Positioning in environments where GPS is absent is a field currently under intensive research.Systems are currently being researched or designed for indoor use, often relying on ultra-wideband radio, ultrasound, fingerprinting or Wi-Fi.For underground mining, the problem is magnified, as installation of new equipment is expensive.Mobilaris Mining and Civil Engineering AB supplies a service, Mobilaris Mining Intelligence, using existing Wi-Fi infrastructure present in many mines for communication, and has developed two Wi-Fi-based positioning methods and one hybrid system, using dead reckoning and gyroscope.The first positioning method, Positioning Method 1, positions resources at the location of the strongest access point.The other positioning method, Positioning Method 4, uses signal strength values to construct an area where the tag is likely to be, similar to a Venn diagram. This thesis proposes a Quality of Positioning system to dynamically and select the best of all available positioning systems for every object to be positioned.This should be trained automatically by ``light vehicles'', such as service pickup trucks, equipped with the hybrid positioning system acting as reference values.Testing was done at the Kristineberg Mine in Västerbotten, Sweden, using a pickup truck equipped with the hybrid positioning system and Wi-Fi personnel positioning tags.It was found that the difference between the two positioning methods was not statistically significant, and that the hybrid positioning system was insufficiently accurate to act as a reference value. This thesis further revealed that the architecture of Mobilaris Mining Intelligence makes implementing a dynamic system impractical.Although planned for, the dynamic Quality of Positioning system was not implemented due to being deemed too impractical, complex and time-consuming compared to the benefit it would have provided.A high-level description of such an implementation is however presented, should it be motivated by future studies.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-66744 |
| Date | January 2017 |
| Creators | Grönlund, Fredrik |
| Publisher | Luleå tekniska universitet, Datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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