# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 1 Introduction  
The term Industry 4.0 (I4.0) represents the fourth industrial revolution. Its beginning dates to 2010, and the term Industry 4.0 is often used interchangeably with the term \"smart industry.\" The concept's core idea lies in that today's industry needs answers to economic, sociological, and political changes generated by the end-users [1]. However, the industry usually requires some time to adapt to new concepts and adjusts its business accordingly. Therefore, the novelties in the industry need some time to be implemented, as is the case with I4.0. While there is no single definition of what precisely the term I4.0 represents, we can generally divide the I4.0 concept into three major categories, such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and cloud computing [2]. The first concept describes the ability to couple the physical properties of a system with advanced computational algorithms, such as in the field of predictive maintenance [3]. The IoT relates to the interconnection of various devices that rely on sensors, communication, networking, and information processing technologies [2]. Sensor networks can gather dataregarding the manufacturing process and then later use that data to optimize the process. Cloud computing refers to sharing documents, collaboration, distributed production, and resource optimization [4]. The main advantage of cloud computing is scalability, which means extra computing power is ensured if additional demand arises [5]. We can conclude that the I4.0 concepts were developed to improve industrial processes by collecting and using the data for optimization. Since the industry depends heavily on logistics services, the logistics processes must be adapted accordingly to cope with the increasing demands. Hence, the Logistics 4.0 (I4.0) term has been coined based on the Industry 4.0 term [6; 7]. Logistics 4.0 describes advanced usage of various technological advancements such as those in Industry 4.0, such as intelligent devices, IoT, and other CPS, which aim to reorganize some of the basic concepts of logistics [7]. This holds especially true due to the rising trends of E-commerce [8] and the concerning trends of workforce deficiency and the aging population [9]. Logistics' global market size accounted for around EUR 8.43 trillion in the year 2021 [10], while the E-commerce market accounted for over EUR 440 billion in the year 2021 [11]. One of the most critical aspects of logistics is intralogistics, which focuses on the processes carried out inside warehouses and distribution centers and ensures the correct flow of materials and information [12]. One of the essential processes in the warehouse is order-picking of goods according to the customer's order. It is estimated that order-picking accounts for around 55% of the warehouse operation costs [13]. Therefore, automatization in warehouses is necessary to achieve high throughput rates, cope with the increasing trends of \"batch size one,\" and provide the shortest delivery times [8]. In warehouses, the most labor- and time-intensive process is the order-picking process, which requires a lot of (human) intervention and does not add expressive increased value to the product. Several solutions have already been developed to reduce the operators' workload and reduce the non-value-adding transport of the product within the warehouse. Mobile robots are used to transport goods along the operators [14], and mobile robots are equipped with robotic arms to pick the objects directly from the shelving units [15] to the most advanced automated storage and retrieval systems coupled with the robotic bin-pickers [16]. Robotic bin-picking systems aim to reduce or eliminate human intervention during the bin-picking processes for the bulk objects (items) with the help of a (collaborative) robotic arm, a vision system, robotic grippers, and control systems.