The design of soft robots is still commonly driven by manual trial-and-error approaches, requiring the manufacturing of multiple physical prototypes, which in the end, is time-consuming and requires significant expertise. To reduce the number of manual interventions in this process, topology optimization can be used to assist the design process. The design is then guided by simulations and numerous prototypes can be tested in simulation rather than being evaluated through laborious experiments. To implement this simulation-driven design process, the possible design space of a slender soft pneumatic actuator is generalized to the design of the circular cross-section. We perform a black-box topology optimization using genetic algorithms to obtain a cross-sectional design of a soft pneumatic actuator that is capable of reaching a target workspace defined by the end-effector positions at different pressure values. This design method is evaluated for three different case studies and target workspaces, which were either randomly generated or specified by the operator of the design assistant. The black-box topology optimization based on genetic algorithms proves to be capable of finding good designs under given plausible target workspaces. We considered a simplified simulation model to verify the efficacy of the employed method. An experimental validation has not yet been performed. It can be concluded that the employed black-box topology optimization can assist in the design process for slender soft pneumatic actuators. It supports at searching for possible design prototypes that reach points specified by corresponding actuation pressures. This helps reduce the trial-and-error driven iterative manual design process and enables the operator to focus on prototypes that already offer a good viable solution.

翻译：软体机器人的设计目前仍普遍依赖于人工试错方法，需要制造多个物理样机，最终导致过程耗时且对专业知识要求较高。为减少该过程中的人工干预，可采用拓扑优化辅助设计流程。设计过程将由仿真驱动，大量样机可在仿真环境中测试，而无需通过繁复的实验进行评估。为实现这一仿真驱动设计流程，我们将细长软体气动执行器的可能设计空间泛化为圆形截面设计。采用基于遗传算法的黑盒拓扑优化方法，获得能够达到目标工作空间的软体气动执行器截面设计，该工作空间由不同压力值下的末端执行器位置定义。本设计方法通过三个不同案例研究与目标工作空间进行评估，这些目标空间或随机生成，或由设计辅助系统操作者指定。基于遗传算法的黑盒拓扑优化方法被证明能够在给定合理目标工作空间下找到优良设计方案。我们采用简化仿真模型验证所提方法的有效性，实验验证尚未进行。可以得出结论：所采用的黑盒拓扑优化方法能够辅助细长软体气动执行器的设计流程，支持探索能够达到特定驱动压力对应位置点的可能设计原型。这有助于减少试错驱动的迭代式人工设计过程，使操作者能够聚焦于已具备良好可行性的原型方案。