Toward Long-Lasting Large-Scale Soft Robots
The Durability Challenge in Architectured Materials
Francesco Stella (École Polytechnique Fédérale de Lausanne, TU Delft - Learning & Autonomous Control)
Guanran Pei (École Polytechnique Fédérale de Lausanne, Technische Universität München)
Omar Meebed (École Polytechnique Fédérale de Lausanne)
Qinghua Guan (École Polytechnique Fédérale de Lausanne)
Zhenshan Bing (École Polytechnique Fédérale de Lausanne)
Cosimo Della Santina (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Learning & Autonomous Control)
Josie Hughes (École Polytechnique Fédérale de Lausanne)
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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.