Ergonomic Adaptation of Robotic Movements in Human-Robot Collaboration

Broek, Marike Koch van den; Moeslund, Thomas B.

doi:10.1145/3371382.3378304

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…However, they only collect and classify design guidelines and prerequisites according to standards, research works, and real use cases, rather than develop a structured design method. Other existing methods deal with the adaptation of the robot's movements to improve the operator's ergonomic condition (Van Den Broek and Moeslund 2020) or reduce human fatigue (Peternel et al 2018).…”

Section: Design Methods For Hrcmentioning

confidence: 99%

A human-oriented design process for collaborative robotics

Papetti

Ciccarelli

Scoccia

et al. 2022

International Journal of Computer Integrated Manufacturing

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The potential of collaborative robotics often does not materialize in an efficient design of the human-robot collaboration. Technology-oriented approaches are no longer enough in the Industry 4.0 era. This work proposes a set of methods to support manufacturing engineers in the humanoriented design process of integrated production systems to obtain satisfactory performance in the mass customization paradigm, without impacting the safety and health of workers. It founds the design criteria definition on five main pillars (safety, ergonomics, effectiveness, flexibility, and costs), favors the consideration of different design alternatives, and leads their selection. The dynamic impact of the design choices on the various elements of the system prevails over the static design constraints. The method has been experimented in collaboration with the major kitchen manufacturer in Italy, which introduced a collaborative robotics cell in the drawers' assembly line. It resulted in a more balanced production line (10% more), a verified risk minimization (RULA score reduced from 5 to 3 and OCRA score from 13.30 to 5.70), and a greater allocation of operators to high added value activities.

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Section: Design Methods For Hrcmentioning

confidence: 99%

A human-oriented design process for collaborative robotics

Papetti

Ciccarelli

Scoccia

et al. 2022

International Journal of Computer Integrated Manufacturing

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“…The analysis of human body pose augments ergonomic assessment, handover chores, and bolstering efficacy and safety in HRI and HRC. [42][43][44] In contrast, hand gesture recognition finds utility in scenarios such as collaborative manipulation, robot programming, surgical robot teleoperation, and beyond. [45][46][47][48][49][50][51][52] Table 1.…”

Section: Human Posementioning

confidence: 99%

“…Body detection MobileNet-SSD; [18] Openpose þ SVM; [19] Bayesian Siamese neural network þ CVAE; [20] YOLO þ Bayesian DNN [21] Human following and autonomous navigation; Visual tracking for autonomous robots tasked with humans and environment interaction; Safe HRI and HRC Face recognition SSD þ FaceNetþKCF; [22] SFPD [23] Human following and autonomous navigation; Simultaneous face and person detection for real-time HRI Human activity Activity recognition Two-stream CNN; [24] 3D LRCN þ 3D CNN þ LSTM; [25] LSTM þ VAE þ DRL; [26] 3D-CNN; [27] STJ-CNN; [28] TCN [29] Collaborative assembly and packaging; Safe HRI and HRC; Companion robots; HRI and VR applications Intention prediction CNN; [30] ILSTM þ IBi-LSTM; [31] CNN þ VMM [32] Surveillance; Collaborative assembly Motion prediction RSSAC-Trajectronþþ; [33] RNN; [34,35] VAE; [36] CVAE þ LSTM; [37] Dynamic motion projection; [38] RNN þ RIMEDNet [39] Safe and efficient HRI and HRC; Collaborative manipulation and assembly; Human imitation; Social HRI; Handover tasks Attention estimation ANN; [40] LSTM [41] Attention level estimation; Blind 3D human attention inference Human pose Body pose recognition OpenPose þ Angle-based rules; [42] Fast-SCNN þ REDE; [43] PoseNet [44] Ergonomics in HRC; Handover task; Efficient and safe HRI and HRC…”

Section: Human Positionmentioning

confidence: 99%

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Wang,

Zheng,

et al. 2023

Advanced Intelligent Systems

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Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

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“…For instance, recognizing the action "wear a shoe" from the NTU dataset can be time-consuming [19]. This latency impedes applications such as human-robot interaction, where swift anticipatory behavior is crucial for seamless cooperation [4], [20].…”

Section: Introductionmentioning

confidence: 99%

InfoGCN: Representation Learning for Human Skeleton-based Action Recognition

Chi

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

188

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Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in realtime situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeletonbased action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence's length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what's possible in online, skeleton-based action recognition. The code for InfoGCN++ is publicly available at https://github.com/stnoah1/infogcn2 for further exploration and validation.

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Ergonomic Adaptation of Robotic Movements in Human-Robot Collaboration

Cited by 9 publications

References 6 publications

A human-oriented design process for collaborative robotics

A human-oriented design process for collaborative robotics

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

InfoGCN: Representation Learning for Human Skeleton-based Action Recognition

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