SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Liu Wenjun)
 

Sökning: WFRF:(Liu Wenjun) > Adaptive real-time ...

Adaptive real-time similar repetitive manual procedure prediction and robotic procedure generation for human-robot collaboration

Liu, Zhihao (författare)
KTH,Produktionsutveckling,School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070, China
Liu, Quan (författare)
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks (Wuhan University of Technology), Wuhan 430070, China
Xu, Wenjun (författare)
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks (Wuhan University of Technology), Wuhan 430070, China
visa fler...
Wang, Lihui (författare)
KTH,Produktionsutveckling
Ji, Zhenrui (författare)
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks (Wuhan University of Technology), Wuhan 430070, China
visa färre...
 (creator_code:org_t)
Elsevier BV, 2023
2023
Engelska.
Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 58
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Manual procedure recognition and prediction are essential for practical human-robot collaboration in industrial tasks, such as collaborative assembly. However, current research mostly focuses on diverse human motions, while the similar repetitive manual procedures that are prevalent in real production tasks are often overlooked. Furthermore, the dynamic uncertainty caused by human-robot interferences and the generalisation of individuals, scenarios, and multiple sensor deployments pose challenges for implementing manual procedure prediction and robotic procedure generation. To address these issues, this paper proposes a real-time, similar repetitive procedure-oriented human skeleton processing system that employs the human skeleton as a robust modality. It utilises an improved deep spatial-temporal graph convolutional network and a FIFO queue-based discriminator for real-time data processing, procedure prediction, and generation. The proposed method is validated on multiple datasets with tens of individuals engaged in a real dynamic and uncertain human-robot collaborative assembly cell and able to run on entry-level hardware. The results demonstrate competitive performance of handcraft feature-free, early prediction and generalisation on individual variance, environment background, camera position, lighting conditions, and stochastic interference in human-robot collaboration.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

Collaborative assembly
Human-robot collaboration
Manual procedure prediction
Robotic procedure generation
Similar repetitive manual procedure

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy