# Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation
## 3 Results
### Case Study 2: Real Robot Behavior  
A second experiment was conducted to demonstrate the feasibility and suitability of the proposed virtual reality interface to control a real mobile robot. Figure 3b shows the remote environment used for this case study, which includes several obstacles located strategically to cause challenging situations, such as the avoidance of obstacles with round and sharp corners and trap situations. The video of this experiment can be played in [67].  
For this second experiment, Figure 6 shows the normalized distance \(\rho\) between the detected obstacles and the mobile robot boundary together with the control velocity commands.  
Moreover, Figure 7 shows several frames of this experiment related to the obstacle avoidance capability of the robot and how this is depicted in the VE. In particular, Figure 7a,c show the robot performance when avoiding an obstacle with rounded shape; see the time interval 45-74 s in the graphs of Figure 6a. Note that the robot deviates from the reference trajectory when the repulsive force of the potential field-based navigation method becomes active (i.e., \(\rho<\rho_{0}\)) (see the top graph in Figure 6a), and tries to go back to the reference once the mentioned repulsive force is deactivated, i.e., when \(\rho>\rho_{0}\). In addition, Figure 7d-f show the robot performance when avoiding an obstacle with sharp corners; see time interval 85-97 s in the graphs of Figure 6a. As in the previous case, the activation of the repulsive force during this time span allows the mobile robot to successfully avoid this kind of obstacle (Figure 6b).  
In addition, Figure 8 depicts several frames of this experiment to show how the robot deals with a trap situation, which occurs around time interval 137-175 s, see Figure 6a. As commented above, this behavior is typically present in potential field-based approaches and, in this case, the user successfully assists the robot to escape from this trap situation by guiding the reference trajectory to an area reachable by the robot.  
Figure 5: Case study 1: robot control performance. (**a**) Top graph: normalized distance \(\rho\) between the detected obstacles and the mobile robot boundary (the dashed line represents the distance threshold \(\rho_{0}\) for the activation of the repulsive force). Middle and bottom graphs: linear and angular velocity commands for the mobile robot. (**b**) 2D robot trajectory: starting robot position (small orange circle); ending robot position (small green diamond); robot trajectory (red dashed line); user reference trajectory (solid black line); and obstacles (solid-thick blue lines).  
Figure 6: Case study 2: robot control performance. (**a**) Top graph: normalized distance \(\rho\) between the detected obstacles and the mobile robot boundary (the dashed line represents the distance threshold \(\rho_{0}\) for the activation of the repulsive force). Middle and bottom graphs: linear and angular velocity commands for the mobile robot. (**b**) The 2D robot trajectory: starting robot position (small orange circle); ending robot position (small green diamond); robot trajectory (red dashed line); user reference trajectory (solid black line); and approximate location of the real obstacles (solid-thick blue lines).  
Figure 7: _Cont._