Merve Acer Kalafat

This paper presents a systematic methodology for the development of resistive force transducers (RFTs), tailored for robotics, particularly for applications in assistive technologies such as exoskeletons. RFTs are sensors that alter their electrical resistance in response to applied force, pressure, or mechanical stress. Their adjustability, flexible design, cost-effectiveness, and ability to be easily positioned at contact points without compromising user comfort make them ideal for mapping pressure distributions over defined and limited areas. To this end, the current study aims to leverage screen printing, a scalable and cost-efficient manufacturing technique—to enable high-precision customization of sensor structures and conductive inks with tailored properties. The fabricated RFTs were integrated into Co-Ex, a bipedal exoskeleton equipped with a lower-body framework designed to assist individuals with mobility impairments. The performance of the custom RFTs was rigorously evaluated in realistic testing scenarios and benchmarked against commercially available RFTs of comparable size and specifications. Key performance metrics, including center of pressure (CoP) measurements, demonstrated that the custom RFTs exhibited performance comparable to their commercial counterparts, with CoP measurements showing strong agreement. This work highlights the potential of custom RFTs as cost-effective, flexible solutions for advanced robotics and assistive technologies.

This paper optimizes the geometry of a novel Delta mechanism via origami-inspired fabrication. The aim is to maximize workspace volume without increasing the mechanism’s footprint. Optimize the geometry to increase the limitation of the mechanism and experimentally test the optimized mechanism and compare the results with the original one. The Delta mechanism’s novelty altered fabrication and design parameters. A genetic algorithm, a common optimization method, was applied. Finite element analysis examined the impact of elastic joints. Experiments compared the kinematic model’s workspace trajectory between optimized and initial mechanisms. The paper reports a 35% increase in workspace volume over the initial mechanism while maintaining a constant footprint size. The optimized mechanism achieves sub-0.5 mm root mean square (RMS) accuracy in the X , Y , and Z directions, ensuring precise positioning within the calculated workspace using rigid kinematics. Despite the expanded workspace, the accuracy of reaching boundary points remains nearly constant compared to the initial model. This study covers only a specific type of parallel mechanism fabricated by the origami-inspired method. Therefore, researchers are encouraged to test and optimize different mechanisms with higher degree of freedom further. The paper discusses implications for advancing flexible mechanisms especially origami-inspired mechanisms, a widely studied type in recent research. Employing optimization techniques can enhance accuracy, reliability, and cost-effectiveness in the fabrication process for other types of mechanisms. This paper addresses the acknowledged requirement to delve into origami-inspired mechanisms, featuring distinctive design parameters and fabrication methods when compared to conventional mechanisms. The significance of this inquiry is heightened by the existing dearth of design optimization in flexible parallel mechanisms.

Robogami structures are employed for many purposes, including miniature parallel manipulators. Parallel manipulators require accurate joint position feedback, underlining the open problem in robogami structures for providing sophisticated feedback with 2D, fully embeddable, down-scalable, flexible sensors. Existing studies include only on-off control applications, excluding any parallel mechanism implementations and continuous proportional control signals. We implement silver inkjet printed angle sensors for the first time into a robogami structure and use this class of sensors for control. For the first time, a parallel manipulator is controlled with a 2D sensor, and printed sensors are tested for continuous control. Performances are analyzed with set-point and tracking control tasks using a paper Delta mechanism, compared with on-off control and encoder feedback control cases for better evaluation. A detailed comparison with the existing studies both for robogami parallel mechanisms and other sensor-embedded flexible hinges is provided. Encoder-like control performances are achieved in a wide range of the joint space and down to ~1 mm. RMS task space accuracy is recorded. Sources and potential improvements for the less accurate regions are stated. We conclude that printed sensors are promising for parallel mechanisms and continuous signal control applications.

An origami-based parallel mechanism is an excellent solution for various applications where small-scale, low profile and foldability are needed. These mechanisms are composed of rigid and flexible layers designed according to layer-by-layer fabrication methods. In addition, it becomes important to design functional layers that provide user feedback. Here, the design and fabrication of an origami-based 3 Degree-of-Freedom (DoF) Delta mechanism, which has the same traditional kinematics as a Delta mechanism, are presented. A sensor layer was designed composed of 3 strain gauges to measure the angular position of the actuated arm of the mechanism. The strain-gauge patterns were printed on a special Polyethylene terephthalate (PET) using Silver nanoparticle ink with a commercial desktop printer. The integration of these sensors has been studied by placing them in different locations between rigid layers. The sensors' outputs were presented when subjected to step and sinusoidal inputs of the actuated arm. The experiment results show that the developed sensor layer can track the angular position changes of the actuated lower arm, which is a promising result to be used in a control loop in the feature.

Contact localization is an essential feature of tactile sensors for biomedical, rehabilitation, surgical, and service robot applications where interaction with the environment is needed. In this work, we have developed a piezoelectric (PZT) based soft tactile sensor using the layer-by-layer fabrication technique. We placed $4\times 4$ mm2 size PZT elements in $3\times 3$ matrix form with 3 mm spaces and embedded them in silicone layers. Although the sensor has discrete brittle sensing elements, the silicone layer distributes the contact force on the surface area, which enables continuous contact localization on the sensor. Here, we studied the estimation using the collected and conditioned output voltage signals of PZT elements. First, we used a logic-based algorithm that gives results qualitatively using a developed user interface. Then we used machine learning algorithms to estimate the contact position coordinates numerically. Finally, we applied Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) algorithms and compared the results. We got our best results using a two-layer ANN algorithm which provides a mean deviation of 0.08 mm for the X-axis and 0.07 mm for the Y-axis. Besides, we had 97.20% of testing estimations with an error lower than 0.35 mm. Therefore, we believe our tactile sensor gives promising results and can be used in small-scale applications where contact localization estimation is desired.