## 2 Methodology
### Performance Evaluation Metrics
Additionally, the time step for encoding sensor signals into the network and the time steps for decreasing reward/punishment values or synaptic weight derivation values are important parameters that significantly impact the network's learning performance. ### Optimizing Control Systems for Mobile Robots

This section will delve into a flowchart thoughtfully designed to investigate the combined influence of proprioceptive feedback and learning algorithms on SNN-based mobile robot control systems operating in unfamiliar environments. Our exploration begins with an experiment in which robot, target, and obstacle positions are randomly generated

Figure 4: The flowchart of the proposed system. and systematically assessed for effectiveness. Figure 4, presented below, showcases the new flowchart, which introduces a structured and comprehensive approach to conducting this study. This flowchart underscores the significance of using structured visual representations to explore complex systems like mobile robot control systems. It is essential to highlight that the findings of this study hold the potential for replication and further development by other researchers. Figure 4 illustrates the steps to achieve the system's objectives. The distance between the robot, target, and obstacles is calculated using sensors. Accurate sensor readings are crucial for determining the experiment's success, as they trigger dopamine or adverse dopamine into the system, acting as a reward or punishment. This helps the system learn and adapt its behavior based on sensory inputs. The diagram utilizes sensor input to calculate target and obstacle errors, which modify synaptic weights. The firing patterns of sensor and motor neurons are crucial in this process. The branch that calculates target error determines if the robot has reached its target, rewarding dopamine if the error is below a certain threshold. If the error is higher than the threshold and the robot collides with an obstacle, adverse dopamine is given as punishment. Both reward and punishment contribute to modifying synaptic weights. Figure 4 shows a flowchart that explains the process clearly. It highlights important steps such as time management, sensory data calculation, reward-punishment mechanisms, motor skill exploration, synaptic weight adjustment, and iterative learning. This research aims to provide insight into the complex process that allows robots to achieve their objectives while skillfully avoiding obstacles, with a specific focus on our system. Step 1: Time Frame

This simulation divides time into milliseconds (ms) and seconds (s). Milliseconds are used to update neuron firing (as mentioned in Algorithm 1) and robot location for precise and timely updates. Seconds are used to average synaptic weights, providing smoother representation for plotting and analysis. This approach allows for precise and efficient simulation of the navigation process of the robot. Step 2: Experiment Initialization

Each experiment randomly defines three crucial elements: the initial position of the robot, the target position, and the obstacle position. These positions determine the starting conditions for the navigation task of the robot. Randomizing these positions creates various scenarios to evaluate the efficiency of the SNN-based learning algorithm. This variability allows the robot to learn and adapt to different navigation challenges. Step 3: Sensor Calculations

Sensors, such as ultrasonic and color detection sensors, are essential for a robot's navigation process. Ultrasonic sensors emit sound waves and measure their reflections to calculate distances between the robot, the target, and obstacles. Color detection sensors analyze object colors to classify the color of the obstacles and the color of the target, providing additional information for navigation. Sensor data are crucial for decision making, including obstacle detection and proximity assessment of the target.