Enhanced Design of a Hand Exoskeleton: Balancing Force Transmission and Actuation with Evolutionary Algorithms

Abstract

Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study addresses significant challenges of ensuring safety and maximizing performance in the design process for an underactuated hand exoskeleton intended for physical rehabilitation. We first implemented a single objective optimization problem by maximizing force transmission from the actuator to the finger joints, then expanded into multi-objective optimization by also minimizing the variance of torques rendered on the finger joints and the actuator displacement needed. The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Our simulation results and statistical analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process - at the cost of complicating the optimization algorithm and computational burden. These findings underline the critical importance of performing proper optimization techniques while designing exoskeletons, as well as providing a significant improvement to this specific robotic design that could provide more effective rehabilitation therapies and augmented human-robot interactions.