## 4 Experiments and Discussion
### Evaluation in Unfamiliar Environments
#### 4.4.1 Unfamiliar Plain Environment  
After the training process, we can obtain 6 models because TD3 has trained separately across env1 to env4. Each model was tested 100 times in env5, and the results are presented in Figure 8 and Table 3.  
During the training and testing experiments, it should be noted that the shape and size of the obstacles gradually become more and more complex from env1 to env5. As shown in Figure 8, when the trained models are applied to resolve unfamiliar scenarios, the success rate of TD3 and FL-TD3 tends to decline. The experimental results show that although the trained models of TD3 and FL-TD3 can perform well in the training scenarios (i.e., env1 to env4), they are still prone to collide with unfamiliar obstacles in env5.  
According to the performance metrics, the trained model obtained from TD3-env1 performs the worst and cannot complete the task at all in env5. The success rate of TD3-env2 is 76%, while TD3-env3 and TD3-env4 can achieve about 90%, with the collision rate being less than 5%. Comparatively, the success rate of FL-TD3 and our FGRL is higher than  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**Algorithm** & **Metric** & **env1** & **env2** & **env3** & **env4** \\ \hline \multirow{2}{*}{TD3} & Success rate & 95\% & 96\% & 95\% & 94\% \\  & Average successful steps & 444 & 461 & 459 & 482 \\ \hline \multirow{2}{*}{FL-TD3} & Success rate & 100\% & 100\% & 99\% & 99\% \\  & Average successful steps & 440 & 444 & 439 & 447 \\ \hline \multirow{2}{*}{FGRL} & Success rate & 100\% & 100\% & 100\% & 100\% \\  & Average successful steps & 432 & 438 & 430 & 440 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of three approaches in familiar environments.  
Figure 7: The success rate and average successful steps of three approaches in familiar environments.  
that of TD3. In particular, our FGRL is successful in all 100 testing runs. The results indicate that the combination of federated learning can indeed improve the generalization ability of UGVs to avoid unknown and unfamiliar obstacles. With regard to the average successful steps, our FGRL reduces the average successful steps by 13.6% and 30.9%, in comparison with FL-TD3 and TD3-env4, respectively. Fewer successful steps can indicate that the UGV takes fewer steps to reach the target location. In other words, the navigation path is shorter and more efficient. Therefore, we can conclude that our proposed FGRL reduces the path length without compromising the success rate.  
We can find that the trained models of TD3-env1 and TD3-env2 are completely incapable of accomplishing the navigation task in env6. In addition, the success rate of TD3-env3 and TD3-env4 is only 1% and 14%, respectively. In comparison, the success rate of FL-TD3 has greatly improved, but it is still very low: only 31%. Our FGRL achieves a success rate of 79%, which greatly exceeds the other approaches. With respect to the average successful steps, compared to FL-TD3 and TD3-env4, our FGRL reduces the path length by 20% and 25%, respectively.  
We can conclude that the trained models of TD3 from env1 to env4 can hardly generalize to a new and unfamiliar factory environment, and it is difficult for UGVs to select reasonable actions based on the trained policy. Although our FGRL model was also trained across env1 to env4, it still has good generalization ability when encountering unfamiliar and complex obstacles.