Modeling of human–robot collaboration for flexible assembly—a hidden semi-Markov-based simulation approach

Wang, Kung‐Jeng; Lin, Chiuhsiang Joe; Tadesse, Ahmed Abide; Woldegiorgis, Bereket Haile

doi:10.1007/s00170-023-11404-2

Cited by 5 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [ 18 ] trained machines to process time series to identify state problems using a hidden semi-Markov model, taking into account the dependence of state duration. Kung-Jeng Wang et al [ 19 ] propose a method for Human–Robot Collaboration (HRC) that allows the embedding of a risk-aware module into a hidden semi-Markov model. The specific step is to link assembly rate and risk perception, requiring a reduction in robot assembly cycle time while reducing safety risk.…”

Section: Related Workmentioning

confidence: 99%

Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for Pilots

Gao

Wang

2023

Sensors

View full text Add to dashboard Cite

Pilots’ loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots’ eye movements during various flight tasks from the perspective of visual awareness. The pilot’s gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot’s visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%.

show abstract

Section: Related Workmentioning

confidence: 99%