# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 2 Related Works  
Many research groups in the robotic research community are working on the problem of successful grasp (or pick-point) determination for various robotic grippers for rigid or nonrigid objects (i.e., cloths, chips packaging, etc.). In a survey by [PERSON], [PERSON], [PERSON], and [PERSON] [18], the authors, in detail, analyze papers dealing with the grasp estimation problems as well as the processes that must happen before the grasping estimation (e.g., object localization, object pose estimation, motion planning). It is evident that the grasp estimation problem mainly relates to the machine-vision challenges regarding when the information about the grasp estimation is directly derived from the RGB-D image/point cloud. The authors categorize the grasp estimation problem into 2D planar grasps (the grasp contact points can uniquely define the gripper's grasp pose) and 6 DoF grasps (the gripper can grasp the object from various angles; 6D gripper pose is essential to conduct the grasp). Since our test object is subjected to the simulation model limitations, we will focus on the 2D grasps in our research model. The authors also divide the methods dealing with the problem into two categories, namely, (i) traditional (e.g., machine learning methods to train classifiers based on manually selected 2D descriptors) and (ii) deep-learning methods [40; 41] (e.g., Multilayer Perceptron (MLP), Fully Convolutional Network (FCN)-based methods, and Capsule-based methods). Based on the literature review by [PERSON], [PERSON], [PERSON], and [PERSON] [18], grasp estimation problems have been gaining attention in recent years. [PERSON] and [PERSON] [42] noted that robot grasping, detection and planning can be roughly divided into three types of problem solving methods: (1) empirical methods (grasp positions on known models are evaluated through physical analysis or virtual environment simulation and stored as a database); (2) analytical methods (first, candidate grasp positions are obtained according to geometric analysis, mechanical analysis, or model reasoning; then, a deep neural network model is established to extract the features of the grasp position, which scores the grasp reliability; and finally, the candidate with the highest score is selected); and (3) detection-based methods that use the neural network as a fitter to directly estimate the parameters of the grasp position from the point cloud or image. According to [PERSON] and [PERSON] [42], this method is relatively simple and easy to implement. Therefore, it has gradually become the most used method in grasp detection in the last few years. Since our problem-solving falls into the first two categories, namely, a combination of empirical and analytical methods, we analyze related works. Lastly, we briefly examine papers containing different \"deep-learning approaches\" as the current state-of-the-art, which show very high accuracy in the grasp-detection area. Perhaps the closest related work is by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [39]. The authors also used the system software ADAMS for conducting grasp configuration determination for a Pisa/IIT SoftHand. They created a batch simulation setup to evaluate grasp affordances on kitchenware objects (a cup, a colander, and a plate). Using MATLAB, they modified simulation parameters so that during each pick-point evaluation, the robotic hand links moved appropriately to form the grasping motion. They performed the grasping experiments on an actual KUKA robot and Pisa/IIT SoftHand prototype by accurately replicating hand/object configuration. The ADAMS simulations served as a means for determining possible grasp configurations that would result in the successful grasping of the object. We further developed our model to execute simulations in parallel compared to their approach. Our model can sweep mechanical parameters in a graphical user interface to find the most appropriate contact and force controller parameters settings.