A Practical and Effective Layout for a Safe Human-Robot Collaborative Assembly Task
Source abstract
Abstract
This work describes a layout to carry out a demonstrative assembly task, during which a collaborative robot performs pick-and-place tasks to supply an operator the parts that he/she has to assemble. In this scenario, the robot and operator share the workspace and a real time collision avoidance algorithm is implemented to modify the planned trajectories of the robot avoiding any collision with the human worker. The movements of the operator are tracked by two Microsoft Kinect v2 sensors to overcome problems related with occlusions and poor perception of a single camera. The data obtained by the two Kinect sensors are combined and then given as input to the collision avoidance algorithm. The experimental results show the effectiveness of the collision avoidance algorithm and the significant gain in terms of task times that the highest level of human-robot collaboration can bring.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Robotic Path Planning Algorithms · Teleoperation and Haptic Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
References (41)
Show source-provided references
1. Pearson, T., Menassa, R., Shi, J., and Jimmerson, G. (2012). Levels of human and robot collaboration for automotive manufacturing. Proc. Workshop Perform. Metr., 95–100.
2. Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications10.1016/j.mechatronics.2018.02.009 ↗
3. Matheson, E., Minto, R., Zampieri, E.G.G., Faccio, M., and Rosati, G. (2019). Human–robot collaboration in manufacturing applications: A Review. Robotics, 8.10.3390/robotics8040100 ↗
4. Collaborative robotics: New era of human–robot cooperation in the workplace10.1016/j.jsr.2020.06.013 ↗
5. Collaborative Robotics: A Survey10.1115/1.4046238 ↗
6. IFR (2018). Demystifying Collaborative Industrial Robots, IFR.
7. Cooperation of human and machines in assembly lines10.1016/j.cirp.2009.09.009 ↗
8. ISO/TS 15066 (2016). Robots and Robotic Devices: Collaborative Robots, International Organization for Standardization.
9. A survey of depth and inertial sensor fusion for human action recognition10.1007/s11042-015-3177-1 ↗
10. Zanuttigh, P., Marin, G., Dal Mutto, C., Dominio, F., Minto, L., and Cortelazzo, G.M. (2016). Time-of-Flight and Structured Light Depth Cameras, Springer International Publishing.10.1007/978-3-319-30973-6 ↗
11. Tilove, R.B. (1990, January 13–18). Local obstacle avoidance for mobile robots based on the method of artificial potentials. Proceedings of the Proceedings IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA.
12. Real-time obstacle avoidance for manipulators and mobile robots10.1177/027836498600500106 ↗
13. Collision avoidance system for collaborative robotics
14. Collision avoidance algorithm for collaborative robotics10.20965/ijat.2017.p0481 ↗
15. Multiple collision avoidance between human limbs and robot links algorithm in collaborative tasks
16. Influence of human limb motion speed in a collaborative hand-over task
17. Scimmi, L.S., Melchiorre, M., Mauro, S., and Pastorelli, S. (2019, January 23–26). Implementing a vision-based collision avoidance algorithm on a UR3 Robot. Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy.10.1109/ICMECT.2019.8932105 ↗
18. Melchiorre, M., Scimmi, L.S., Pastorelli, S., and Mauro, S. (2019, January 23–26). Collision avoidance using point cloud data fusion from multiple depth Sensors: A Practical Approach. Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy.10.1109/ICMECT.2019.8932143 ↗
19. Scimmi, L.S., Melchiorre, M., Mauro, S., and Pastorelli, S. (2019, January 2–4). Experimental real-time setup for vision driven hand-over with a collaborative robot. Proceedings of the 2019 International Conference on Control, Automation and Diagnosis (ICCAD), Grenoble, France.10.1109/ICCAD46983.2019.9037961 ↗
20. Vision-based control architecture for human–robot hand-over applications10.1002/asjc.2480 ↗
21. Niola, V., and Gasparetto, A. (2021). Human arm motion tracking by Kinect sensor using Kalman filter for collaborative robotics. Advances in Italian Mechanism Science, Springer. IFToMM ITALY 2020; Mechanisms and Machine Science.
22. Method for the evaluation of layout options for a human-robot collaboration10.1016/j.procir.2019.04.068 ↗
23. Collaborative manufacturing with physical human–robot interaction10.1016/j.rcim.2015.12.007 ↗
24. Introduction and configuration of a collaborative robot in an assembly task as a means to decrease occupational risks and increase efficiency in a manufacturing company10.1016/j.rcim.2018.12.015 ↗
25. Application of dynamically scaled safety zones based on the ISO/TS 15066:2016 for collaborative robotics
26. Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements10.1016/j.mechatronics.2017.12.009 ↗
27. Towards an optimal avoidance strategy for collaborative robots10.1016/j.rcim.2019.01.015 ↗
28. Considerations of potential runaway motion and physical interaction for speed and separation monitoring10.1016/j.rcim.2020.102034 ↗
29. Prediction of human activity patterns for human–robot collaborative assembly tasks10.1109/TII.2018.2882741 ↗
30. (2021, February 12). Kinect SDK. Available online: https://developer.microsoft.com/it-IT/windows/kinect.
31. Real-time human pose recognition in Parts from Single Depth Images
32. Barros, J.M.D., Garcia, F., and Sidibè, D. (2015, January 11–14). Real-time human pose estimation from body-scanned point clouds. Proceedings of the International Conference on Computer Vision Theory and Applications, Berlin, Germany.
33. Improved skeleton tracking by a duplex kinects: A practical approach for real-time applications10.1115/1.4025404 ↗
34. (2021, February 12). Universal Robots. Available online: https://www.universal-robots.com/articles/ur/remote-control-via-tcpip/.
35. Murphy, R. (2000). Introduction to AI Robotics, The MIT Press.
36. Flacco, F., Kröger, T., De Luca, A., and Khatib, O. (2012, January 14–18). A depth space approach to human-robot collision avoidance. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.10.1109/ICRA.2012.6225245 ↗
37. Real-time strategy for obstacle avoidance in redundant manipulators
38. (2021, February 12). MathWorks. Available online: https://www.mathworks.com/help/stats/knnsearch.html.
39. Sciavicco, L., and Siciliano, B. (2012). Modelling and Control of Robot Manipulators, Springer Science & Business Media.
40. (2021, February 12). Azure Kinect DK. Available online: https://docs.microsoft.com/en-us/azure/kinect-dk/.
41. Digo, E., Antonelli, M., Cornagliotto, V., Pastorelli, S., and Gastaldi, L. (2020). Collection and analysis of human upper limbs motion features for collaborative robotic applications. Robotics, 9.10.3390/robotics9020033 ↗