# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 3 Simulation Model for Object Pick-Point Evaluation  
Determining pick-points with simulations is a complex task that requires an accurate description of the robotic order-picking system (a robotic gripper and a gripped object) and all possible physical relationships between the objects (contact forces, friction, etc.). It is necessary to evaluate the influencing parameters and ensure that the system response is as close as possible in simulations compared to a physical system.  
The developed simulation model for pick-point evaluation consists of three main parts: (1) a model of a two-fingered robotic gripper, (2) the model of an object to be gripped, and (3) control functions that permit gripper translation and rotation and vertical movement of the gripper and enable the force control of the gripper closing. For this purpose, it was necessary to select a simulation tool that would allow the automatic analysis of arbitrary pick-points, considering the kinematic and dynamic properties of the objects, and would also allow the graphical presentation of the results through animations. Therefore, the MSC ADAMS (Automatic Dynamic Analysis of Mechanical Systems) software package, designed for multibody analysis, was selected for our research study. ADAMS was used to simulate various mechanical systems, enabling verification of forces generated by the interactions between elements while allowing the results to be presented graphically in the form of an animation. The software was selected due to its ability to integrate with the MATLAB/Simulink simulation environment.  
Figure 1 schematically presents the simulation model components. The ADAMS/View contains the model of the robotic gripper, gripped object, and various measures for determining forces and other influential parameters. It also serves as a tool for exporting the model for the ADAMS/MATLAB cosimulation. MATLAB/Simulink contains a force controller for closing the robotic gripper fingers and several support and analysis functions. Additional scripts are written in MATLAB, which work as various support functions and enable simulations to be executed parallelly. Lastly, MATLAB/App Designer was used to provide the user with a graphical interface, which enables various parameters settings and analysis of selected pick-points.  
A few assumptions were made to reduce the overall complexity of the simulation model: (1) the object pick height is always half of the object depth, (2) rotation of the object is permitted only around the (\(Y\)) axis (in the negative direction of gravitational force), (3) only rigid objects and rigid robotic fingers were considered.  
Despite those assumptions, the model still qualifies as applicable to a number of real-world bin-picking problems. The user must first input a 2D top-down image of the analyzed object to evaluate selected object pick-points. The best practice is to use a snapshot of the 3D model with a clear distinction between the background and the model since several calculations (generation of pick-points, collision checks, etc.) rely on the correct image segmentation process. Further, the user must specify the input parameters of the selected 2-F robotic gripper (finger width, finger depth, stroke width, etc.) for the model to function correctly. Parameters for the P force controller must be set beforehand, although the user can fine-tune them if needed. Next, the number of pick-points must be assigned if the \"Normal\" simulation mode is selected. Otherwise, the maximum number of points is set if the simulation mode is set to \"Optimization.\" In this mode, the user must also specify the successful grasp quality metric (see Section 4.2. for more details) by which the pick-points are considered valid. Pick-points are then generated dynamically, and simulations are run until the selected number of successful pick-points is generated.