A Fast and Model Based Approach for Evaluating Task-Competence of Antagonistic Continuum Arms

TL;DR

This paper introduces a fast, interpretable, model-based method for evaluating whether antagonistic continuum arms can complete specific tasks, significantly improving design efficiency over traditional optimization techniques.

Contribution

It presents a novel, rapid analysis method with new performance metrics and visualizations for soft arm design, advancing model-based design tools for soft robotics.

Findings

Method is over 80x faster than optimization-based approaches

Provides graphical interpretation of segment forces

Enables direct comparison of antagonistic and non-antagonistic designs

Abstract

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.