669
5 CONCLUSIONS
Geo-referenced three-dimensional models of a mine can be generated from the nadir
aerial imagery with resolution comparable to other scanning methods. Resolution of
the generated point cloud and orthomosaic from the first stage flight is not sufficient
to be used for measuring tension cracks. Resolution of the models can be improved by
increasing the size of image sensor resolution or decreasing the flight altitude or both.
Therefore, a second terrain-aware flight has been planned for getting high-resolution
images in this study.
An empirical study was performed to model the battery performance of a test UAV considering various flight circumstances. This way, a linear regression model for the power consumption of the drone was derived and validated for a test scenario. Further, an optimization
algorithm was proposed to solve the problem of path planning which resulted in a path which
consumes the least battery power. A case study based on an open pit mine from Nevada was
presented. The algorithm was tested to find out the optimum path for high resolution imaging of the benches of the given mine. It is safe to conclude that the proposed algorithm can
generate tours which are near-optimal considering the stochastic nature of the algorithm.
The suggested algorithm can also be studied for online implementation to maximize the area
mapped in a mine with one single battery of the UAV. Considering the huge size of openpit mines, the algorithm can also be implemented for the case of path planning for multiple
drones.
An android application has been designed using Android Studio, Google Maps API and
DJI Android SDK. The application is able to plan and execute area mapping operations for
remote surveying of any area with UAVs. The application can also import the 3-D terrain
models generated from photogrammetric software. A terrain-aware feature has been implemented inside the application to maneuver the UAV over a non-planar terrain. This feature
enables the drone to fly at altitudes as low as 10 m to take high definition images of an area.
It is able to accomplish this task with two stages of flight. Stage 1 flight is a traditional
high-altitude mapping operation which outputs 3-D models of the area. Stage 2 flight is
being planned to use these models making the drone to adjust its flying altitude based on the
ground elevation. The result is the UAV which is capable of flying closer to the ground and
taking high-resolution images. The entire method was tested and validated at site in Reno,
Nevada. The resolution of the models was compared to arrive at the conclusion that the stage
two flight generates a high-resolution 3-D model of a given area.
REFERENCES
Bhide, S., John, N. & Kabuka, M.R. 1993. A Boolean Neural Network Approach for the Traveling
Salesman Problem. Computers, IEEE Transactions on, 42:1271–1278.
Cappiello, A., Ismail, C., Edward, N., Alessandro, L. & Maya, A.Z. 2002. A Statistical Model of Vehicle
Emissions and Fuel Consumption. Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems:801–809.
Dassualt systemss. 2018. Geovia Surpac.
Di Franco, C. & Buttazzo, G. 2016. Coverage Path Planning for UAVs Photogrammetry with Energy
and Resolution Constraints. Journal of Intelligent & Robotic Systems, 83(3):445–462.
DJI. 2018. DJI Developer. [Online] Available from: https://developer.dji.com/mobile-sdk/.
Ergezer, H., Kemal, L. 2013. Path Planning for UAVs for Maximum Information Collection. IEEE
Transactions on Aerospace Electronic Systems, 49:502–520.
Gen, M. & Runwei, C. 1997. Genetic Algorithms & Engineering Design.
Glover, F. 1990. Artificial intelligence, heuristic frameworks and tabu search, Managerial & Decision
Economics. 365–378.
Google. 2018. Google Developer. [Online] Available from: https:// developer.android.com/studio/.
Google. 2018. Google Maps Platform. [Online] Available from: https://cloud.google.com/maps-platform/
maps/.
Hartley, R. & Zisserman, A. 2000. Multiple view geometry in computer vision. Cambridge University
Press.
5 CONCLUSIONS
Geo-referenced three-dimensional models of a mine can be generated from the nadir
aerial imagery with resolution comparable to other scanning methods. Resolution of
the generated point cloud and orthomosaic from the first stage flight is not sufficient
to be used for measuring tension cracks. Resolution of the models can be improved by
increasing the size of image sensor resolution or decreasing the flight altitude or both.
Therefore, a second terrain-aware flight has been planned for getting high-resolution
images in this study.
An empirical study was performed to model the battery performance of a test UAV considering various flight circumstances. This way, a linear regression model for the power consumption of the drone was derived and validated for a test scenario. Further, an optimization
algorithm was proposed to solve the problem of path planning which resulted in a path which
consumes the least battery power. A case study based on an open pit mine from Nevada was
presented. The algorithm was tested to find out the optimum path for high resolution imaging of the benches of the given mine. It is safe to conclude that the proposed algorithm can
generate tours which are near-optimal considering the stochastic nature of the algorithm.
The suggested algorithm can also be studied for online implementation to maximize the area
mapped in a mine with one single battery of the UAV. Considering the huge size of openpit mines, the algorithm can also be implemented for the case of path planning for multiple
drones.
An android application has been designed using Android Studio, Google Maps API and
DJI Android SDK. The application is able to plan and execute area mapping operations for
remote surveying of any area with UAVs. The application can also import the 3-D terrain
models generated from photogrammetric software. A terrain-aware feature has been implemented inside the application to maneuver the UAV over a non-planar terrain. This feature
enables the drone to fly at altitudes as low as 10 m to take high definition images of an area.
It is able to accomplish this task with two stages of flight. Stage 1 flight is a traditional
high-altitude mapping operation which outputs 3-D models of the area. Stage 2 flight is
being planned to use these models making the drone to adjust its flying altitude based on the
ground elevation. The result is the UAV which is capable of flying closer to the ground and
taking high-resolution images. The entire method was tested and validated at site in Reno,
Nevada. The resolution of the models was compared to arrive at the conclusion that the stage
two flight generates a high-resolution 3-D model of a given area.
REFERENCES
Bhide, S., John, N. & Kabuka, M.R. 1993. A Boolean Neural Network Approach for the Traveling
Salesman Problem. Computers, IEEE Transactions on, 42:1271–1278.
Cappiello, A., Ismail, C., Edward, N., Alessandro, L. & Maya, A.Z. 2002. A Statistical Model of Vehicle
Emissions and Fuel Consumption. Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems:801–809.
Dassualt systemss. 2018. Geovia Surpac.
Di Franco, C. & Buttazzo, G. 2016. Coverage Path Planning for UAVs Photogrammetry with Energy
and Resolution Constraints. Journal of Intelligent & Robotic Systems, 83(3):445–462.
DJI. 2018. DJI Developer. [Online] Available from: https://developer.dji.com/mobile-sdk/.
Ergezer, H., Kemal, L. 2013. Path Planning for UAVs for Maximum Information Collection. IEEE
Transactions on Aerospace Electronic Systems, 49:502–520.
Gen, M. & Runwei, C. 1997. Genetic Algorithms & Engineering Design.
Glover, F. 1990. Artificial intelligence, heuristic frameworks and tabu search, Managerial & Decision
Economics. 365–378.
Google. 2018. Google Developer. [Online] Available from: https:// developer.android.com/studio/.
Google. 2018. Google Maps Platform. [Online] Available from: https://cloud.google.com/maps-platform/
maps/.
Hartley, R. & Zisserman, A. 2000. Multiple view geometry in computer vision. Cambridge University
Press.