Human Digital Twin in the context of Industry 5.0
Human-centricity, a core value of Industry 5.0, places humans in the center of production. It
leads to the prioritization of human needs, spanning from health and safety to self …
leads to the prioritization of human needs, spanning from health and safety to self …
Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
[HTML][HTML] Robot learning towards smart robotic manufacturing: A review
Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …
manufacturing. Since the beginning of the first integration of industrial robots into production …
Reinforcement learning for disassembly system optimization problems: A survey
The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …
methods are facing difficulties in solving the decision-making and control problems of …
Multi-agent reinforcement learning method for disassembly sequential task optimization based on human–robot collaborative disassembly in electric vehicle battery …
With the wide application of new Electric Vehicle (EV) batteries in various industrial fields, it
is important to establish a systematic intelligent battery recycling system that can be used to …
is important to establish a systematic intelligent battery recycling system that can be used to …
An adaptive human sensor framework for human–robot collaboration
Manufacturing challenges are increasing the demands for more agile and dexterous means
of production. At the same time, these systems aim to maintain or even increase productivity …
of production. At the same time, these systems aim to maintain or even increase productivity …
Dynamic scenario-enhanced diverse human motion prediction network for proactive human–robot collaboration in customized assembly tasks
Human motion prediction is crucial for facilitating human–robot collaboration in customized
assembly tasks. However, existing research primarily focuses on predicting limited human …
assembly tasks. However, existing research primarily focuses on predicting limited human …
Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review
T Kiyokawa, N Shirakura, Z Wang, N Yamanobe… - Robotics and Computer …, 2023 - Elsevier
This paper presents a literature review on the different aspects of task allocation and
assignment problems in human–robot collaboration (HRC) tasks in industrial assembly …
assignment problems in human–robot collaboration (HRC) tasks in industrial assembly …
Deep reinforcement learning applied to an assembly sequence planning problem with user preferences
Deep reinforcement learning (DRL) has demonstrated its potential in solving complex
manufacturing decision-making problems, especially in a context where the system learns …
manufacturing decision-making problems, especially in a context where the system learns …
[HTML][HTML] Performance comparison of reinforcement learning and metaheuristics for factory layout planning
Factory layout planning is a time-consuming process that has a large impact on the
operational performance of a future factory. Besides, changing technologies and market …
operational performance of a future factory. Besides, changing technologies and market …