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.

翻译：机器人外骨骼能够增强人类力量并辅助身体残疾人士。然而，设计兼具安全性与最优性能的外骨骼面临重大挑战。开发外骨骼需融入特定优化算法以寻找最优设计。本研究以欠驱动手部外骨骼（U-HEx）为案例，探讨演化计算方法在机器人设计优化中的潜力。我们通过整合遗传算法与大爆炸-大收缩算法等演化计算技术，改进最初采用朴素暴力法优化的U-HEx设计的性能与实用性。对比分析表明，演化计算方法在显著缩短时间的同时，始终能比暴力法产生更精确、更优的解决方案。这使得我们能够通过增加设计变量数量来改进优化过程，而该方法在朴素方法中难以实现。结果表明，该装置向用户传递的扭矩幅值获得显著改善，效率得到提升。这些发现凸显了在外骨骼设计中进行恰当优化的重要性，同时为此特定机器人设计带来了重大改进。