## 1 Introduction  
In today's industrial landscape, where speed and efficiency are critical factors for success, optimizing robotic processes is not only a technical challenge but also a strategic necessity to maintain competitiveness. A robotic cell (RC), defined as an automated system composed of one or more robots, is designed to perform specific tasks in manufacturing and industrial production, such as assembly, welding, and material handling. These configurations not only optimize workflow but also reduce human intervention, improve precision, and enhance efficiency in a controlled and safe environment [1, 2]. The current trend in the design and improvement of RC focuses on optimizing operations to maximize productivity, reduce cycle times, and minimize operational costs [3]. This focus has led to the exploration of advanced methods aimed at enhancing overall performance and leveraging the capabilities of automated systems more effectively. Traditionally, RC design has been carried out using methods that, while effective in some cases, often lack efficiency, flexibility, and scalability. Early designs required multidisciplinary teams to undergo iterative processes of review-adjustment, evaluating cell productivity through cost-benefit analyses [4]. Some authors proposed initiating the design process by determining the relative position of the robot and its assigned tasks, followed by incorporating machine dimensions, workstation locations, orientations, and specific operational characteristics of the implemented robot [5]. Other studies have addressed the optimization of cycle times and robotic trajectories using mathematical models based on kinematics and dynamics. These models calculate joint angles, velocities, and accelerations but are limited to well-defined and controlled systems [6; 7; 8]. Classical optimization algorithms, such as gradient descent, have been employed to adjust trajectories and minimize energy or time, while linear programming has been used to optimize dynamic work areas. However, these approaches have shown limitations in scalability and efficiency, particularly in nonlinear or large-scale problems [9]. On the other hand, simulations have served as practical tools to optimize trajectories and spatial layouts before the physical implementation of robots. These simulations allow for testing multiple configurations without disrupting production, identifying potential collisions, and significantly reducing testing time and costs. Nevertheless, their effectiveness heavily relies on the fidelity of the simulated model [10; 11]. Additionally, expert systems, generally based on rule-based models, have enabled the integration of historical engineering data to generate process design alternatives that consider both performance requirements and the specific constraints and specifications of robots and components [12; 13]. Feedback-based optimization operates in real-time using sensors, such as cameras and force or position sensors, to dynamically adjust robotic trajectories. Algorithms analyze sensor data to correct paths and avoid collisions. While this approach adapts well to dynamic environments, its response is slower due to higher computational demands compared to preprocessed solutions [14; 15]. Meanwhile, the combination of virtual reality and digital twins is revolutionizing the design, analysis, and optimization of RC. Through 3D virtual models that accurately replicate all physical components in a digital environment, these tools allow for the simulation of various spatial configurations before making physical changes, providing an immersive real-time experience. These models are particularly useful for identifying ergonomic or accessibility issues that traditional algorithms might overlook. However, despite their ability to test multiple configurations, the number of scenarios that can be simulated is limited by computational resources and system capabilities, preventing guarantees of global optimization [16; 17; 18].