Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly

Hjorth, Sebastian; Chrysostomou, Dimitrios

doi:10.1016/j.rcim.2021.102208

Cited by 132 publications

(43 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Actions. The 7 types of actions of CHICO are inspired from ordinary work sessions in an HRC disassembly line as described in the review work of [29]. Each action is repeated over a time interval of ∼1 minute on average.…”

Section: The Chico Datasetmentioning

confidence: 99%

Pose Forecasting in Industrial Human-Robot Collaboration

Alessio¹,

Guido²,

Avogaro³

et al. 2022

Preprint

View full text Add to dashboard Cite

Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channelwise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is ∼4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64.

show abstract

Section: The Chico Datasetmentioning

confidence: 99%

Pose Forecasting in Industrial Human-Robot Collaboration

Alessio¹,

Guido²,

Avogaro³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“… Ref. PY DSRSA DT-HRI DT-IR/DT-RM DT-SPR DT-SR DT-MR/DT-RR Aivaliotis et al [13] 2019 ✗ ✗ ✓ ✗ ✗ ✗ Lu et al [14] 2020 ✗ ✓ ✓ ✗ ✗ ✗ Lattanziet et al [15] 2021 ✗ ✓ ✓ ✗ ✗ ✗ Phanden et al [16] 2021 ✗ ✗ ✓ ✓ ✗ ✗ Bi et al [17] 2021 ✗ ✓ ✓ ✗ ✗ ✗ Lambrecht et al [18] 2021 ✗ ✓ ✓ ✗ ✗ ✗ Hjorth & Chrysostomou [19] 2022 ✗ ✓ ✗ ✗ ✗ ✗ Fan et al [20] 2022 ✗ ✓ ✗ ✗ ✗ ✗ Ramasubramanian et al [21] 2022 ✗ ✓ ✗ ✗ ✗ ✗ Semeraro et al [22] 2023 [Available...…”

Section: Introductionmentioning

confidence: 99%

Towards next generation digital twin in robotics: Trends, scopes, challenges, and future

Mazumder¹,

Sahed²,

Tasneem³

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…The traditional industrial robot realizes repetitive action through teaching and solving the problem of efficiency in mass production, but emerging manufacturing industries are moving toward more varieties and smaller batches. Therefore, traditional manual teaching and fixed-action robots cannot meet the needs of the emerging manufacturing industry [1], [2]. Machine vision can not only replace artificial vision, but also analyze and explain through visual processing [3], [11].…”

Section: Introductionmentioning

confidence: 99%

Robot Target Location Based on the Difference in Monocular Vision Projection

Zheng

Deng

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Visual guidance is widely used in industrial robots. At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance. However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced. A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image. First, the contour region of the template image is divided according to the position of the grasping points. Then the weight is assigned according to the distance from each contour region to the grasping points. Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured. Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately. A large number of experiments show that the method has the design requirements of high stability and high accuracy.

show abstract

Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly

Cited by 132 publications

References 102 publications

Pose Forecasting in Industrial Human-Robot Collaboration

Pose Forecasting in Industrial Human-Robot Collaboration

Towards next generation digital twin in robotics: Trends, scopes, challenges, and future

Robot Target Location Based on the Difference in Monocular Vision Projection

Contact Info

Product

Resources

About