Human motion prediction for human-robot collaboration

Liu, Hongyi; Wang, Lihui

doi:10.1016/j.jmsy.2017.04.009

Cited by 153 publications

(54 citation statements)

References 22 publications

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“…This prediction is finally considered in order to generate the optimal robot trajectory. In parallel, Liu and Wang [133] modelled the recognised human motions in a Hidden Markov model (HMM). A motion transition probability matrix is then generated after solving the HMM.…”

Section: Convolutional Layers Input Imagementioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Section: Convolutional Layers Input Imagementioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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“…To prevent injuries, there exist tools able to detect dangerous situations and send a warning to workers. Several danger prediction technics exist, such as monitoring and detecting collisions (Wang et al, 2013;Schmidt & Wang, 2014), human motions in an assembly line (Liu & Wang, 2017;Coupeté et al, 2019;Unhelkar et al, 2018;Marin et al, 2018;Gopinathan et al, 2018), workers' presence and intentions (Koch et al, 2017).…”

Section: Safetymentioning

confidence: 99%

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Hashemi-Petroodi

Thevenin

Kovalev

et al. 2020

Annual Reviews in Control

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A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot (hybrid) system, where humans and robots perform a variety of tasks (manual, automated, and hybrid tasks) in a shared workspace. Contrarily to the existing surveys, this study is dedicated to operations management problems (focusing on the applications and features) for human and machine/robot collaborative systems in manufacturing. This research is divided into two types of interactions between human and automated components in manufacturing and assembly systems: dual resource constrained (DRC) and human-robot collaboration (HRC) optimization problems. Moreover, different characteristics of the workforce and machines/robots such as heterogeneity, homogeneity, and ergonomics are introduced. Finally, this paper identifies the optimization challenges and problems for hybrid systems. The existing literature on HRC focuses mainly on the robotic point of view and not on the operations management and optimization aspects. Therefore, the future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput. In addition, studying flexibility and reconfigurability in hybrid systems is one of the main research avenues for future research.

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“…Actually, the main part of the integration of a collaborative into an existing production system will be the division of tasks and activities between the operator and the robot. There are different studies relating to human-robot coordination and the "dynamic" task division in collaborative applications (Chen et al 2011;Darvish et al 2018;Liu and Wang 2017), which means a real-time sequencing of activities depending on different operator behaviors and preferences during the assembly cycle. In this situation, the operator can freely choose which task will be the next one indiscriminately.…”

Section: Collaborative Robot Operatormentioning

confidence: 99%