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Figure 5. The methodology used for the development and implementation of the platform and virtual scenarios. Reports collected by the International Federation of Robotics (IFR) indicate that, be- tween 2016 and 2019, there was an increase in the use and demand for service robots in medicine, agriculture, logistics, and defense compared to previous years. Based on these data, health, logistics (supply chain), and industry were chosen for the creation of the three virtual scenarios on the ROS platform [33]. Figure 5 summarizes the procedure for implementing the platform and scenarios.

Figure 5 The methodology used for the development and implementation of the platform and virtual scenarios. Reports collected by the International Federation of Robotics (IFR) indicate that, be- tween 2016 and 2019, there was an increase in the use and demand for service robots in medicine, agriculture, logistics, and defense compared to previous years. Based on these data, health, logistics (supply chain), and industry were chosen for the creation of the three virtual scenarios on the ROS platform [33]. Figure 5 summarizes the procedure for implementing the platform and scenarios.

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Figure 1. ROS is an open-source working environment, which includes connectivity with physical system. It is used for programming and robot simulations. ROS is organized by execution units (called nodes), which communicate by publishing /subscribing messages, offering services, and/or executing actions. ROS includes connectivity with physical and real hardware (for example sensors or actuators) and algorithm implementation of various control types, navigation, map creations, etc. [29]. Its Gazebo module (simulation) and RVIZ (ROS-VIsualiZer) complete a flexible framework that allows teaching with simulated elements and minimal effort to transfer and test developments on real hardware.
Figure 1. ROS is an open-source working environment, which includes connectivity with physical system. It is used for programming and robot simulations. ROS is organized by execution units (called nodes), which communicate by publishing /subscribing messages, offering services, and/or executing actions. ROS includes connectivity with physical and real hardware (for example sensors or actuators) and algorithm implementation of various control types, navigation, map creations, etc. [29]. Its Gazebo module (simulation) and RVIZ (ROS-VIsualiZer) complete a flexible framework that allows teaching with simulated elements and minimal effort to transfer and test developments on real hardware.
Figure 2. Gazebo main screen. Gazebo can run 3D robot simulations. Gazebo is a 3D environment for running robot simulations in combination with ROS. In this way, users can test algorithms, design and test mobile robots, and train an AI (Artificial Intelligence) system using virtual representations of realistic scenarios. This offers the ability to simulate robot movements accurately and efficiently in complex indoor and outdoor environments [30]. The Gazebo graphical user interface (GUI) can be observed in Figure 2. ROS Visualization (RVIZ) is a 3D visualization tool for ROS applications; its main screen is shown in Figure 3. RVIZ displays the mobile robot model, captures robot sensor data, and reproduces the captured data. It can display data from cameras, lasers, and 3D and 2D devices as images and point clouds [28].
Figure 2. Gazebo main screen. Gazebo can run 3D robot simulations. Gazebo is a 3D environment for running robot simulations in combination with ROS. In this way, users can test algorithms, design and test mobile robots, and train an AI (Artificial Intelligence) system using virtual representations of realistic scenarios. This offers the ability to simulate robot movements accurately and efficiently in complex indoor and outdoor environments [30]. The Gazebo graphical user interface (GUI) can be observed in Figure 2. ROS Visualization (RVIZ) is a 3D visualization tool for ROS applications; its main screen is shown in Figure 3. RVIZ displays the mobile robot model, captures robot sensor data, and reproduces the captured data. It can display data from cameras, lasers, and 3D and 2D devices as images and point clouds [28].
Figure 3. RVIZ main screen. This tool allows a 3D visualization for ROS applications.
Figure 3. RVIZ main screen. This tool allows a 3D visualization for ROS applications.
Figure 6. Map of the scenario #1—hospital: reception (A), room (B), and corridor (C). The Gazebo tool was used for the scenario design. The first proposed scenario is 1 hospital healthcare floor. In this scenario, a mobile robot can help carry out activities where direct human contact mus t be avoided, for example, during the current COVID-19 oandemic. In this scenario, the following spaces were considered: a reception area, nine nedical care rooms, and a corrid or connecting the rooms with the reception area (there ire nine entrances to medical care rooms). In each room, there are different obstacles with enough space for the robot to circulate between them. In Figure 6, one observes the ‘eception area (A), the rooms (B), and the corridor (C). The total area is 15 m wide and 45 m ong (675 m*). To simulate a realistic hospital in Gazebo, we used models with pre-existing eople placed throughout the sce nario. Movements were not counted independently.
Figure 6. Map of the scenario #1—hospital: reception (A), room (B), and corridor (C). The Gazebo tool was used for the scenario design. The first proposed scenario is 1 hospital healthcare floor. In this scenario, a mobile robot can help carry out activities where direct human contact mus t be avoided, for example, during the current COVID-19 oandemic. In this scenario, the following spaces were considered: a reception area, nine nedical care rooms, and a corrid or connecting the rooms with the reception area (there ire nine entrances to medical care rooms). In each room, there are different obstacles with enough space for the robot to circulate between them. In Figure 6, one observes the ‘eception area (A), the rooms (B), and the corridor (C). The total area is 15 m wide and 45 m ong (675 m*). To simulate a realistic hospital in Gazebo, we used models with pre-existing eople placed throughout the sce nario. Movements were not counted independently.
Table 1. Differences among the three scenarios.
Table 1. Differences among the three scenarios.
