# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 1 Introduction
However, our approach does not focus on a state-of-the-art IPC model but on improving existing approaches, showing possible simulation capabilities. Therefore, ADAMS 2022.1 simulation software was chosen as the primary physics simulation tool, coupled with the advanced control functionalities provided by MATLAB 2020b and MATLAB/Simulink. [PERSON] et al. [39] noted that the ADAMS system software is closely related to simulators, such as GraspIt!, Open-Rave, and OpenGRASP. To provide a robotic bin-picking integrator with a tool, which would enable one to systematically determine object pick-points for various objects in this paper, we explore the below research questions:  
RQ1: Can the ADAMS/MATLAB cosimulation be used to determine the systematic grasp quality for the selected 2-F parallel robotic gripper? RQ2: Can the simulation model also be used to evaluate 2-F robotic grippers or even develop new variants of the 2-F grippers? RQ3: How does a combination of the analytical and empirical approach of grasp quality evaluation compared to \"deep-learning\" model-based approaches influence the overall system performance? A novel simulation model for determining pick-points for a 2-F robotic gripper is proposed and developed to answer the above research questions. This model aims to systematically determine optimal pick-points of the gripping object sent to the collaborative robot for performing bin-picking actions. The model accurately considers the mechanical parameters of the 2-F robotic gripper and an object to be gripped, which results in an accurate bin-picking application. The proposed model is scalable, meaning it can perform simulations on any object. However, several parameters must be determined before pick-point evaluation. The main contributions of this paper are as follows: (1) The proposed ADAMS/MATLAB cosimulation model can be used to develop the robotic bin-picking setup, where object pick-points must be determined systematically due to object complexity. The proposed simulation model can be integrated with the module to select object pick-points. Furthermore, the proposed simulation model can be used with the robotic bin-picking software to substitute manual determination of pick-points, i.e., via ROS or a similar supported robotic interface. (2) In case of replacement of the 2-F robotic gripper with another one, the proposed model can be used to determine which of the robotic grippers (or robotic gripper variants) is the most appropriate for the bin-picking process. For example, if the selected robotic gripper achieves a higher overall grasp-quality score on a set of pick-points, it can be considered the most appropriate. (3) The proposed simulation model can also be used to improve the existing configuration of the robotic gripper (finger width, finger depth, finger shape, stroke width, etc.) according to the bin-picking score of the specific configuration. Additionally, based on our analysis, new variants of robotic grippers can be proposed. While the first application requires little or no modification to the existing model, the second and the third applications proposed would require moderate modification to the simulation model to provide the user with the model's potential benefits for pick-point evaluation. It must be emphasized that the most advanced pick-point determination procedures operate in real-time for various objects based on data gathered with machine vision systems. Nevertheless, many bin-picking applications still focus on bin-picking products of a single type (i.e., electrical outlets). In this case, a study of the object pick-points can be determined prior to the bin-picking application. Since the number of pick-points corresponds to the overall bin-picking success, the object pick-points should be determined systematically and in the most significant number possible. This paper aims to provide an alternative approach to pick-point determination for industrial bin-picking applications.