# A Free Simulation Environment Based on ROS for Teaching Autonomous Vehicle Navigation Algorithms
## 4 Discussion  
Figure 15 shows the results from virtual scenario #1--the hospital floor in which the mobile robot using algorithm TEBand required longer periods of time to complete the trajectories, 14.23% and 48.62% slower than when using the EBand algorithm. In terms of accuracy, the EBand algorithm was 11.40% more accurate than the TEBand algorithm on the short path. However, the TEBand algorithm was 15.46% more accurate than the EBand algorithm on the long path. The statistical analysis demonstrates that, in the case of the simple path, there is no statistically significant difference between TEBand and EBand groups in terms of the time taken to complete the trajectory and their accuracy but not in the case of the complex path (\(p<0.05\)). Figure 16 shows the results from virtual scenario #3--the industrial manufacturing area. The EBand algorithm took the longest times in both the short and long paths, 82.50% and 58.40% slower than the TEBand algorithm, respectively. In terms of accuracy, the EBand algorithm was 56.85% more accurate than the TEBand algorithm on the short path. Similarly, the Eband algorithm was 97.69% more accurate than the TEBand algorithm on the long path. The statistical analysis demonstrates that there is a statistically significant difference between all study groups (\(p<0.05\)) in terms of the time taken to complete the trajectory and their accuracy. Figure 16: Comparison of time to finish and error among experimental groups using the warehouse scenario (* \(p<0.05\)). Figure 15: Comparison of time to finish and error among experimental groups using the hospital scenario (* \(p<0.05\)). Regarding the performance of the students, Figure 14 shows that there is a difference in the results of the written exams. Moreover, the \(t\)-test demonstrates that there is a statistically significant difference (\(p<0.05\)) between phase 1 and phase 2. In other words, in the case of phase 2 (when professor uses the simulations in class), this educational approach helps improve the performance of students in comparison to when professor uses a traditional teaching method. This fact demonstrates that the main objective of this work has been met, the proposed platform helps students in improving their academic performance. Figure 18 shows that, in general, the use of simulations helps better understand the theoretical concepts, the advantages and disadvantages of each algorithm, makes the class session more interesting, motivates students to pay attention, and makes students feel more confident. More than 85% of the students suggest that this educational approach should always be used in class in order to improve their learning process. Nevertheless, some students think that the use of the platform is not simple and easy. This fact suggests that it is necessary to consider an initial session in which the teacher familiarizes the students with the use of the platform and its functions so that the tool is not distracting or does not impair the learning process. From the point of view of the professor and the students, this platform is a valuable tool for those who teach subjects related to the design and implementation of autonomous vehicles. In addition, freed from the responsibility of implementing the tools, the students can focus on the learning algorithms and autonomous driving techniques in order to analyze different variables that affect their operation. An advantage of this project is that by using the ROS environment, students can perform different tests and analyze the performance of the algorithms to compare the data. Using the proposed platform, students can evaluate and compare the performance of different algorithms in different scenarios and different trajectories. The students have to set the trajectory (start point and target point), and at the end of the simulation, the platform provides the performance results of the algorithm (distance, time, and error).