## 2 Methodology
### Performance Evaluation Metrics
The system calculates two error distances: one between the robot and the target, and another between the robot and obstacles. Spatial or ultrasonic sensors are used for this purpose. If the distance to obstacles exceeds a predefined threshold (0.1 cm), the system evaluates proximity and adjusts navigation actions to avoid collisions. By integrating sensor data and using threshold-based evaluations, the framework enhances the robot's navigation capabilities. This sensor-driven decision-making process is critical for successful navigation in dynamic environments. Step 4: Reward-Punishment Mechanism

The robot of performance depends on sensory data, particularly the target error and obstacle distance. The target error reflects the difference between the position of the robot and the desired target position, while the obstacle distance measures the proximity of the robot to obstacles. If the target error is below a specified limit, the robot receives a dopamine reward, strengthening the synaptic connections related to successful navigation. Conversely, negative dopamine is given as punishment if the obstacle distance exceeds a specific threshold, indicating a collision. This punishment weakens the synaptic connections associated with collisions, discouraging the robot from repeating the same behavior. These feedback signals adjust the weights between neurons in the learning algorithm, guiding the navigation of the robot. * Step 5: Motor Babbling for Exploration

If the robot fails to reach the target due to not meeting the specified threshold, it utilizes \"motor babbling\". This technique involves introducing random motor actions to the movements of the robot, allowing it to explore different motor actions and potentially discover new navigation strategies. By introducing variability, the robot is encouraged to explore its capabilities and the environment, enhancing its navigation paths and overall performance. * Step 6: Firing Patterns and Synaptic Weight Adjustment

The firing patterns of sensor and motor neurons are essential for modifying synaptic weights. During training, the weights between sensory and motor neurons change based on firing patterns and reward or punishment signals. Successful firing patterns leading to successful navigation strengthen corresponding connections, while patterns resulting in collisions weaken those connections. This process helps the robot learn navigation strategies, reinforcing success and inhibiting detrimental patterns. * Step 7: Motor Neurons and Location Calculation

Firing motor neurons is crucial for navigation. These neurons activate and control the movements of the robot. Analyzing neuron firing patterns allows us to determine the location of the robot using inverse kinematic equations. This information helps the robot make decisions and adjust its movements based on obstacles and the target. The direct link between neural activity and physical movements enables precise control over navigation. * Step 8: Iterative Experimentation

The process involves multiple iterations of experiments, where the robot receives reward or punishment signals based on its task performance. The synaptic weights are adjusted accordingly, and the modified weights are used in subsequent experiments. The number of experiments (or \"time\") increases with each iteration until the robot successfully reaches the target or collides with an obstacle. The experiment concludes when the number of iterations exceeds a predetermined threshold, indicating successful navigation. The navigation skills of the robot improve by continually refining synaptic weights, increasing its chances of reaching the target and avoiding obstacles. It is worth noting that the experiment duration was 1000 simulated seconds. The process involves procedures enabling a robot to move towards a specific destination while avoiding obstacles. It involves calculating sensor information, adjusting weights, exploring different movements, and learning through trial and error.