The mining industry desires to cut costs and to operate in more dangerous mines, which is why companies such as Atlas Copco are developing autonomous vehicles. The problem to navigate autonomously is however complex, so the vehicles have in the recent years become more intelligent; the number of computers, actuators and sensors are increasing. For example, an autonomous LHD (Loading – Hauling – Dump) vehicle has sensors including: inertial measurement unit (IMU), odometer, hinge angle sensor, laser range finders and cameras. The parameters for the sensors needs to be calibrated before the vehicle can be used in a mine. There are also a number of electrical currents which needs to be calibrated for the actuators.The calibration of parameters has traditionally been made manually, but Atlas Copco realizes that manual calibration is not feasible once the sales of intelligent mine vehicles increases. Effort is therefore put into automation of the calibration procedures.Interviews with employees were carried out at Atlas Copco to identify the most time consuming procedures during calibration and installation of autonomous mine vehicles. The calibration of steering currents was not only identified as the most time consuming procedure, but also as one of the most complex procedures.The goal of this thesis is to enable easier and quicker installation of mine vehicles. This is done through investigation of methods for automatic calibration of steering currents. The problem is approached from two angles: a grey box model using system identification and a black box model using neural network with resilient backpropagation. The models are compared to a search algorithm, used for simulation of the manual calibration method. In the end, the models are evaluated with regard to performance and ease of implementation.The hypothesis was that the more complex grey box or black box model would have higher accuracy than a simple search algorithm. However, the search algorithm proves to outperform the other models both with regard to accuracy and calibration time, and is also easier to implement. The search algorithm is thus suggested to be implemented instead of a complex model. Moreover, it is suggested that a straightforward mapping of 20 currents may outperform even the search calibration. It is also concluded that calibration of steering currents can be done when the vehicle is standing still.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-91034 |
| Date | January 2013 |
| Creators | Lerider, Malcolm |
| Publisher | Linköpings universitet, Institutionen för datavetenskap, Linköpings universitet, Tekniska högskolan |
| 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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