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Kevin Wu

Development of an Origami-Inspired Bistable Soft Robotic Gripper
The increasing demand for high-quality, delicate agricultural products like fruits and vegetables requires innovative harvesting solutions due to the limitations of manual labor. Traditional robotic grippers often fail to handle these items gently, causing bruise damage. This poster presents an origami-inspired bistable soft robotic gripper designed to address this challenge by providing a gentle and adaptive grasp of soft fruits – peaches. Utilizing a bistable mechanism, the gripper can remain in two stable states without continuous power input, automatically closing when contacting an object, thus reducing the need for active control. The gripper’s design includes parameters such as valley width, height, and wall thickness to optimize performance. Through a series of tests and simulations using SolidWorks and Abaqus CAE, the gripper demonstrated the ability to handle peaches without causing bruising. The final design, featuring a combination of rigid and soft materials for the fingers and a strategically positioned elastic ring, ensures efficient and damage-free fruit handling. This innovative approach not only enhances the efficiency of automated agricultural operations but also contributes to the production of higher-quality produce. Future work will focus on integrating the gripper components into a single part and further optimizing gripping parameters.

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Quinn Zabiegalski

Optimizing Boundary Transformation Vectors by
Choosing the Best 2-D Representation

Boundary transformation vectors (BTVs) are a method tailored for analyzing damage in linear and nonlinear dynamic systems. BTVs are constructed by first comparing two system attractors-one representing the system with normal parameters and the other with altered parameters. A system attractor represents the long-term behavior that a dynamic system settles into after transient behavior has dissipated. Utilizing a 2-D representation of the system attractors, BTVs can be created by drawing vectors between the boundaries of the normal and altered attractors to illustrate system changes. The main objectives of this research were to understand the sensitivity of BTVs to different 2D attractor representations and determine an optimal representation method if given a set of data. Initially, literature regarding system attractors and their representations were reviewed. MATLAB was then used to code the four most promising representations for the system attractors, these being plane projections, Poincaré sections, principal component analysis (PCA), and singular value decomposition (SVD). The BTVs were constructed using simulated Lorenz attractor data. After successful implementation of the different representations, two comparison groups were formed. The first group involved altering the rho parameter (ρ) of the Lorenz system from its normal value (ρ = 28) to 27, 26, and 25. The second group used intermediate rho values (27.5, 26.5, and 25.5). Finally, MATLAB functions were used to evaluate the magnitude and direction of the resulting BTVs. Among the methods tested, Poincaré sections yielded the most consistent and effective BTV results, while PCA appeared least effective.

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