## 1 Introduction
In addition, some researchers have explored techniques for collision-free path planning combined with position control to ensure that the robot accurately follows the trajectory. An example of this is the work of [36], which implemented neural network controllers to execute the trajectory and used an RRT path planner to generate it in robotic manipulators with four and seven degrees of freedom. Another example is [37], where an improved APF-RRT* algorithm was developed for motion planning, optimizing node selection and smoothing the path. In the field of mobile robotics, [38] developed an enhanced APF-RRT* algorithm for the motion planning of a mobile robotic arm and established a Fuzzy-DDPG-PID controller for the arm to follow the planned path. Studies on parametric uncertainty have also been conducted, such as in [39], which proposed a simple and effective adaptive control scheme that includes adaptation laws for unknown constant kinematic and dynamic parameters. Finally, refs. [40; 41] present the implementation of fixed-time SMC tracking controllers for robotic manipulators in the presence of uncertainties and parametric disturbances. The importance of this study is primarily based on the ability of SCARA robots to address cooperative task challenges using advanced trajectory planning and robot coordination algorithms. By implementing an RRT trajectory planner and nonlinear control techniques, the system enhances the adaptability and autonomy of robots. This enables them to predict and react in real time to the movements of another robot and changes in their environment, which are crucial for maintaining a safe and efficient work environment. In this context, this study focuses on the development, design, and simulation of control algorithms for coordination and obstacle avoidance in cooperative SCARA robots. The main objective is to create a system that allows multiple SCARA robots to work together efficiently in a shared environment, avoiding collisions and optimizing movement trajectories. To achieve this goal, advanced control algorithms have been designed, such as RRT algorithms, which integrate trajectory planning techniques that consider the positions and velocities of the involved robots and a predictive nonlinear controller to anticipate and adjust movements in real time, ensuring smooth and precise coordination. The relevance of synchronization and effective communication between robots to avoid collisions and optimize trajectories and cycle times is highlighted. All the tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots under controlled conditions. The positive results obtained in the simulations suggest that the system is suitable for future practical implementations in industrial manufacturing applications, such as handling of chemicals, collaborative welding, quality inspection, among others, which are performed in high-speed production environments. These findings underscore the potential of a cooperative SCARA system to improve the precision and speed of operations and allow greater flexibility in the design of production lines. By integrating advanced trajectory planning and control algorithms, the system can quickly adapt to changes in the environment and handle objects of different sizes and shapes, which is essential for personalized production and agile manufacturing. These benefits can translate into a significant reduction in cycle times, an improvement in product quality, and a decrease in operating costs. This manuscript is structured into six sections:  
In Section 1, the introduction and state of the art are presented, referencing various applications, in which robot coordination, obstacle avoidance, and control techniques are used. In Section 2, kinematic and dynamic modeling of SCARA robots are developed. An algorithm to synchronize robots in cooperative tasks is presented, and possible collisions in the system are analyzed.