# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Overview of Swarm Model Performance  
The performance values for the swarm models were determined based on simulation results, with the lowest harvesting time used as a standard metric. The honeybee model, which achieved the shortest harvesting time, was set as the benchmark for efficiency, scalability, and selectivity, with its performance value normalized to 1.0. For instance, the honeybee model took 55 s to complete a selective mining task, so this was set as the highest performance value (1.0). For comparison, the ant model, which took 360 s for the same task, received a performance value of 0.40 calculated as 55/360. This scaling method facilitated a comparative analysis of the models' performances across various aspects such as efficiency, reliability, selectivity, and scalability. The spider graph presented in Figure 15 visually represents these scaled metrics, highlighting relative strengths and weaknesses of each swarm model.  
According to the swarm model performances presented in the spider graph analysis in Figure 15, the honeybee model demonstrates superior capabilities in efficiency, scalability, and selectivity. This excellent performance is attributed to its hybrid decentralized-centralized control system, which provides prior knowledge about ore deposits, enhancing mining efficiency and selectivity for precise operations. Additionally, the honeybee model's  
Figure 14: Swarm-mining reliability performance for the four models.  
performance remains unaffected by environmental changes, as it does not rely on real-time environmental data. In contrast, the baseline, ant, and firefly models exhibit the strongest reliability due to their decentralized control systems. These models ensure that each robot operates independently, so if a robot fails or malfunctions, the overall process continues and is unaffected. Among these three models, the firefly model surpasses the ant model in efficiency, scalability, and selectivity because it incorporates a localization feature, such as a guide light, which optimizes the mining operation in addition to task allocation. To provide a clear overview of each swarm model's strengths in relation to specific mining tasks, key applications and advantages of each model are summarized in Table 3.  
Table 3 highlights the advantages and strengths of each swarm model in relation to specific mining tasks. The firefly model excels in ore detection, communication, and adaptability, making it ideal for operating in complex environments to achieve efficient exploration. The ant model is particularly strong in ore transportation, offering high reliability and effective task specialization, which ensures continuous operation with minimal interference. The honeybee model, best suited for selective ore mining, combines concentrated precision and dispersed adaptability, making it highly effective in targeting high-grade ore. This classification underscores the importance of aligning swarm models with specific mining tasks to enhance productivity and operational efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Swarm Model** & **Mining Task** & **Strength** \\ \hline Ant model & Ore transportation & High reliability and task specialization \\ \hline Firefly model & Ore detection & Robust communication and adaptability \\ \hline Honeybee model & Selective ore extraction & High precision and centralized control \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of swarm model applications and strengths.  
Figure 15: The performance overview for the four swarm models.