Abstract
AbstractIn human–robot collaboration (HRC), human motion capture can be considered an enabler for switching autonomy between humans and robots to create efficient and safe operations. For this purpose, wearable motion tracking systems such as IMU and lighthouse-based systems have been used to transfer human joint motions into robot controller models. Due to reasons such as global positioning, drift, and occlusion, in some situations, e.g., HRC, both systems have been combined. However, it is still not clear if the motion quality (e.g., smoothness, naturalness, and spatial accuracy) is sufficient when the human operator is in the loop. This article presents a novel approach for measuring human motion quality and accuracy in HRC. The human motion capture has been implemented in a laboratory environment with a repetition of forty-cycle operations. Human motion, specifically of the wrist, is guided by the robot tool center point (TCP), which is predefined for generating circular and square motions. Compared to the robot TCP motion considered baseline, the hand wrist motion deviates up to 3 cm. The approach is valuable for understanding the quality of human motion behaviors and can be scaled up for various applications involving human and robot shared workplaces.
Funder
Bundesministerium für Bildung und Forschung
European Regional Development Fund
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Mechanical Engineering,Engineering (miscellaneous),Computational Mechanics
Reference36 articles.
1. Agethen P, Gaisbauer F, Manns M, Link M, Rukzio E (2018) Towards realistic walk path simulation of single subjects: presenting a probabilistic motion planning algorithm. In: Proceedings of the 11th annual international conference on motion, interaction, and games—MIG ’18. ACM Press, Limassol, Cyprus, pp 1–10. https://doi.org/10.1145/3274247.3274504. http://dl.acm.org/citation.cfm?doid=3274247.3274504
2. Andy Project—Home. https://andy-project.eu/
3. Caputo F, Greco A, D’Amato E, Notaro I, Spada S (2019) IMU-based motion capture wearable system for ergonomic assessment in industrial environment. In: Ahram TZ (ed) Advances in human factors in wearable technologies and game design, advances in intelligent systems and computing. Springer International Publishing, Berlin, pp 215–225
4. Caserman P, Garcia-Agundez A, Konrad R, Göbel S, Steinmetz R (2018) Real-time body tracking in virtual reality using a Vive tracker. Virtual Real. https://doi.org/10.1007/s10055-018-0374-z
5. Clark MW (1976) Some methods for statistical analysis of multimodal distributions and their application to grain-size data. J Int Assoc Math Geol 8(3):267–282. https://doi.org/10.1007/BF01029273
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献