# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
## 5 Experimental Studies
### Case 1: \"Lider Krumzi Engelin Arkansas Git (\"Go behind the Red Obstacle\")\" Command  
\"Lider krumzi engelin arkasna git (\"Go behind the red obstacle\")\" was given in Turkish to the leader of a traveling robot group of three robots. The command was first decomposed into its elements using a NLP:  
\"subject/lider (\"leader\")\";  
\"complement/krumzi engelin arkasn (\"behind the red obstacle\")\";  
\"predicate/git (\"go\")\". After parsing, the command structures obtained were applied to the robot system, and the necessary actions were requested. While the leader follows the prescribed trajectory, the other follower robots follow the leader in a platoon formation. This application was carried out considering two different initial conditions to examine whether the initial conditions affected the robots' behavior. The initial positions of the robots are given in Table 2.  
According to the first initial conditions of the robots, the leader and followers depend on the command given to the leader. The robots realized the predicted trajectory, as shown in Figure 12. As seen in Figure 12, the leader robot planned its path according to the command given to it and went to the target location. When the position and orientation errors of the leader robot are analyzed, it can be observed that the error increases at node transitions. Still, when the target node is reached, the errors approach zero. Initially, there is a distance of one meter between the robots. While the leader robot makes sharper turns at some points according to the given trajectory, since the followers follow their leader, they cannot keep up with these turns properly and follow the leader with more deviations than in other places. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Robot Name** & **X** & **Y** \\ \hline Leader & 2 & 0 \\ Follower 1 & 3 & 0 \\ Follower 2 & 4 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial coordinates of robots for Case 1. Figure 12: Position–path graph for Case 1. Case 2: \"Lider Beyaz Engelin Arkansas Git Sonra Yesil Engelin Onune Git (\"Leader Go behind the White Obstacle and then go in Front of the Green Obstacle\")\" Command  
There is a two-step process here. The robots have to pass behind the white obstacle and move from there to the front of the green obstacle. The target point is in front of the green obstacle. \"Lider beyaz engelin arkasina git sonra yesil engelin onune git (\"Leader go behind the white obstacle and then go in front of the green obstacle\")\" was given in Turkish to the leader of the traveling robot group of three robots. The command was first decomposed into its elements using NLP:  
First action:  
\"subject/lider (\"leader\")\";  
\"complement/beyaz engelin arkasa (\"behind the white obstacle\")\";  
\"predicate/git (\"go\")\". Second action:  
\"subject/lider (\"leader\")\";  
\"complement/yesil engelin arkasa (\"in front of the green obstacle\")\";  
\"predicate/git (\"go\")\". The subject of the command is the leader robot. Here, a conjunction is used to indicate sequence and time. The location information is behind the white obstacle and in front of the green obstacle. The predicate of the sentence is the action of going. The data structure used for this process is given in Figure 4. This decomposition was then applied to the robot system, and they were required to carry out the task accordingly. In this experiment, the starting positions of the robots were taken, as shown in Table 2. While the leader followed the prescribed trajectory, the other follower robots followed the leader in a platoon logic. Figure 13 shows the position-path graph of this experiment. Due to differences in the initial conditions, followers 1 and 2 tried to follow the leader using a shorter route without passing through the leader's initial position. A similar situation occurs when the leader made a sharp turn. The position and orientation of the paths taken by the leader and the followers concerning the predicted initial positions can be clearly seen in the position-path graph.