# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Mining Selectivity  
Selective mining is a critical approach in the mining industry, focusing on extracting high-quality minerals to maximize profitability and meet market demands. This method prioritizes high-grade ores and leaves low-grade ores behind unless market conditions make their extraction economically viable. In underground mining, room-and-pillar mining typically leaves pillars made up of gauge or low-value material to support the roof while extracting high-grade ores. Similarly, cut-and-fill mining prioritizes high-grade ores, using the extracted gangue or low-grade ores as a backfill material. Longwall mining cuts along the ore seam to extract higher-quality ore, leaving low-grade ores for structural support.  
In this study, the implementation of selective mining was evaluated within a swarm model, focusing on tasks like distinguishing between the low-grade ore goethite and high-grade ore hematite blocks during extraction. The experiment involved 10 robots processing 20 randomly dispersed ore blocks with 10 high-grade hematite ore blocks marked in red and 10 low-grade goethite ore blocks marked in green. Each robot was equipped with a LiDAR sensor to detect the color of the ore blocks, allowing them to differentiate between high-grade and low-grade ores based on color. This method is commonly used in swarm robot simulations to test their capabilities, emphasizing the dynamic interactions within swarm models rather than the specific technology used for ore quality detection. The results of these selective mining tests are illustrated in Figure 14.  
This section evaluates the precision and accuracy of the swarm models in mining high-grade deposits (hematite ore), focusing on their selective mining protocols. As shown in Figure 14, the honeybee model outperformed the others, with an average selective mining time of 55 s, compared to 158 s, 360 s, and 237 s for the firefly, ant, and baseline models, respectively. The baseline, ant, and firefly models, which use decentralized and behavior-based control, rely on real-time environmental data to reach consensus. However, their selective mining protocols did not significantly improve performance due to limitations in the perception radius, resulting in redundant exploration and wasted effort. Without centralized control, these models lack prior knowledge of high-grade ore locations, leading to longer mining times, increased robot numbers for high-grade ore detection, and reduced efficiency. Decentralized control also causes issues like congestion, inconsistent environmental cues in the firefly model, and workload imbalance in the ant model, where some robots mine low-grade ores while the others search for high-grade ones, leading to inefficient task allocation. The absence of centralized data interpretation and command issuing further exacerbates inefficiency, as robots search without a coordinated strategy. In contrast, the honeybee model uses centralized control, offering distinct advantages for selective mining. Scout robots explore the area and relay ore deposit information to the master robot, which, like an onlooker bee in nature, selectively directs mining robots to high-grade ore locations based on mineral quality and resource data. This centralized approach leverages prior knowledge, eliminating redundant searches and directing robots straight to high-grade ores, significantly improving mining efficiency and profitability. The time-based adjustments according to mineral quality ensure that the honeybee model prioritizes the high-grade ore, making it particularly effective in room-and-pillar mining, where selective extraction is crucial for maintaining structural integrity.