  Robotic exoskeletons can enhance human strength and aid people with physical
disabilities. However, designing them to ensure safety and optimal performance
presents significant challenges. Developing exoskeletons should incorporate
specific optimization algorithms to find the best design. This study
investigates the potential of Evolutionary Computation (EC) methods in robotic
design optimization, with an underactuated hand exoskeleton (U-HEx) used as a
case study. We propose improving the performance and usability of the U-HEx
design, which was initially optimized using a naive brute-force approach, by
integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch
Algorithm. Comparative analysis revealed that EC methods consistently yield
more precise and optimal solutions than brute force in a significantly shorter
time. This allowed us to improve the optimization by increasing the number of
variables in the design, which was impossible with naive methods. The results
show significant improvements in terms of the torque magnitude the device
transfers to the user, enhancing its efficiency. These findings underline the
importance of performing proper optimization while designing exoskeletons, as
well as providing a significant improvement to this specific robotic design.
