Open Access Open Access  Restricted Access Subscription Access

A Predictive Model based on a Cartographic and Geodetic System for Overhead Power Line Inspection by UAV

Juan I. Larrauri, Gorka Sorrosal, Mikel González

Abstract


In this paper, we present a new automatic system in real time for overhead power line inspection by Unmanned Aerial Vehicle (UAV). The main contribution and novelty of this paper is focused on the design and development of an offline/online path planner through a new predictive model based on the cartographic and geodetic system. The environment of a power line inspection by UAV is static and known beforehand, therefore the flight planner could be designed offline and will automatically be updated during the flight inspection. This predictive model is used for achieving, by intelligent way, the best flight path and also will determine a priori the position and features of the electrical components to be reviewed and analyzed during the automatic flight inspection.

The main purpose of the digital cartographic system is to provide the conversion of geographic information to spatial data. In order to realize the conversion from graphic maps to data, new artificial vision algorithms have been designed in this project to automatically extract the information from metadata files from GIS system. The output of data contains information about the position of electrical towers, insulators, roads, streets, railroad lines, rivers , wooded areas, isolated trees and obstacles along the power lines path.

This paper is structured as follows. First section briefly introduces the current methods of inspection of overhead power lines. Second section describes our proposed system in detail. Third section present and discuss the experimental results. In the next section concludes our work with the conclusions. Finally, we cite the publications used in this paper.


Full Text:

PDF

References


K.Sarabandi and M. Park, “Extraction of Power line Maps from

Millimeter-wave Polarimetric SAR images”. IEEE Transactions on

Antennas and Propagation. 48: 1802-1809. 2000.

P.J. Appelt, and J.W. Goodfellow, “Research on How Trees Cause

Interruptions- Applications to Vegetation Management”, IEEE Rural

Electric Power Conference. 2004: Scottsdale, Arizona.

S. Clode and F. Rottensteiner, “Classification of trees and powerlines

from medium resolution airborne laser scanner data in urban

environments”. APRS Workshop on Digital image computing. Brisbane,

Australia.191-196. 2005

G. Conte, and P. Doherty, “An integrated UAV navigation system based

on aerial image matching”, IEEE Aerospace Conference, pp. 1–10.

R.A. McLaughlin. “Extracting Transmission Lines from Airborne

LiDAR Data”. IEEE Geoscience and Remote Sensing 222-226.2006.

L.A.F. Fernandez,. and M.M. Oliveira, “Real-time line detection through

an improved Hough transform voting scheme”. Pattern Recognition, 41:

p. 299-314. 2008.

K. Beck, and R. Mathieu, “Can Power Companies use Space Patrols to

Monitor Transmission Corridors?” in ESRI User Group Conference.:

San Diego. 2004.

B.D. Russell, “Reliability Based Vegetation Management Through

Intelligent System Monitoring”, Power Systems Engineering Research

Center. 2007, Texas A&M University: Texas.

J. Zhang, J. Dong, and M. Shi, “An Adaptive “Method for Image

Filtering with Pulse-coupled Neural Networks”, IEEE Internation

Conference on Image Processing (ICIP). 2005: Genova, Italy.

I. Golightly and D. Jones. “Visual Control of an Unmanned Aerial

Vehicle for Power Line Inspection”. Advanced Robotics, 2005. ICAR

'05. Proceedings, 12th International Conference. 2005.

M. Williams, D. I. Jones, and G. K. Earp, “Obstacle avoidance during

aerial inspection of power lines,” Aircraft Engineering and Aerospace

Technology, vol. 73(5), pp. 472–479, 2001.

T. Lindblad, and J.M. Kinser, “Image Processing Using Pulse- Coupled

Neural Networks”. Second ed. 2005: Springer. 161.

Yan, G., “Automatic extraction of power lines from aerial images. IEEE

Geoscience and Remote Sensing Letters, 2007. 4(3): p. 387-391.

K. Yamamoto and K.Yamada, “Analysis of the infrared images to detect

power lines”. IEEE Annual International Conference,

Proceedings/TENCON. Brisbane, Australia. 343-346. 1997.

K. Valavanis, “Advances in unmanned aerial vehicles: state of the art

and the road to autonomy. Intelligent Systems, Control and Automation:

Science and Engineering 33. 2007.

I. Golightlyand D. Jones, “Visual Control of an Unmanned Aerial

Vehicle for Power Line Inspection”, 12th International Conference on

Advanced Robotics. Seattle, WA. 6. 2005.

Y. Ma, “Principle and Applications of Pulse-Coupled Neural Networks”.

Beijing: Science Press.2005.

R. Eckhorn, ”A neural network for feature linking via synchronous

activity: Results from cat visual cortex and from simulations, in Models

of Brain Function”, R.M.J. Cotterill, Editor. 1989, Cambridge University

Press: Cambridge. p. 255-272.

R. Forgác, and I. Mokriš. “Formal Representation of Images by Pulse

Coupled Neural Networks”, 3rd Slovakian-Hungarian Joint Symposium

on Applied Machine Intelligence 2005. Slovakia.

N.Aggarwaland W. Karl, “Line Detection in Images Through

Regularized Hough Transform”. IEEE Transactions on Image

Processing, 15(3): p. 582-591. 2006.

N.Agganval, and W.C. Karl, “Line Detection in Images Through

Regularized Hough Transform”, International Conference on Image

Processing: Vancouver, BC. 2000.

J.A Berni,,” Thermal and Narrowband Multispectral Remote Sensing for

Vegetation Monitoring From an Unmanned Aerial Vehicle”. IEEE

Transactions on Geoscience and RemoteSensing,. 47(3): p. 722-738.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2012.v8.783

Refbacks

  • There are currently no refbacks.