Soft robot arms have made significant progress towards completing human-scale tasks, but designing arms for tasks with specific load and workspace requirements remains difficult. A key challenge is the lack of model-based design tools, forcing advancement to occur through empirical iteration and observation. Existing models are focused on control and rely on parameter fits, which means they cannot provide general conclusions about the mapping between design and performance or the influence of factors outside the fitting data. As a first step toward model-based design tools, we introduce a novel method of analyzing whether a proposed arm design can complete desired tasks. Our method is informative, interpretable, and fast; it provides novel metrics for quantifying a proposed arm design's ability to perform a task, it yields a graphical interpretation of performance through segment forces, and computing it is over 80x faster than optimization based methods. Our formulation focuses on antagonistic, pneumatically-driven soft arms. We demonstrate our approach through example analysis, and also through consideration of antagonistic vs non-antagonistic designs. Our method enables fast, direct and task-specific comparison of these two architectures, and provides a new visualization of the comparative mechanics. While only a first step, the proposed approach will support advancement of model-based design tools, leading to highly capable soft arms.

翻译：软体机器人手臂在执行人体尺度任务方面已取得显著进展，但针对特定负载与工作空间要求的任务进行手臂设计仍具挑战性。关键难题在于缺乏基于模型的设计工具，迫使研发只能通过经验迭代与观察推进。现有模型主要面向控制且依赖参数拟合，这意味着它们无法提供关于设计与性能映射关系或拟合数据外因素影响的普适性结论。作为迈向基于模型设计工具的第一步，我们提出一种创新方法用于分析拟议手臂设计能否完成预期任务。该方法具备信息性、可解释性与快速性：它提供量化拟议手臂设计任务执行能力的新指标，通过分段力生成性能的图形化解读，且计算速度比基于优化的方法快80倍以上。我们的公式化方法聚焦于拮抗式气动软体手臂。我们通过示例分析及拮抗式与非拮抗式设计的对比论证本方法的有效性。该方法支持对两种架构进行快速、直接且任务针对性的比较，并提供对比力学特性的全新可视化呈现。尽管仅是初步探索，所提方法将推动基于模型设计工具的发展，最终催生高性能软体手臂。