用可调硬度的仿生水果模型，解决机器人采摘训练难的问题。
Robotic Fruits with Tunable Stiffness and Sensing: Towards a Methodology for Developing Realistic Physical Twins of Fruits
- 设计可调硬度的气动纤维增强仿生猕猴桃，模拟不同成熟度果实。
- 硬度调节精度达97.35%~99.43%，50次应力测试误差仅0.56%~1.10%。
- 适合用于机器人夹持器训练与评估，减少真实水果浪费。
全球农业食品行业面临劳动力短缺、消费需求高涨及供应链中断等问题，导致大量未收获农产品损失。机器人采摘成为潜在解决方案，但因天然果实机械性能高度变异，难以对软性夹持器进行有效评估与训练，现有测试依赖大量真实果实，效率低、成本高且产生浪费。本文提出一种新方法，开发可调软性物理孪生体，模拟不同成熟度果实的硬度特性。基于气动纤维增强结构，构建了猕猴桃物理孪生体，实验表明其硬度可在多轮测试中实现精准调控（准确率97.35%–99.43%）。使用商用机器人夹持器进行抓取测试，证明孪生体传感器能反映施加的夹持力。经过50次应力循环测试，其硬度保持稳定，误差为0.56%–1.10%。结果表明，该物理孪生体可动态模拟真实果实的力学特性，为机器人夹持器提供可持续、可控的基准测试与训练平台。
原文摘要 · Abstract (English)
The global agri-food sector faces increasing challenges from labour shortages, high consumer demand, and supply-chain disruptions, resulting in substantial losses of unharvested produce. Robotic harvesting has emerged as a promising alternative; however, evaluating and training soft grippers for delicate fruits remains difficult due to the highly variable mechanical properties of natural produce. This makes it difficult to establish reliable benchmarks or data-driven control strategies. Existing testing practices rely on large quantities of real fruit to capture this variability, leading to inefficiency, higher costs, and waste. The methodology presented in this work aims to address these limitations by developing tunable soft physical twins that emulate the stiffness characteristics of real fruits at different ripeness levels. A fiber-reinforced pneumatic physical twin of a kiwi fruit was designed and fabricated to replicate the stiffness at different ripeness levels. Experimental results show that the stiffness of the physical twin can be tuned accurately over multiple trials (97.35 - 99.43% accuracy). Gripping tasks with a commercial robotic gripper showed that sensor feedback from the physical twin can reflect the applied gripping forces. Finally, a stress test was performed over 50 cycles showed reliable maintenance of desired stiffness (0.56 - 1.10% error). This work shows promise that robotic physical twins could adjust their stiffness to resemble that of real fruits. This can provide a sustainable, controllable platform for benchmarking and training robotic grippers.
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