# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application  
[PERSON]  
1 Discipline of Mining and Petroleum Engineering, School of Chemical Engineering,  
The University of Adelaide, Adelaide 5005, Australia; [EMAIL_ADDRESS]  
2 School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia;  
[EMAIL_ADDRESS]  
3  
[PERSON]  
1 Discipline of Mining and Petroleum Engineering, School of Chemical Engineering,  
The University of Adelaide, Adelaide 5005, Australia; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia;  
[EMAIL_ADDRESS]  
[PERSON]  
2 School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia;  
[EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf](https://www.mdpi.com/journals/article/pdf)]([https://www.mdpi.com/journals/article/pdf](https://www.mdpi.com/journals/article/pdf))  
Full automation remains a challenge in the mining sector, highlighting the need for further technological advancements. One promising solution is the application of swarm robotics, inspired by the collective behaviors of biological systems like ants, honeybees, and fireflies. These systems excel in decentralized decision-making and self-organization, potentially offering significant improvements in scalability, flexibility, and reliability for automated mining systems [6]. Swarm robotics has already found applications in various fields, including agriculture, medicine, and construction. For example, the Swarm Robotics for Agricultural Applications (SAGA) project optimizes farming tasks using honeybee-inspired models [7]. [PERSON] et al. (2022) reported that a small-scale unmanned vehicle, using an automated navigation system with infrared sensors and Bezier curves, navigates, monitors, and harvests plantations with narrow pathways, reducing the need for manual labor [8; 9]. In medicine, millirobots mimicking caterpillars or jellyfish have been developed for intricate medical procedures such as arteries navigation, targeted drug delivery, and internal examinations of the human body [10]. In construction, fiber-bots and KALI are inspired by particle swarms and termite colonies, respectively [11; 12]. Swarm robotics are also used in rescue operations, transportation, logistics, and other domains [13]. However, despite its success in these areas, swarm robotics remains underutilized in mining [14]. Preliminary studies have explored its potential in mining operations, particularly in ore detection, transportation, and exploration within simulated environments. Research has demonstrated the benefits of decentralized control and cooperative behavior in mining tasks [15]. [PERSON] et al. (2014) highlighted the advantages of using unmanned vehicles in coal mines for ore detection and mining tasks [16]. Similarly, [PERSON] et al. (2021) [15] proposed a swarm robotic system for extraterrestrial mining using the ABCO method, showing how nature-inspired swarm behaviors can enhance mining efficiency and reliability. The study by [PERSON] et al. (2016) emphasized the applicability of swarm behaviors in mining tasks and large-scale robot swarms for ore detection [17]. The use of nature-inspired algorithms has been widely studied and applied to various optimization problems in the mining industry, such as inaccurate drill hole positioning and mineral estimation, long-term scheduling challenges under grade uncertainty, inefficiencies in production rate due to fixed dispatch methods, and inefficiency of traditional methods in meeting high-quality data and computational resources [18]. Although automation technologies like AHS and ADS have advanced significantly [2], full automation has not been achieved yet.