DOI: 10.5061/dryad.44j0zpcqd
title: Data for: MOGrip: Gripper for multi-object grasping in pick-and-place tasks using translational movements of fingers

description: Humans utilize their dexterous fingers and adaptable palms in various
 multi-object grasping strategies to efficiently move multiple objects
 together in various situations. Advanced manipulation skills, such as
 finger-to-palm translation and palm-to-finger translation, enhance
 dexterity in multi-object grasping. These translational movements allow
 the fingers to transfer the grasped objects to the palm for storage,
 enabling the fingers to freely perform various pick-and-place tasks while
 the palm stores multiple objects. However, conventional grippers, although
 able to handle multiple objects simultaneously, lack this integrated
 functionality, which combines the palm's storage with the
 fingers' precise placement. Here, we introduce a gripper for
 multi-object grasping that applies translational movements of fingertips
 to leverage the synergistic use of fingers and the palm for enhanced
 pick-and-place functionality. The proposed gripper consists of four
 fingers and an adaptive conveyor palm. The fingers sequentially grasp and
 transfer objects to the palm, where the objects are stored simultaneously,
 allowing the gripper to move multiple objects at once. Furthermore, by
 reversing this process, the fingers retrieve the stored objects and place
 them one by one in the desired position and orientation. A finger design
 for simple object translating and a palm design for simultaneous object
 storing are proposed and validated. In addition, the time efficiency and
 pick-and-place capabilities of the developed gripper were demonstrated.
 Our work shows the potential of finger translation to enhance
 functionality and broaden the applicability of multi-object grasping.
description: <strong>Experiments</strong> To
 validate the motion of the decoupling design, the grasping tendon was
 pulled with a motor (100:1 Micro Metal Gearmotor HPCB 6V, Pololu). The
 movement of five different decoupling linkages was recorded by a camera
 with 30 fps, and the positions of the three red markers of each frame were
 tracked through MATLAB (MathWorks). The MATLAB function “imfindcircle” was
 utilized to find the red markers, and through the positions of these three
 markers, the rotation angle and translation distance of the finger were
 calculated. Experiments for each decoupling link design were repeated five
 times. The storing force along the y-axis was measured
 by a tensile testing machine (INSTRON 5948 Microtester). In the experiment
 measuring the storing force for a single object, to ensure repeatability
 in the process of inserting the object into the storage, the storage was
 divided in half and placed on both sides of the rail. Then, the target was
 placed between the storages, and stored by moving both storages to the
 center (figs. S4B and S4C). Each stored target was pulled 5 times through
 a tensile testing machine at a speed of 30 mm/min, and the maximum pulling
 force was measured as the storing force (fig. S4D). The storing force was
 also analyzed using finite element (FE) simulation (fig. S4E).
 Similarly, for the experiment on the simultaneous storing
 capability of two objects, the settings from the previous experiment
 (Figs. S4B and S4C) were utilized to ensure the reliability of the
 experimental setup. The storage was divided into three parts as shown in
 Fig. S8A. The two objects were placed on both sides of the middle storage
 (highlighted in green), and the left and right storages (highlighted in
 orange) were moved to the center to store both objects. The black cylinder
 was pulled 5 times for each experiment at a speed of 30 mm/min through a
 tensile testing machine (INSTRON 5948 Microtester), and the maximum
 pulling force was measured as the storing force (Fig. S8B).
 The storing force along the x-axis was measured by a load cell
 (KTOYO 333FDX) while pulling the objects using a linear motor (Actuonix
 P16-150-256-12-P) at a speed of 4.2 mm/s (Fig. S5). For repeated
 experiments, a weight block was hung to apply force to insert the
 cylindrical object into the storage while rotating the belt. When
 calculating the storing force, the weight of the block was subtracted from
 the measurement obtained by the load cell. All experiments for each
 parameter were repeated five times. In the experiment
 measuring the placement error of the multi-object grasping sequence, the
 configuration of the object before grasping and after placement was
 captured using a camera (ABKO APC900). Blue markers were placed at the
 center of mass of the object and at a point 30 mm along the length from
 that center. The position changes of the markers were measured using
 OpenCV. In the storing process of the multi-object grasping sequence, the
 distance at which each object was stored from the entrance of the storage
 was set to 30 mm.
