## 2 Methodology
### Performance Evaluation Metrics
Following these steps improves the navigation abilities of the robot until it successfully reaches the target while effectively avoiding obstacles. The thresholds for target accuracy and obstacle distance may vary based on system implementation and requirements. One approach is to use predefined thresholds based on prior knowledge or expertise. These thresholds can be set to achieve the desired level of accuracy and safety. For example, a small target error threshold can be set for precise target position information. Similarly, the obstacle distance threshold can be determined based on the physical dimensions of the robot and the operating environment. Another approach involves adaptive thresholds that dynamically change during the learning process. Initially, high thresholds promote exploration and learning. As the robot gains experience and improves navigation, the thresholds gradually tighten to encourage more accurate target reaching and obstacle avoidance. Adaptive thresholds can be determined by analyzing the robot of performance over multiple experiments or using reinforcement learning to optimize values. In some cases, thresholds can be determined through trial-and-error experimentation. The robot undergoes multiple iterations of the task with different threshold values, and the results are measured to adjust the thresholds for optimal navigation behavior iteratively. When setting thresholds, it is important to balance accuracy and robustness. Very strict thresholds can make the robot overly cautious and hinder navigation in complex environments. On the other hand, lenient thresholds can lead to imprecise navigation and frequent collisions. The choice of threshold values depends on the navigation requirements and the desired balance between accuracy and efficiency. Significant advancements have been made in optimizing our robotic system, resulting in enhanced adaptability and performance. One key aspect of this optimization involved fine-tuning hyper parameters, which are crucial coefficients governing neuronal currents. Through experimentation and triangulation, an optimal balance was achieved to ensure stable firing patterns without the issues of non-firing or excessive bursting. Additionally, a novel algorithm was introduced to address a challenge during learning phases. Unlike previous setups, this algorithm maintains stability, preserves firing patterns, and enhances system reliability. These strategic modifications have had a positive impact on the overall performance of the robotic system, as demonstrated in Figure 4.

### The Robot's Navigation and Obstacle Avoidance Performance

In this study, a mobile robot is trained to navigate an unfamiliar environment with obstacles and reach a specific destination. The proposed approach is evaluated using testing, which collects data from various viewpoints to understand the behavior of the robot in the simulated environment. This allows for a detailed analysis of the robot of performance and can help improve its control system. Figure 5A,B depict the training and testing stages to teach the robot how to navigate and reach the desired destination. The Webots Simulator 3D is used for testing, providing a realistic simulation environment. Multiple data are collected to observe the actions of the robot in the simulated environment. Figure 5: (**A**) The robot during the training phase to teach, (**B**) The robot during the testing phase to reach the target. During the training process, we initiate by initializing all synaptic weights to one. However, as time progresses, there is a notable transformation depicted in Figure 6B, illustrating a significant and meaningful distinction between the left and right motor neurons with sensors. These alterations indicate that the system is functioning effectively. Consequently, the robot exhibits specific reactions to targets or obstacles. The firing pattern, illustrated in Figure 6C, elucidates how motor neurons adhere to a rule during training, selectively activating only a specific group of motor neurons.