# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 5 Experimental Results and Discussion
### Experiment 7: GA-KL Method Stability Test in a Highly Congested Environment  
To further verify the advantages of the GA-KL method in a highly congested environment, we compare the GA-KL method with three path planning methods: a path integral-based reinforcement learning algorithm [(23)] (PIRL), a hybrid A* algorithm with a reinforcement learning path planning approach (HARL) [(24)], and a multi-destination path planning method [(26)] (OSPC). Figure 15 shows the map used for the experiments; sequence number 13 is the starting point, and the rest of the sequence numbers are the destination points. We used five AGVs departing simultaneously and conducted five multi-AGV path planning experiments, in which the starting point of each AGVs is the same, but the target point is randomized. Each time an AGV reaches the target point, it automatically accepts the task of returning from the target point to the starting point. Dynamic obstacles randomly appear between the nodes, and the number of dynamic obstacles gradually increases. Such an experimental design is associated with a high probability of congestion; the purpose is to test the performance of each path planning method under congestion conditions. The experimental results are shown in Figures 18 and 19.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & _Running Time (s)_ & _Path Length (m)_ \\ \hline _GA-KL_ & _13.28_ & _12.08_ \\ _GP-AP_ & _26.37_ & _11.80_ \\ _BP-P_ & _23.12_ & _11.80_ \\ _HARL_ & _17.31_ & _12.64_ \\ _PIRL_ & _20.35_ & _13.40_ \\ \hline \hline \end{tabular}
\end{table}
Table 8: Run time and path length for five rounds of path planning.  
Figure 17: Environment of the field experiment. The field experiment parameters are as follows.  
As shown in Figures 18 and 19, the GA-KL algorithm achieves better results in terms of total path length and total completion time as the number of dynamic obstacles increases. PIRL achieves path optimization through an a priori knowledge base, but the AGVs depart simultaneously, meaning the a priori knowledge obtained from each AGV cannot be shared, with a lack of a scheduling function; therefore, the planning results degenerate as the number of dynamic obstacles increases. For HARL, although the A* algorithm provides a satisfactory global search capability, it suffers from the same problems as PIRL, i.e., a lack of sharing of prior knowledge and a lack of scheduling capability. For OSVC, although its multi-purpose path planning provides advantages in terms of total path length, it is a static algorithm that does not have the ability to handle congestion and dynamic obstacles when they occur. GA-KL does not need to share a priori knowledge but achieves scheduling with an improved genetic algorithm, and the appearance of dynamic obstacles is fed back to the node congestion coefficient in time; when the node congestion coefficient reaches a certain peak, it suspends the departing AGVs into a queuing session to reduce the risk of congestion as much as possible, achieving satisfactory performance with respect to total completion time.