Robotic Fruits with Tunable Stiffness and Sensing: Towards a Methodology for Developing Realistic Physical Twins of Fruits
🤖 AI Summary
This study addresses the lack of reliable, repeatable, and sustainable testing platforms for soft robotic grippers in delicate fruit harvesting, where the high variability in real fruit mechanical properties leads to costly and wasteful benchmarking. To overcome this limitation, the authors propose a physically embodied fruit twin with tunable stiffness and integrated force sensing, based on a fiber-reinforced pneumatic soft actuator. The biomimetic design accurately replicates the mechanical behavior of fruits at varying ripeness levels. The platform achieves unprecedented precision in stiffness modulation (97.35%–99.43% tuning accuracy) and provides real-time grasping force feedback. Over 50 consecutive cycles, it demonstrates exceptional stability, with stiffness drift limited to only 0.56%–1.10%, thereby significantly surpassing the constraints of conventional testing approaches reliant on actual fruits.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
robotic harvesting
soft grippers
fruit mechanical properties
benchmarking
physical twins
Innovation
Methods, ideas, or system contributions that make the work stand out.
tunable stiffness
soft robotic twin
fruit harvesting robotics
pneumatic actuation
physical digital twin
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