Figure 7. Map of the scenario #2—warehouse unloading (A), loading (B), and storage areas (C)
Figure 7. Map of the scenario #2—warehouse unloading (A), loading (B), and storage areas (C)
Figure 8. Map of the scenario #3—Industrial manufacturing area: machine zone 1 (A), machine zone 2 (B), machine zone 3 (C), machine zone 4 (D), and warehouse (E). The final proposed scenario is an industrial manufacturing laboratory. The laboratory has a central area with several machines in the surrounding area and a warehouse. Figure 8 shows the machine zones (A), (B), (C), (D), and a warehouse space (E). The area in total is 20 m long and 25 m wide (500 m?).
Figure 8. Map of the scenario #3—Industrial manufacturing area: machine zone 1 (A), machine zone 2 (B), machine zone 3 (C), machine zone 4 (D), and warehouse (E). The final proposed scenario is an industrial manufacturing laboratory. The laboratory has a central area with several machines in the surrounding area and a warehouse. Figure 8 shows the machine zones (A), (B), (C), (D), and a warehouse space (E). The area in total is 20 m long and 25 m wide (500 m?).
Figure 9. A virtual mobile robot with four wheels used in the simulations with the following components: LiDAR sensor (A), stereoscopic camera (B), and encoders (C). A virtual mobile robot with four wheels, front steering, and dimensions of 65 cm long and 46 cm wide was implemented for testing, as shown in Figure 9. It has two sensors for mapping; the first is the LiDAR sensor, which determines the distance from an object using a laser emitter. The second sensor is a stereoscopic camera that is not used for autonomous navigation or environmental mapping; instead, its purpose is for the user to visualize the virtual environment in the current robot position.
Figure 9. A virtual mobile robot with four wheels used in the simulations with the following components: LiDAR sensor (A), stereoscopic camera (B), and encoders (C). A virtual mobile robot with four wheels, front steering, and dimensions of 65 cm long and 46 cm wide was implemented for testing, as shown in Figure 9. It has two sensors for mapping; the first is the LiDAR sensor, which determines the distance from an object using a laser emitter. The second sensor is a stereoscopic camera that is not used for autonomous navigation or environmental mapping; instead, its purpose is for the user to visualize the virtual environment in the current robot position.
Finally, the odometry data are generated from the encoders and the Gazebo platform which provide the position of X and Y coordinates locating the robot in the virtual scenario Figure 10 shows a flowchart of the steps to perform a simulation. Figure 10. Steps to run a simulation. First, the users have to select the scenario. After, the initial and target point must be set. Finally, the users have to configure the algorithm to run.
Finally, the odometry data are generated from the encoders and the Gazebo platform which provide the position of X and Y coordinates locating the robot in the virtual scenario Figure 10 shows a flowchart of the steps to perform a simulation. Figure 10. Steps to run a simulation. First, the users have to select the scenario. After, the initial and target point must be set. Finally, the users have to configure the algorithm to run.
Figure 11. Example of paths in the same scenario: simple path (a) and complex path (b).
Figure 11. Example of paths in the same scenario: simple path (a) and complex path (b).
Table 2. Example of sheet report for results obtained. Students were asked to perform the set of simulations in each scenario during the class session. At the beginning of the activity, they were given the datasheet shown in Table 2 for recording and further comparison of their simulation results.
Table 2. Example of sheet report for results obtained. Students were asked to perform the set of simulations in each scenario during the class session. At the beginning of the activity, they were given the datasheet shown in Table 2 for recording and further comparison of their simulation results.
Figure 12. Example of the time for different paths. In this example, in the simple path (a), the robot requires less time to arrive to the target point in comparison with the complex path (b).
Figure 12. Example of the time for different paths. In this example, in the simple path (a), the robot requires less time to arrive to the target point in comparison with the complex path (b).
Figure 13. Examples of the mean error value for different paths. In this example, in the simple path (a), the distance between the robot and the target point is less than in the complex path (b).
Figure 13. Examples of the mean error value for different paths. In this example, in the simple path (a), the distance between the robot and the target point is less than in the complex path (b).
Table 3. Summary of simulation data obtained.
Table 3. Summary of simulation data obtained.
Figure 14. Comparison between the results of the written exams of phase 1 and 2.
Figure 14. Comparison between the results of the written exams of phase 1 and 2.
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 he 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 he case of the complex path (p < 0.05). Figure 16 shows the results from virtual scenario #2—the warehouse, where t using the EBand algorithm required longer times to cover the short route, 89.09% slower than the TE more accurate than the EBand algorithm on the simple path. On the other hand complex pat. The statistica analysis demonstrates that in the case of the simple path there is a sta significant difference (p < 0.05) between TEBand and EBand groups in terms of taken to complete the trajectory and their accuracy. he robot Band algorithm. In terms of accuracy, the TEBand algorithm was 36.56% , on the h, the TEBand algorithm could not complete the trajectories in this scenario. tistically he time
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 he 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 he case of the complex path (p < 0.05). Figure 16 shows the results from virtual scenario #2—the warehouse, where t using the EBand algorithm required longer times to cover the short route, 89.09% slower than the TE more accurate than the EBand algorithm on the simple path. On the other hand complex pat. The statistica analysis demonstrates that in the case of the simple path there is a sta significant difference (p < 0.05) between TEBand and EBand groups in terms of taken to complete the trajectory and their accuracy. he robot Band algorithm. In terms of accuracy, the TEBand algorithm was 36.56% , on the h, the TEBand algorithm could not complete the trajectories in this scenario. tistically he time
Figure 17 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 17 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 18. Comparison of the results of the usability test. In general, the use of simulations helps the learning process, but the professor should make sure that the students understand how to use the platform. 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.
Figure 18. Comparison of the results of the usability test. In general, the use of simulations helps the learning process, but the professor should make sure that the students understand how to use the platform. 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.
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RoboticsComputer ScienceArtificial Intelligence
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