kth.sePublications
Change search
Refine search result
1234567 1 - 3 of 24502
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. ABB Corporate Research, Västerås, Sweden.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Gao, Robert X.
    Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, USA.
    Cognitive neuroscience and robotics: Advancements and future research directions2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, article id 102610Article, review/survey (Refereed)
    Abstract [en]

    In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions.

  • 2.
    Li, Xuebing
    et al.
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Yue, Caixu
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Liu, Xianli
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Zhou, Jiaqi
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    ACWGAN-GP for milling tool breakage monitoring with imbalanced data2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, article id 102624Article in journal (Refereed)
    Abstract [en]

    Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece quality and mini-mizing economic losses. Under the premise of sufficient training data with a balanced distribution, TBM methods based on statistical analysis and artificial intelligence enable accurate recognition of tool breakage conditions. However, considering the actual manufacturing safety, cutting tools usually work in normal wear conditions, and acquiring tool breakage signals is extremely difficult. The data imbalance problem seriously affects the recog-nition accuracy and robustness of the TBM model. This paper proposes a TBM method based on the auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) from the perspective of data generation. By introducing Wasserstein distance and gradient penalty terms into the loss function of ACGAN, ACWGAN-GP can generate multi-class fault samples while improving the network's stability during adversarial training. A sample filter based on multiple statistical indicators is designed to ensure the quality and diversity of the generated data. Qualified samples after quality assessment are added to the original imbalanced dataset to improve the tool breakage classifier's performance. Artificially controlled face milling experiments for TBM are carried out on a five-axis CNC machine to verify the effectiveness of the proposed method. Experimental results reveal that the proposed method outperforms other popular imbalance fault diagnosis methods in terms of data generation quality and TBM accuracy, and can meet the real-time requirements of TBMs.

  • 3.
    Zhang, Xuexin
    et al.
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
    Zheng, Lianyu
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China; Beijing Key Laboratory of digital design and manufacturing technology, Beijing 100191, China.
    Fan, Wei
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China; Beijing Key Laboratory of digital design and manufacturing technology, Beijing 100191, China.
    Ji, Wei
    Sandvik Coromant, Stockholm 12679, Sweden.
    Mao, Lingjun
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, p. 102609-, article id 102609Article in journal (Refereed)
    Abstract [en]

    Robotic machining is a potential method for machining large-scale components (LSCs) due to its low cost and high flexibility. However, the low stiffness of robots and complex machining process of LSCs result in a lack of alignment between the physical process and digital models, making it difficult to realize the robotic machining of LSCs. The recent Digital Twin (DT) concept shows potential in terms of representing and modeling physical processes. Therefore, this study proposes a robotic machining DT for LSCs. However, the current DT is not capable of knowledge representation, multi-source data integration, optimization algorithm implementation, and real-time control. To address these issues, Knowledge Graph (KG) and Function Block (FB) are employed in the proposed robotic machining DT. Here, robotic machining related information, such as the machining parameters and errors, is represented in the virtual space by building the KG, whereas the FBs are responsible for integrating and applying the algorithms for process execution and optimization based on real-world events. Moreover, a novel adaptive process adjustment strategy is proposed to improve the efficiency of the process execution. Finally, a prototype system of the robotic machining DT is developed and validated by an experiment on robotic milling of the assembly interface for an LSC. The results demonstrate that the robotic machining is successfully optimized and improved by the proposed method.

1234567 1 - 3 of 24502
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf