Underground Mine Terrain Obstacle Detection based on Multi-Robot System with Swarm Intelligence
Keywords:
Obstacle detection, Multi-Robot System, Swarm Intelligence, QLACSAbstract
The sheer number of disasters that strike underground mining regions every month all over the world is hard to ignore. For instance, some of these tragic events comprise roof falls; other probable causes of injury or death involve crashes, breathing toxic gas, in-mine automobile crashes, etc. However, it remains difficult for firefighters to respond rapidly when similar instances arise during mining projects. This renders it vital to use multi-robot systems to seal the space between the products acquired in dark mines and the livelihoods of the miners. This study proposes an autonomous multi-robot cooperative behavioral concept that may help in steering multi-robots for the safety inspection of underground mines instead of humans. We offer a detailed examination of the feasibility of our proposed framework in two real-world circumstances like observing rock falls and spotting gas levels in deep mines. This questionnaire can be utilized as a source of information for further study of supportive behavioral models and safety administration for underground mines. It additionally has the potential to contribute to conducting additional studies on current approaches to make them more scalable, trustworthy, and productive, which will boost adoption in larger mines and fields. A QLACS paradigm relying on an Ant Colony System (ACS) and Q-Learning (QL) is conceptually built by the architecture. With the objective of establishing an efficient approach for accomplishing pre-emergency and disaster restoration within the coal mine, the scalable QLACS has been investigated using multiple robots. The final result of the performance assessment reveals that the QLACS is extendable to n-based MRS and continually durable in terms of communication and search expenditures.
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