# NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance
## 4 Results
### Static Environment  
In a critical analysis of the scientific literature related to mobile robotics, a variety of algorithms implementing DRL can be observed, as shown in Table 5. It is important to note that most of these algorithms are primarily limited to simulation environments and have not been extensively tested in physical robotics. An exceptional case in Table 5 is the DQN-based algorithm proposed in [9], which has demonstrated its applicability in physical robotics under real-world environments. However, despite its real-world utility, the work of [9] has notable limitations in terms of efficiency. Specifically, this algorithm achieves an accuracy of only 60% after extensive training over 1500 epochs. In contrast, the proposed NRNH-AR algorithm surpasses this performance. With a considerably shorter training period of just 210 episodes, our algorithm achieves an accuracy of 97.62%. This superior accuracy demonstrates the robustness and superiority of NRNH-AR compared to the DQN-based algorithm. It is worth noting that the environments used for each method are different from each other. Furthermore, it is important to highlight that existing research in the field of mobile robotics often relies on high-end hardware, as depicted in Table 5. While this may enhance the effectiveness of simulations, its practical feasibility in mobile robotics with limited resources is questionable. Therefore, it is crucial to continue developing and optimizing algorithms like NRNH-AR, that not only exhibit superior performance, but are also practical and viable for implementation in mobile robotics. Table 5 presents a consolidated comparison of selected DRL-based methods in mobile robotics to complement the narrative review provided in the Introduction. While similar \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Hardware** & **Software** & \begin{tabular}{c} **Obstacle** \\ **Evasion** \\ \end{tabular} & *** Episodes** & *** Accuracy** & **Simulation** & \begin{tabular}{c} **Physical** \\ **Robot** \\ \end{tabular} \\ \hline \begin{tabular}{c} GNND \\ Multi-Robot \\ [21] \\ \end{tabular} & \begin{tabular}{c} 3.2 GHz i7-8700 CPU and \\ an Nvidia GTX 1080 Ti \\ GPU with 32 \\ \end{tabular} & 2D cluttered & Yes & \(3.0\times 10^{4}\) & 99.50\% & Yes & No \\ \hline \begin{tabular}{c} ISA [19] \\ \end{tabular} & Velodyne VLP-16 & SemanticKitti & Yes & \(5.6\times 10^{6}\) & 52.50\% & Yes & No \\ \hline \multirow{3}{*}{\begin{tabular}{c} FAM-HGNN [18] \\ \end{tabular} } & 2.2 GHz i7-10870 CPU & \multirow{3}{*}{\begin{tabular}{c} OpenAI gym \\ GPU with 6 GB \\ \end{tabular} } & \multirow{3}{*}{Yes} & \multirow{3}{*}{\(1.0\times 10^{7}\)} & \multirow{3}{*}{\(96.00\%\)} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} \\ \cline{1-1} \cline{5-5} DQN algorithm [20] & \begin{tabular}{c} Intel core i7 8 GBRAM, \\ Nvidia 940 mx \\ \end{tabular} & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} DenseCAvoid [22] \\ \end{tabular} } & \begin{tabular}{c} Intel Xeon 3.6 GHz \\ processor and an Nvidia \\ GeForce RTX 2080 Ti GPU \\ \end{tabular} & & & & & & \\ \hline DQN algorithm [9] & Raspberry Pi 3 & ROS Gazebo & Yes & 1500 & 60.00\% & Yes & Yes \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Ours \\ \end{tabular} } & \multirow{3}{*}{Jetson Nano 4 GB 64 bits} &
\begin{tabular}{c} Python \\ 3.12.5 \\ \end{tabular} & & & & & & \\ \hline \hline \end{tabular} Note.: * The Episodes and Accuracy of each method were reported by the authors in different environments using different targets.  
\end{table} 
Table 5: Methods of DRL in robotics in static environments. points are discussed earlier, this table allows for clearer quantitative comparison across hardware and training configurations. Figure 11 illustrates the episode-wise rewards obtained by the standard deep reinforcement learning algorithms, DQN (yellow), TD3 (green), and DDPG (orange), compared to the DRL stage of the proposed NRNH-AR model (navy blue) across 210 training episodes in the environment shown in Figure 7a.