Researchers in Kazakhstan recently developed a smart safety system for underground miner tracking. The machine learning-based technology uses Wi-Fi spot signals and inertial measurement unit (IMU) sensors to track miners’ whereabouts underground, and could drive up safety standards in mines worldwide.
Electronic Underground Miner Tracking Systems
Electronic tracking systems are making their way into increasingly more mines every year. But currently available electronic methods such as GPS and Wi-Fi tracking still have some limitations. Underground and open-cut mines present terrain restrictions for GPS and Wi-Fi sensing devices. Similarly, these systems struggle in disaster zones where the existing infrastructure has been destroyed. Reader-based tracking can overcome these limitations. Using the same radio frequency identification (RFID) that prevents retail theft, mines can create ultra-low-cost electronic tracking systems over relatively small-scale operations. Node-based electronic tracking technology creates a communications link between the radio and nodes. A chip analyzes the signal strength coming from a radio carried by the miner and determines the miner’s distance from a number of nodes around it to triangulate the miner’s location in three-dimensional space. One proposed technology for miner tracking is known as inertial navigation or inertial guidance. Systems like these are carried by miners and passively measure accelerations, turns, and so on to calculate the miner’s distance from a fixed start point.
New Algorithm for Wi-Fi and IMU Method Could Be Used in Underground Mining Safety
Researchers at the Nazarbayev University’s Institute for Smart Systems and Artificial Intelligence in Kazakhstan recently developed a neural network algorithm designed to improve Wi-Fi-based localization and tracking systems. The researchers say that their method, which combines data from Wi-Fi localization with IMU sensors data gathered in a handheld device, could improve mining safety by making a cost-effective smart tracking method more accurate, even with a poor Wi-Fi signal.
The algorithm was trained initially within the university building, which researchers used to simulate an underground mine. By sending pairs of “workers” around the building, the team established a dataset with geospatial information from three floors using only information from Wi-Fi signals. They continued to “train” the algorithm that was generating a model of the building until it had an error distance of approximately 2.5 meters. At this point, the researchers added data from IMU sensors to the algorithm’s feed. IMU sensors will mean the tracking devices will still be able to localize themselves when the algorithm feed is offline by calculating the distance it has been displaced through IMU signals.
This can give an overall estimate of the device’s position, which will become more accurate over time as the algorithm matures. IMU sensors are already found in smartphones and pedometers. They are relatively inexpensive devices used to measure acceleration relative to the motion of the earth to calculate location. The research is now focused on gathering more data to train the artificial neural network algorithm using machine learning. This process involves walking around the university building with handheld devices, feeding Wi-Fi access and IMU information back to the neural network.
Sources:
Azomining.com
https://www.azomining.com/Article.aspx?ArticleID=1694 .
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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