Automatic synthesis of arthrokinematically compatible exoskeletons. A case study on its application on a shoulder occupational exoskeleton. (March 2021)

• Record Type: Journal Article
• Authors: Rossini, Marco; Geeroms, Joost; Lefeber, Dirk; Rodriguez-Guerrero, Carlos
• Is Part Of: Mechanism and machine theory. Volume 157(2021)
• Publication Date: 2021-03
• Subjects: Wearable robots -- Occupational shoulder exoskeleton -- Evolutionary morphology -- Graph theory; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2020.104186
• Languages: English
• ISSNs: 0094-114X

Abstract

Highlights: The kinematic synthesis of a new fully compatible shoulder exoskeleton is presented. A new automatic synthesis method has been developed to improve the design process. Spatial kinematic chains compatible with the anatomy of the shoulder are enumerated. Abstract: 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.