This paper presents an experimental study comparing four approaches—random, reactive, planning, and anticipation—in robot navigation. Two robots navigated through environments with or without obstacles, aiming to switch places. The study evaluated the efficiency of each strategy in different scenarios. The reactive approach, which directs the robot toward the goal, performed well in empty environments but struggled with obstacles. The planning approach, using grid-based navigation, was effective in environments with known layouts. The anticipation approach, which predicts the other robot's movements, showed potential for improving efficiency by avoiding collisions and reducing the need for reactive avoidance. However, the results also showed that anticipation was not always better than purely reactive strategies, especially in complex environments. The study found that anticipation could be beneficial in certain situations, but its effectiveness depended on the environment's complexity and the accuracy of the robot's control system. In environments with obstacles, anticipation strategies like A-long (where the robot farthest from the goal has priority) were more efficient. However, in some cases, the planning approach outperformed anticipation. The experiments revealed that the time taken for robots to switch places varied significantly depending on the strategy used. The random strategy was the slowest, while the reactive and planning strategies were more efficient. Anticipation strategies showed promise but were not consistently superior. The study highlights the importance of anticipation in robot navigation, particularly in dynamic environments where obstacles and other robots are present. However, it also emphasizes that the effectiveness of anticipation depends on the environment and the accuracy of the robot's control system. Future research aims to improve the precision of control systems, increase environmental complexity, and test with more robots to better understand the benefits of anticipation in real-world scenarios.This paper presents an experimental study comparing four approaches—random, reactive, planning, and anticipation—in robot navigation. Two robots navigated through environments with or without obstacles, aiming to switch places. The study evaluated the efficiency of each strategy in different scenarios. The reactive approach, which directs the robot toward the goal, performed well in empty environments but struggled with obstacles. The planning approach, using grid-based navigation, was effective in environments with known layouts. The anticipation approach, which predicts the other robot's movements, showed potential for improving efficiency by avoiding collisions and reducing the need for reactive avoidance. However, the results also showed that anticipation was not always better than purely reactive strategies, especially in complex environments. The study found that anticipation could be beneficial in certain situations, but its effectiveness depended on the environment's complexity and the accuracy of the robot's control system. In environments with obstacles, anticipation strategies like A-long (where the robot farthest from the goal has priority) were more efficient. However, in some cases, the planning approach outperformed anticipation. The experiments revealed that the time taken for robots to switch places varied significantly depending on the strategy used. The random strategy was the slowest, while the reactive and planning strategies were more efficient. Anticipation strategies showed promise but were not consistently superior. The study highlights the importance of anticipation in robot navigation, particularly in dynamic environments where obstacles and other robots are present. However, it also emphasizes that the effectiveness of anticipation depends on the environment and the accuracy of the robot's control system. Future research aims to improve the precision of control systems, increase environmental complexity, and test with more robots to better understand the benefits of anticipation in real-world scenarios.