## 1 Introduction  
Cooperative robotics and automation have generated growing interest in the scientific community and industry, owing to their potential to improve the efficiency and precision of manufacturing and other industrial applications [1; 2; 3]. Among the most widely used industrial robots are Selective Compliance Assembly Robot Arm (SCARA) robots, which are known for their high speed and precision in assembly and material handling tasks [4]. These types of robots stand out for their ability to move in a horizontal plane with high vertical rigidity, making them ideal for applications that require fast and repetitive movements with high precision, helping industries reduce costs, increase production, and improve the quality of their final products [5]. Cooperative robotics, which involves collaboration between multiple robots to achieve common goals, is an emerging field with considerable potential for transforming industrial processes [6; 7; 8]. However, the integration of multiple robots in a cooperative environment presents significant challenges, primarily in terms of movement coordination and obstacle avoidance, particularly in dynamic environments where conditions can change rapidly. Coordination between robots is essential to avoid collisions and ensure that each robot completes its task without interference [9; 10]. The main challenges include synchronization and task allocation, as well as route management, while detecting and avoiding both static and dynamic obstacles to prevent interruptions in their work and possible damage, as well as to avoid contact between robots. Effective communication and the ability to make quick decisions are necessary, adjusting their movements according to the actions of their counterparts and changes in the environment. In the search for solutions, various authors have developed techniques to coordinate robots in cooperative tasks. One of the most notable examples is decentralized coordination, in which each robot makes decisions based on local information obtained from its neighbors. This method is highly scalable and efficient for large robot swarms in dynamic environments. For example, in [11], Gaussian belief propagation was used to handle multimodal distributions, improve coordination, and avoid collisions in real time for thousands of agents. This type of coordination allows robots to operate autonomously without the need for a central controller, thereby reducing computational complexity and intensive communication. Another relevant technique is altruistic coordination described in [12], which is based on each robot being willing to make concessions to avoid conflicts in congested environments. Robots use strategies such as waiting, advancing, dodging, retreating, and turning to adjust their trajectories and avoid collisions. This method allows for a quick response to changes in the environment and significantly reduces communication overhead because the robots only need to coordinate with those within their immediate communication network. The experimental results demonstrate that this strategy is both efficient and scalable, providing competitive solutions compared to other coordination approaches. The use of probabilistic graphical models has also been prominent in multi-robot coordination, as detailed in [12]. In these models, robots are represented as nodes in a graph, and the connections between them indicate communication. This approach has enabled the development of robust solutions for the localization and control of robots in environments with dynamic uncertainty, using belief propagation to handle multimodal distributions and improve coordination in complex scenarios. The synchronization and coordinated control of multiple manipulators are other important areas of study in cooperative robotics, as mentioned in [13]. Centralized methods consider all robots to be part of a high-dimensional composite system and seek a global solution to avoid collisions.