applications. Moreover, camera devices are increasingly equipped with specific functionalities for modifying images or creating artistic effects on the images. For example, many camera devices are equipped with image processing capabilities for generating different effects on captured images.

    Patent Issued for Machine learning model for determining a time interval to delay batching decision for an order received by an online concierge system to combine orders while minimizing probability of late fulfillment (USPTO 11875394)

    129-133页
    查看更多>>摘要:According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Faturechi, Reza (San Francisco, CA, US), Putrevu, Jagannath (Daly City, CA, US), Wang, Site (Fremont, CA, US), filed on February 2, 2022, was published online on January 16, 2024. The assignee for this patent, patent number 11875394, is Maplebear Inc. (San Francisco, California, United States). Reporters obtained the following quote from the background information supplied by the inventors: “This disclosure relates generally to fulfilling orders for items through an online concierge system, and more specifically to machine learning models for determining a time interval for delaying identification of a received order to one or more shoppers for selection to increase batching of orders while avoiding late fulfillment.