## 5 Experimental Setup and Results  
We conducted multiple physical tests on the specially developed telepresence robot using ROS to validate the model's performance in practice. Numerous tests on the telepresence robot are conducted to reduce the error. The site is a cardiologist ward in the Government general hospital, Ghulam Muhammad-Abad, Faisalabad. The distance for traveling a telepresence robot from the initial point (doctor's office) to the endpoint (admitted patient ward) is 10.5 m. We use the Lidar to reconstruct the hospital environment. The telepresence robot starts its journey from point A in a hospital environment which is the main entrance of the reception area as shown in Figure 12. It drives through the main lobby while avoiding different static obstacles and reaches destination point B, which is the admitted ward of patients. Table 3 contains the significant parameters for the experiment environment, which shows the obstacle type: static, and the number of static obstacles is eight and 4 \(\times\) 4 feet in size. The total coverage path of the telepresence robot from starting A to endpoint B is 140 m. The teleoperator and assistance from the DRL based autonomous are controlling the telepresence robot. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Static obstacles & 4 \(\times\) 4 feet & 8 \\ Dynamic Obstacle & - & - \\ Path & Curved & 1 \\ Starting Point & - & A (Main entrance) \\ End Point & - & B (Patient Ward) \\ Coverage & meters & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental significant parameters. Figure 12: Lidar-based indoor map of the hospital and path of a telepresence robot. We modify the scanning function to automatic in the gmapping package of ROS to gather the data from the scanner while traveling the telepresence robot from the initial point to the endpoint in the hospital. We performed parameter training based on the hospital environment at the initial stage. Other algorithms have been trained for a certain period except for A*. After receiving all the data, the trained model is fed into the package ROS in the telepresence robot. Then we conducted four verification trips, and each trip was restarted after completing the previous trip. Each trip consists of three different trials, and the mean is established on those trips' trial data, as discussed in Table 3. By comparing the mean column of the three different trials, the result obtained from the proposed framework-based experiments is good than the other two algorithms' experiments in the context of trip length and time consumption. The telepresence robot controlled by the teleoperator makes multiple trips with different measured distance trials from Point A to point B consisting of varying time durations. After learning from the teleoperator-based generated data, and in the scenario of delayed controlling signal, the telepresence robot turns its autonomous mode to move to the desired path by itself. We implement this with three different algorithms, A*, DDQN, and our proposed GRU-integrated DDQN algorithm. We note the cumulative distance covered in a particular time duration, as written in Table 4.  
According to this equation, the maximum discounted reward by adhering to that optimal policy for the future state-action pair \((s^{\prime},a^{\prime})\) will be equal to the predicted reward \(\mathrm{R}_{t+1}\) after executing that action in addition to the optimal Q value function for the given state-activity pair \((s,a)\). The ideal Q value is approximated in a complicated environment using a non-linear function approximator, such as a neural network. A value-based reinforcement learning technique is called Q-learning. To find the best course of action, Q-learning modifies the Q value of each action-state combination using the Bellman Equation. Furthermore, a dual network structure can lessen the correlation between the present and target Q values and increase the method's stability.