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.

利用进化计算方法对机器人外骨骼设计进行优化，特别是在优化下驱动式手部外骨骼 (U-HEx) 的性能和可用性方面，结果显示进化计算方法在较短时间内持续提供精确和最佳解决方案，显著提高了转移给用户的扭矩大小和设备效率。