DTA

Dottorato

PAGLIARANI, NICCOLO

etd-12302024-202259

Delicate yet effective grasping: soft robotics from lab to field

ING-IND/34

Istituto di Biorobotica - PHD IN BIOROBOTICA

relatore Prof. CIANCHETTI, MATTEO
Presidente Prof. Robert Howe
Membro Prof. Josie Hughes

• agrifood
• jamming-based systems
• soft grippers
• soft robotics
• soft sensing
• vine robots

28/04/2025;

parziale

Soft robotics harnesses the inherent flexibility and adaptability of soft materials to enable safer interaction with delicate objects and complex environments—including healthcare, agriculture, and confined spaces. While soft grippers have already demonstrated their ability to handle fragile items, a persistent challenge is extending their functionality beyond controlled laboratory settings to real-world applications such as produce harvesting and operation in tight spaces.<br>This thesis addresses that challenge by enriching grippers performance with tunable stiffness through jamming-transition systems, allowing them to switch between stiff and soft states. Such adaptability not only enables the modulation of gripping forces but also provides variable kinematics, allowing the soft actuator to handle objects of diverse shapes and sizes. The design process strategically incorporates intelligence into the robot’s physical form through careful selection of materials, geometric configurations, and structural patterns that inherently guide interactions with the environment. This work introduces a diverse portfolio of gripper designs, balancing compliance and robustness through finger-based and monolithic architectures tailored for various harvesting scenarios. Looking beyond agriculture, this work also explores the deployment of soft robotics in confined spaces, featuring “growing robots” capable of active manipulation. Finally, the integration of proprioceptive and exteroceptive sensors refines the gripping process without compromising the robots’ flexibility. By integrating tunable stiffness, adaptive morphologies, and embedded sensing, this thesis establishes a foundation for soft robotic grippers with enhanced embodied intelligence, enabling them to adapt dynamically to diverse tasks and environments.

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