Toward Long-Lasting Large-Scale Soft Robots

Abstract

Soft robots promise groundbreaking advancements across various industries. However, soft robots are susceptible to wear, fatigue, and material degradation. Their durability and long-term reliability are often overlooked, despite being critical for the successful deployment of these systems in real-world applications. This article contributes to solving this challenge by identifying metrics that reflect material wear, mechanical hysteresis, and drift occurring during long-term operations in soft architectured materials. While this same pipeline can be generalized to different soft robots, we test these metrics on the trimmed helicoid architectured materials, and we validate the improvement in performance on the Helix soft manipulator. Thanks to the proposed metrics, we demonstrate a 75% reduction in repeatability errors over long-duration experiments.