## 2 Methodology
### Performance Evaluation Metrics
These dynamic adjustments in synaptic weights illustrate the capacity of the SNN to continually refine its connections, enabling the robot to adapt and learn in response to sensory input and motor actions as it navigates its complex environment. The arrangement of synaptic weights plays a crucial role in shaping the behavior of a robot and decision-making process. These weights dictate how sensory inputs impact motor activation by strengthening or weakening specific sensor-motor connections. This enables the robot to learn and adapt its responses, allowing it to navigate and overcome challenges in ever-changing environments. During training, the synaptic weights are adjusted based on input and output signals, enhancing the ability of the robot to handle dynamic situations effectively. Figure 9 explains the connection between the sensor motors of the robot in Table 3. These linkages rely on synaptic weights that significantly affect the decision-making abilities and behavior of the robot. These weights are adjusted during training to improve the robot of performance and determine the neural network weights. When the synaptic weight is increased, the neural network prioritizes the motor input of the sensor, and higher neural network weight correlates the sensor input with the planned output. The mobile robot has learned to prioritize sensor input when making decisions or directing its behavior. As a result, the sensors significantly impact the motor responses of the robot, and the synaptic weights play a crucial role in enabling the robot to adapt and learn from its environment. Figure 9: Synaptic weights between the sensors and motors in the SNN of a mobile robot during the training phase. ## 4 Verification with Counterparts

In this section, we compare our final results with six other articles that explored similar approaches to training and testing autonomous robots. The accuracy of our model is an impressive 94%, outperforming the results presented in all other articles. This highlights the effectiveness of our training methodology and the robustness of our model in accurately reaching the target. Additionally, our model demonstrates a considerably reduced collision rate, with only 6% of interactions resulting in obstacles. It is worth noting that the average experiment duration was 1000 simulated seconds. This achievement is notable compared to the collision rates reported in the other articles, ranging from 6.79% to 19.98%. These results indicate that our model can navigate through obstacles and avoid collisions, further highlighting its practical applicability in real-world scenarios. The comparison presented in Table 4 and Figure 10 demonstrates the advancements made in our study, positioning it as a significant contribution to the field. The combination of high accuracy and low collision rates places our model at the forefront of autonomous robot navigation research, paving the way for safer and more efficient robotic systems. In Table 4, we compared the effectiveness of our procedure with others by conducting an experiment that involved two different types of neural networks: spiking and non-spiking. The SNN used the LIF model, while the non-SNN utilized the McCulloch-Pitts neuron model [38]. [PERSON]'s study tested the SNN, which had 100 neurons, in 1000 scenarios. The SNN reached the target in 842 cases, achieving an 84% accuracy rate. However, it also collided with obstacles 16% of the time, resulting in 158 collisions. On the other hand, the non-spiking network, also with 100 neurons, outperformed the spiking network by reaching the target successfully in 914 cases, achieving a 91% accuracy rate with only 86 collisions (9% of cases). Both networks had sensory inputs from IR sensors, an odometer, and a compass, and they shared a single challenge and objective [38].