## 2 Methodology
### Performance Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-5}  & & & **Reached the Target (\%)** & **Collisions (\%)** \\ \hline \multirow{2}{*}{1} & Proposed method & \multirow{2}{*}{SNN} & \multirow{2}{*}{94\%} & \multirow{2}{*}{6\%} \\  & (3 obstacles) & & & \\ \multirow{2}{*}{2} & [PERSON] [38] & & & \\  & [PERSON] [38] & & & \\  & [PERSON] [38] & Non-SNN & 91 & 9 \\ \multirow{2}{*}{4} & [PERSON] et al. [39] & CNN & 80.2 & 19.98 \\ \multirow{2}{*}{5} & [PERSON] et al. [40] & CNN-based vision model & 81.72\% & 18.28\% \\ \multirow{2}{*}{6} & [PERSON] et al. [40] & CNN-based vision model & 93.21\% & 6.79\% \\ \multirow{2}{*}{7} & [PERSON] et al. [40] & Backpropagation & \multirow{2}{*}{91.8\%} & \multirow{2}{*}{7.4\%} \\  & & Q-learning & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The last finding of the current study in comparison with the accuracy of obstacle avoidance of some counterparts. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Connection Words** & **Connection (Sensor, Motor)** & **Refer to Line Colors in Figure 9** \\ \hline tarL\textgreater{}MR & Left target sensor to the right motor & Blue \\ tarL\textgreater{}ML & Left target sensor to the left motor & orange \\ tarR\textgreater{}MR & Right target sensor to the right motor & Green \\ tarR\textgreater{}ML & The right target sensor to the left motor & Red \\ obsL\textgreater{}MR & Left obstacle sensor to the right motor & Purple \\ obsL\textgreater{}ML & Left obstacle sensor to the left motor & Brown \\ obsR\textgreater{}MR & Right obstacle sensor to the right motor & Pink \\ obsR\textgreater{}ML & Right obstacle sensor to the left motor & Gray \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of the relationship between Figure 9 and the sensor motors of the robot. Deep learning has transformed computer vision, which uses hierarchical models inspired by the human brain to improve recognition and cognitive tasks. However, its application in decision-making and control has been limited. To demonstrate the effectiveness of a hierarchical structure that combines a convolutional neural network (CNN) with a decision process, [PERSON] et al. 's study [39] focused on indoor obstacle avoidance. The study proposed a compact network that takes raw depth images as input and generates control commands as output, enabling model-less obstacle avoidance behavior. Real-world indoor environments were used to test the approach, which showed effective results. To demonstrate the effectiveness of the proposed strategy, we compared its efficiency with other state-of-the-art methods. According to the results presented in Table 4, [PERSON] et al. [39] obtained an overall accuracy of 80.2% after testing a deep-network solution to avoid obstacles in an indoor setting. In this regard, an experiment was conducted indoors, first with a mark and then without one, and the results were compared. The final test result demonstrates that our model can achieve high accuracy when avoiding obstacles for mobile robots. Furthermore, this level of accuracy may be improved by increasing the number of markings in the environment. In this article, authored by [PERSON] et al., a novel learning strategy for Autonomous Mobile Robots (AMRs) is proposed in Table 4. The strategy addresses obstacle avoidance in uncertain environments by utilizing a Reinforcement Q-Learning (RNQL) algorithm. A Single Hidden Layer Feedforward Network (SLFN) is employed as a Q-function approximator, with the SLFN's parameters being tuned by the OS-ELM algorithm. Initial output weights are estimated under a quadratic inequality constraint. Compared to the BPQL algorithm, RNQL demonstrates superior learning performance, evidenced by faster convergence, reduced training time, and enhanced generalization ability [41]. We look at the results with a few other outcomes to illustrate that this tactic is more successful than others. For example, [PERSON] et al.