## 2 Methodology
### Performance Evaluation Metrics
[40] performed better than [PERSON] et al. [39] As shown in Table 4, the findings can be broken down as follows: [PERSON] et al. 's work [40]

Figure 10: Accuracy and collision comparison [38; 39; 40; 41]. achieved a higher accuracy of 93.21% in reaching the target, while [PERSON] et al. [39] had an accuracy of 81.72%. Additionally, [PERSON] et al. [40] experienced fewer collisions, with only 6.79% of the attempts resulting in collisions, compared to [PERSON] et al. 's [39] 18.28%. These results suggest that the approach or methodology employed by [PERSON] et al. [40] was more effective in reaching the target and avoiding collisions. It is essential to mention that [PERSON] et al. 's research centers on how to help a mobile robot avoid obstacles using the backpropagation Q-learning algorithm [41]. In Table 4, the performance of two learning algorithms, backpropagation and Q-learning, is shown in terms of accuracy. The accuracy is measured by \"reaching the target\" and \"collisions\". As per the table, the backpropagation algorithm achieved a 91.8% accuracy rate for reaching the target but had 7.4% collisions. These numbers demonstrate the effectiveness of backpropagation in navigating to the target accurately while minimizing collisions. However, the Q-learning algorithm does not provide specific accuracy percentages for reaching the target and collisions in the table. We conducted various comparisons to determine if our findings could enhance the ability to avoid obstacles, further supporting the use of SNNs in robots. SNNs can improve robotic intelligence by mimicking the mechanisms of the brain, leading to faster speeds, energy efficiency, and computational capabilities. This article explores the biological evidence and motivations behind SNNs in robotics, modeling, and training approaches. We also discuss common modeling methods, including neurons, synaptic connections, and networks. As [PERSON] et al. [42] described, SNNs have two types of learning solutions based on the Hebbian rule and Reinforcement Learning [40]. Table 4 presents a comparative analysis of various algorithms utilized for the control and navigation of an Autonomous Mobile Robot (AMR) in the presence of obstacles. Each entry in the table corresponds to a distinct reference, algorithm, and associated performance metrics. Notably, the proposed method, employing an SNN and a sensor setup consisting of ultrasonic and color detection, demonstrated remarkable efficacy. It achieved a 94% success rate in reaching the target, coupled with a low collision rate of 6%. Comparatively, [PERSON]'s SNN, relying on a camera sensor, achieved an 84% success rate and a 16% collision rate. The non-SNN algorithm by [PERSON] performed with 91% accuracy in reaching the target and a 9% collision rate. Additionally, Convolutional Neural Network (CNN) algorithms, such as those by [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [41] showcased varying degrees of accuracy and collision rates, each employing camera sensors for perception. These findings offer valuable insights into the strengths and limitations of different algorithms in the specific context of AMR navigation amidst obstacles. ## 5 Conclusions

This study presents an innovative approach for training and controlling AMRs in unknown environments. The strategy employs a customized SNN that integrates biologically inspired principles, such as STDP with dopamine modulation and the Izhikevich neuron model, for autonomous learning and control. The proposed system is extensively simulated in the Webots robotic environment to test the algorithm. The results demonstrate the effectiveness of the approach in achieving high accuracy and minimizing collisions. Specifically, the SNN algorithm achieves an accuracy of 94% in reaching the target with only a 6% collision rate when faced with three obstacles after 1000 simulated seconds. These findings highlight the method's superior navigational capabilities for AMRs.