 <strong>Simulation</strong> Finite
 element analysis (FEA) was conducted using the FEA software ABAQUS (ABAQUS
 2023, Dassault systems). All simulation conditions were set to be
 identical to the experimental conditions. The belt surfaces opposite to
 the hair were set as a fixed boundary condition, and the gravitational
 force applied to the hair was also considered (fig. S4E, i). In addition,
 by dividing the simulation steps, the object was stored in the storage by
 pushing the hairs (fig. S4E, ii), and after the storing process was
 finished, the maximum pulling force was obtained by pulling the object
 (fig. S4E, iii). The objects were considered as a rigid body, and all
 contact between the object and hairs and the hairs with each other was
 considered as “general contact condition.” Since dynamic motion occurs as
 the hair is pushed, static analysis is challenging; therefore, the
 simulation was conducted through dynamic analysis. For the quasi-static
 assumption, both mass scaling and time period were set to 1, and the
 kinetic energy of the hair belt was less than 3% of the internal energy at
 every step until the maximum storing force was measured.
description: # Data for: MOGrip: Gripper for multi-object grasping in pick-and-place
 tasks using translational movements of fingers
 [https://doi.org/10.5061/dryad.44j0zpcqd](https://doi.org/10.5061/dryad.44j0zpcqd) ## Description of the data and file structure **Overview** This dataset contains the experimental data, modeling, and simulation model for analyzing the proposed gripper that can be applied to multi-object grasping in various pick-and-place tasks by using translational movements of fingers. The presented dataset is necessary for generating figures, models, and results for the paper titled "MOGrip: Gripper for Multi-Object Grasping in Pick-and-Place Tasks Using Translational Movements of Fingers". ### Files and variables #### File: ABAQUS\_Simulation.zip **Description:**  INP files for running ABAQUS simulations with varying design parameters of the conveyor palm are provided. Although we intended to include all ODB files containing the simulation results, each file is approximately 500MB, totaling over 15GB. Therefore, the essential information is instead provided in a CSV file (storing force data). #### File: Data\_from\_the\_figures.zip **Description:**  All experimental results discussed in this paper, including supplementary materials, are compiled in the dataset. Experimental data corresponding to the figures in the article are organized in folders named after each figure. Each folder contains a summary CSV file summarizing the results and detailed CSV files representing the raw data. When the raw data is too large, additional subfolders have been created for the organization. Below is a description of each folder: **"Fig. 3G and I"** This folder contains data measuring the change in grasping angle when pulling the translating tendon, depending on the design of the decoupling link. The design parameters for each decoupling link are specified in Table S1 of the paper and the first row of each CSV file. The file "Modeling_Results.csv" provides the results of the analytic modeling. **"Fig. 4C and D"** This folder includes data measuring the y-axis storing force of the conveyor's palm varying the design parameters (hair radius r and distance between adjacent hairs b) and the target object radius R. The "Raw_Data_Fig. 4C and D" folder contains force data collected using a tensile machine to pull objects stored in the conveyor's palm. Outside the raw data folder, the "Summary.csv" file summarizes all experimental results, while individual CSV files organize data for each conveyor palm design. For example, "r1.5_b20.csv" corresponds to a conveyor palm with a hair radius of 1.5 mm and an adjacent hair distance of 20 mm. **"Fig. 4E and F"** Similar to "Fig. 4C and D," this folder organizes data measuring the x-axis storing force of the conveyor palm. The "Raw_Data_Fig. 4E and F" folder contains force data measured using a linear motor and a load cell. Outside the raw data folder, "Summary.csv" summarizes all experimental results, and individual CSV files organize data by conveyor palm design. For example, "r1.5_b20.csv" represents a conveyor palm with a hair radius of 1.5 mm and an adjacent hair distance of 20 mm. **"Fig. 5B"** As shown in Supplemental Movie S2, this folder contains data comparing the path differences between the developed gripper and a single-object gripper in logistic demonstrations. The "Summary.csv" file lists the calculated path lengths and process times, while "Detailed Data.csv" specifies the points in the task space traversed by the robotic arm, which was used to calculate the manipulator's travel distance. **"Fig. S3E"** This folder contains data measuring grasping forces varying object radius. "Summary.csv" summarizes the experimental data, and the remaining files provide raw data from five measurements per object. **"Fig. S8"** This folder includes data on storing forces measured during the simultaneous storage of multiple objects. Each case was measured five times, and the results are compiled in "Fig. S8.csv." **"Fig. S10D to G"** This folder contains data on the success rates and placement errors of multi-object grasping sequences measured across varying object types and sizes. The raw data folder includes captures of each experiment, with author faces edited out when visible. Python code is provided for detecting blue markers in the captures and performing location comparisons. Outside the raw data folder, "Summary.csv" provides a comprehensive summary of the success rates and placement errors for each object. **"Fig. S11D to F"** This folder contains data measuring the success rates and placement errors of multi-object grasping sequences based on off-center distances. Similar to "Fig. S10D to G," raw data folders include experimental captures, and Python code is provided for analyzing blue marker positions. The "Summary.csv" file summarizes the success rates and placement errors. **"Fig. S13D"** This folder contains data measuring retrieval offsets varying object weight and gripper tilt angle. "Raw Data.csv" provides five repeated measurements for each case, while "Summary.csv" includes a summary of the experimental data and analytic modeling results. **"Graph_Origin"** This folder includes Origin files used to generate the graphs in the paper. **"ABAQUS_Data_Fig. 4C and D"** This folder provides simulation data on the y-axis storing force of the conveyor palm. **etc** All modeling covered in this paper is provided in MATLAB code.