# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 6 Conclusions  
Using a simulation model to evaluate pick-points for a two-finger robotic gripper, we proved that it is possible to systematically check the performance of the selected robotic gripper pick-point using cosimulation with ADAMS and MATLAB/Simulink. Since the model does not yet consider variations of the object depth or allow inner grasping, the proposed object allowed us to test the proof of our concept. The initial results (75% of correctly determined pick-points) proved that the simulation model should be further developed to address the variations of the object depth. Although the simulations do not yet match reality completely, we have successfully laid the foundations for further research. Setting the correct contact parameters requires the most attention. Therefore, we will use machine learning methods to automatically (based on the actual system response) select the most appropriate ones. To further verify the simulated pick-points, we will use force gauges to check the magnitude of the contact forces and the consistency of the set parameters in the simulation and its physical test setup. In the following research, we will first focus on improving the performance of existing simulations and visualization of the pick-points in the 3D model and further reducing simulation time by optimizing the various aspects of the model. Furthermore, to confirm the pick-point resilience toward minor imperfections of the vision system information, we propose that the pick-point evaluation takes place in two steps. First, the pick-point generator should generate points randomly, and the simulation model should determine the most appropriate pick-points. Next, those most appropriate pick-points should be further modified with small perturbations to account for the vision system imperfections. If the original and the perturbed pick-points are still valid, then those pick-points can be considered to lead to reliable picking. Additionally, the limitations on object rotation should be addressed since the possible application of the simulation model is now limited to a few specific applications. Additionally, we would like to use other types of robotic grippers, not limited only to two-fingered robotic grippers, such as vacuum grippers, three-finger grippers, and different special types of robotic grippers.  
In combination with a selected 3D vision system, evaluating and setting the pick-points using the developed model could be entirely automated, providing reliable information. In the future, we plan to automate the measurements, which we plan to do using an advanced 3D vision system.  
Conceptualization, P.B., D.H. and T.L.; methodology, P.B.; software, P.B.; validation, P.B., D.H. and T.L.; formal analysis, P.B. and D.H.; investigation, P.B.; resources, T.L.; data curation, T.L.; writing--original draft preparation, P.B., D.H. and T.L.; writing--review and editing, P.B., T.L. and D.H.; visualization, T.L.; supervision, T.L. and D.H.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Slovenian Research Agency (ARRS) in ARRS Young Researcher Program (Research activity agreement 2018/2019). T.L. and D.H. were supported by the ARRS Applied Research Project (Research activity agreement 2020/21) entitled: \"Warehousing 4.0--Integration model of robotics and warehouse order picking systems\"; grant number: L5-2626.  
Not applicable.  
All data obtained in this study are available upon request by contacting the corresponding author.  
The authors would like to thank the entire team of the Laboratory for Cognitive Systems in Logistics at the Faculty of Logistics for their support, useful comments, and suggestions.  
The authors declare no conflict of interest.