4.7 Article

Automatic synthesis of arthrokinematically compatible exoskeletons. A case study on its application on a shoulder occupational exoskeleton

期刊

MECHANISM AND MACHINE THEORY
卷 157, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechmachtheory.2020.104186

关键词

Wearable robots; Occupational shoulder exoskeleton; Evolutionary morphology; Graph theory

资金

  1. Research Foundation - Flanders (FWO)

向作者/读者索取更多资源

Synthesizing kinematically compatible structures for human anatomy and complexity is challenging, often resulting in sub-optimal solutions due to the large solution space. Existing methods require simplifications to reduce the number of solutions, limiting their usefulness for designers. A new method presented in this work overcomes these limitations by using graph and linear transformation theory, expanding applicability to 3D space and reducing symbolic calculations by 99%.
The synthesis of kinematic structures compatible with the human anatomy and complexity is not a straightforward problem. The resulting solution space has a large amount of possible combinations and finding one that is suitable, usually comes down to the designer's expertise, often leading to sub-optimal solutions. Automatic synthesis of wearable devices is a field that can help on providing the designer with an atlas of solutions to greatly simplify and improve this process. However, existing methods up to now, often require unrealistic simplifications to reduce the number of solutions produced by the machine, to a reasonable amount that can be useful for the designer. The objective of this work is to present a new method for the type synthesis of arthrokinematically compatible exoskeletons, that uses graph and linear transformation theory to overcome the limitations of the existing methods, expanding their applicability to the 3D space. Spatial kinematically compatible shoulder exoskeletons have been generated with an algorithm that uses 99% less symbolic calculations in comparison with the state of the art, reducing the complexity of the process and enabling its use in real case scenarios. (C) 2020 Elsevier Ltd. All rights reserved.

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