# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Performance and Resources Metrics
#### 5.7.3 CPU Utilization  
In this study the CPU utilization of four swarm robot models at different numbers of robots, i.e., 5, 10, and 15, has been tested. The computational requirements of each model have been tested over time, and the number of robots in the swarm have been taken into consideration to judge on how they scale. The tests were designed to determine how the considered different models operate as the swarm size increases, with a particular focus on the impact on CPU utilization, which is a key indicator of computational resources. The CPU utilizations for four swarm models for various swarm sizes are shown in Figure 18.  
Analysis of CPU utilization for the four swarm models showed that the computational demand greatly increases as the swarm size increases. The baseline model used the least CPU power, ranging from 50% to 87%. This is because its operation is simple and there is little communication between robots, so it can manage computational resources without burdening the system. The ant model had slightly higher CPU utilization, ranging from 75% to 119%, because its division of labor increases task allocation, balances complexity and resource consumption, and remains stable and controllable even under increased demand. The firefly model's beacon-based communication system required real-time asynchronous signals, which can cause sudden spikes in CPU demand, such as a sudden increase to 171%. Figure 18: CPU usage for four swarm models across varying swarm sizes of 5, 10, and 15 robots. As a result, the CPU utilization of this model was more complex and variable, ranging from 56% to 175%. These peaks show that the CPU demand of the firefly model becomes more unpredictable and difficult to control as the swarm size increases, and that swarm robots put pressure on the system when communicating simultaneously. The honeybee model used the highest CPU power, up to 250%, when there are 15 robots. Complex processes such as real-time coordination between the master robot and the other robots, recruiting, data sharing, and exploration lead to bottlenecks that require a lot of processing power, when the number of robots exceeds the system's processing capacity. If the master robot experiences a high computational load, it can slow down decision-making and introduce delays in the overall operation. This makes the Honeybee model highly resource-intensive and potentially less reliable in scenarios requiring the management of large swarms or complex environments. In cases where processing power or communication bandwidth is limited, the complexity of the hybrid system could reduce the overall system efficiency and stability. The high communication overhead of the honeybee model increases CPU consumption and compromises system stability, especially in large-scale swarm applications in real-world scenarios. A key component of the model's effectiveness is its communication method. While the baseline model's minimal communication achieved low CPU usage and system stability, the firefly and honeybee models rely on more complex communication systems, which resulted in a higher CPU utilization. The firefly model's beacon-based system added a lot of communication overhead, resulting in unstable CPU requirements, while the honeybee model's extensive data sharing required powerful computing resources. Due to the increased communication overhead, the CPU usage became more unstable as the swarm size increased, especially in the firefly and honeybee models. This affects the stability of the system. In real-world applications, large swarms may make the system unstable without sufficient processing power. Although the firefly and honeybee models are powerful, they still require careful management of resources to prevent system overload and maintain reliable performance. In summary, since the ant model and the standard model are more resource-efficient and scalable, they are more suitable for environments with limited computing resources.