## 5 Experimental Evaluation
### Simulation Using Gazebo  
For gait tracking and crowd maneuvering, a test environment in Gazebo [(54)] was developed, as shown in Figure 7. The developed simulation mimics a real-world scenario of a patient walking behind a robot amidst a crowd and evaluates the robot's ability to assist the patient. The crowd is simulated using the social force model to model crowds [(55)]. First, the patient can gradually walk at their own speed toward the robot. Then, the robot senses the patient and prepares to assist the patient. Finally, once the patient is near the robot and starts using it as support, they co-navigate to the destination avoiding both the crowd and any other static obstacles, such as furniture that obstruct the path. The patient's velocity is sensed using the LIDAR leg detector U-Net model, as discussed in Section 4.1. An Intel Realsense D435i camera is loaded to the front of the robot, which uses Yolov5 [(56)] to detect humans and track them. The front laser actively scans for any obstacles in the environment. The patient's gait velocity, pedestrian spatial information, front laser readings, and the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Humans**} & \multicolumn{2}{c}{**Dynamic Object**} & \multicolumn{2}{c}{**Static Object**} & \multicolumn{2}{c}{**Contact Loss**} & \multicolumn{2}{c}{**Timeout**} & \multicolumn{2}{c}{**Success**} & \multicolumn{2}{c}{**Navigation Time**} \\  & \multicolumn{2}{c}{**Collision Rate (\%)**} & \multicolumn{2}{c}{**Collision Rate (\%)**} & \multicolumn{2}{c}{**Rate (\%)**} & \multicolumn{2}{c}{**Rate (\%)**} & \multicolumn{2}{c}{**Rate (\%)**} & \multicolumn{2}{c}{**Rate (\%)**} & \multicolumn{2}{c}{**(in s)**} \\ \hline
**Baseline** & **Proposed** & **Baseline** & **Proposed** & **Baseline** & **Proposed** & **Baseline** & **Proposed** & **Baseline** & **Proposed** & **Baseline** & **Proposed** & **Baseline** & **Proposed** \\  & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** & **Model V2** \\ \hline
2-7 & 5 & 36 & **8** & 4 & 2 & NA & 10 & 0 & 0 & 60 & **90** & NA & 11.44 \\ \hline
2-7 & 10 & 36 & **24** & 2 & 14 & NA & 12 & 0 & 0 & 60 & **62** & NA & 19 \\ \hline \hline \robot's current state are inputted into the RL model V2 (explained in Section 4.2.2). The model outputs the velocity values that need to be set on the robot. The model also outputs a predicted action value. The action value of the robot determines how good it is for the robot to be in a particular state or to do a certain action in that state. The concept of \"how good\" is described in terms of projected future rewards or, to be more accurate, expected returns. This video [[https://youtu.be/LqCMkvKuMSE](https://youtu.be/LqCMkvKuMSE)]([https://youtu.be/LqCMkvKuMSE](https://youtu.be/LqCMkvKuMSE)), (accessed on 30 March 2023) shows the model V2 controlling the robot in Gazebo. On the left side, a terminal is shown that constantly prints the action value predicted by the model. Initially, when the robot faces an obstacle and moves toward the obstacle, the action value is negative. However, once the robot adjusts its path and avoids the furniture, the values are positive. The robot is able to reach its goal without losing contact with the patient. As evident from the data presented in Tables 1 and 2, the contact loss rate of model V2 is not always zero. This highlights the importance of implementing measures to prevent contact loss, such as stopping the robot when the distance between the robot and the patient exceeds a certain threshold, such as 0.25 m. A flowchart illustrating the control system of the robot, including this safety feature, is depicted in Figure 8.  
Due to the COVID-19 pandemic and its impact on real-world robotic studies [57], a real-world evaluation is not yet possible. Our goal, in the future, will be to evaluate our models in a real-world lab environment to ensure safety, and then in a hospital environment.