Helicopter Unmanned Aerial Vehicle Path Planning to Optimise Power Line Inspections

137 Pages·2015·2.66 MB·English

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How to cite this thesis
Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/
M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved
from: https://ujdigispace.uj.ac.za (Accessed: Date).
Helicopter Unmanned Aerial Vehicle Path Optimisation
for Power Line Inspections

By
Thembani Thabo Moyo

DISSERTATION

Submitted in partial fulfilment of the requirements for the degree

MASTERS INGENERIAE

In
ELECTRICAL AND ELECTRONIC ENGINEERING SCIENCE

In the
FACULTY OF ENGINEERING AND THE BUILT ENVIRONMENT
At the
UNIVERSITY OF JOHANNESBURG

STUDY LEADER: Francois du Plessis

July 2014
Abstract
Electricity is a highly valued resource used in households and to drive economic production
in industry; its efficient generation and distribution to the end user is therefore critical. This
necessitates frequent inspection of power lines and the associated components that allow for
energy transmission over long distances.
Current power line inspection methods such as manned helicopters and ground surveillance
crews are expensive resulting in the use of Unmanned Aerial Vehicles (UAVs) being
presented as a cost effective alternative. Extensive research has been done in the development
of a UAV platform for a number of uses including power line inspection. However, issues
arise from the use of UAVs for power line inspection. Some of these issues include ensuring
that the platform is at a safe distance from power lines; procedures to be followed when
conducting the inspections; and how the UAV responds to unexpected changes in the
environment.
The underlying hypothesis of this research is that the Travelling Salesman Problem (TSP) can
be utilised in the optimisation of power line inspection using helicopter UAVs. A set of
waypoints was collected based on the best position to view the overhead power line
components and these waypoints represent the cities in the TSP. The UAV’s path is
optimised to minimise the time taken, distance travelled and fuel used to complete the power
line inspection.

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Acknowledgements

Sincere gratitude to:
 God for the opportunity and the grace to complete this project.
 My family and friends for their support and prayers.
 Francois du Plessis (Supervisor UJ) and Prof. Johan Meyer (co-supervisor UJ) for
advice and guidance.
 Materials Sensors and Manufacturing group at the Council for Scientific and
Industrial Research for advice and mentorship.
 Dean Grobbeler (Aerial concepts) for carrying out all flight inspections.
 Brian Taylor from the South African Bureau of Standards (SABS) testing facility for
allowing me to conduct testing at the facility.

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Contents

Chapter 1 Introduction ............................................................................................................................ 1
1.1 Background ......................................................................................................................... 1
1.2 Problem Statement .............................................................................................................. 8
1.3 Hypothesis ......................................................................................................................... 12
1.4 Research Objectives .......................................................................................................... 13
1.5 Project Scope .................................................................................................................... 13
1.6 Document overview .......................................................................................................... 15
1.7 Conclusion ........................................................................................................................ 16
Chapter 2 Literature Review ................................................................................................................. 17
2.1 Introduction ....................................................................................................................... 17
2.2 State of the art in power line inspections .......................................................................... 17
2.3 Path planning .................................................................................................................... 28
2.4 Path Optimisation .............................................................................................................. 48
2.5 Difference between path planning and path optimisation ................................................. 59
2.6 Helicopter fuel burn model ............................................................................................... 59
2.7 Conclusion ........................................................................................................................ 62
Chapter 3 Theoretical Development ..................................................................................................... 65
3.1 Introduction ....................................................................................................................... 65
3.2 Inspection protocol ............................................................................................................ 65
3.3 Path planning formulation ................................................................................................. 66
3.4 Path optimisation formulation ........................................................................................... 77
3.5 Conclusion ........................................................................................................................ 80
Chapter 4 Experimental Analysis ......................................................................................................... 81
4.1 Introduction ....................................................................................................................... 81
4.2 Setting of simulation parameters and limit testing ............................................................ 81
4.3 Implementation of waypoint dataset ................................................................................. 84
4.4 Determining helicopter fuel flow ...................................................................................... 87
4.5 Comparison of paths generated using the different optimisation criteria with fixed start
and end points ....................................................................................................................................... 89
4.6 Comparison of paths generated using the different optimisation criteria with variable start
and end points ....................................................................................................................................... 90
4.7 Investigating the effect on the optimal path when helicopter speeds are varied ............... 91
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4.8 Investigating the effect on the path after increasing the fuel flow .................................... 92
4.9 Comparison of criteria values obtained from brute force and TSP-GA ............................ 94
4.10 Conclusion ........................................................................................................................ 96
Chapter 5 Conclusion ............................................................................................................................ 98
5.1 Introduction ....................................................................................................................... 98
5.2 Restatement of hypothesis ................................................................................................ 98
5.3 Research methodology ...................................................................................................... 98
5.4 Summary of findings ....................................................................................................... 101
5.5 Limitations of the study .................................................................................................. 103
5.6 Future work ..................................................................................................................... 104
Appendix ............................................................................................................................................. 106
Appendix A: Miniature Helicopter Modelling and Control ................................................................ 106
Appendix B: Minutes from meeting with ESKOM representatives ................................................... 109
Appendix C: Fuel Consumption ......................................................................................................... 113
Bibliography ...................................................................................................................................... 119

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Table of Figures

Figure 1-1 Typical obstacles on conductors shown: (a) suspension insulator, (b) strain insulator, (c)
damper, (d) spacer and (e) aircraft warning light [6] .............................................................................. 4
Figure 1-2 An example of a ceramic insulator [8] (left) and a polymeric insulator (right) .................... 5
Figure 1-3 Self-supporting pylon used in research ................................................................................. 6
Figure 1-4 An image of the MultiCAM (left) and an image taken with MultiCAM (right)[12] ............ 7
Figure 1-5 Image taken with MultiCAM [12] ........................................................................................ 8
Figure 1-6 (a)Manned helicopter inspection; (b) Foot patrol inspection; (c) and RUAV with gimbal
shown clockwise [11] ............................................................................................................................. 9
Figure 1-7 MultiCAM attached to gimbal [12] ..................................................................................... 10
Figure 2-1 Expliner inspection robot [25] ............................................................................................. 19
Figure 2-2 The LineScout inspection robot [22] ................................................................................... 19
Figure 2-3 Diagram showing the AMPLIS system configuration [24] ................................................. 20
Figure 2-4 The CSIRO AVS used to test the stereo based navigation methods [27]. .......................... 21
Figure 2-5 Experimental set up showing trolley system with camera mounted on gimbals [29]. ........ 22
Figure 2-6 High level diagram showing how the various components will interact [30] ..................... 23
Figure 2-7 An image of the processed sensor layout. A: Line accelerometer; B: Tension sensor; C:
Local processing and gateway; D: MAV landing pad and recharging station; E: Lightning arrestor and
dipole VHF Yagi antenna; G: Helium canisters; H: Anemometry station; I: Temperature, humidity
and vibration sensors; J: Magnetometer and EMF collector [30] ......................................................... 24
Figure 2-8 The MAV with robotic manipulators and on-board camera is shown [30] ......................... 24
Figure 2-9 The image to the left is an artist's impression of how the rotorcraft would traverse the line
and the image to the right is a ducted fan rotorcraft model used as a proof of concept [32] ................ 25
Figure 2-10 Scaled experimental model of MoboLab concept [33] ..................................................... 26
Figure 2-11 Piano Mover's Problem [37] .............................................................................................. 30
Figure 2-12 Voronoi and Delaunay representation [40] ....................................................................... 32
Figure 2-13 The depth first search [42] ................................................................................................ 33
Figure 2-14 The breadth first search [42] ............................................................................................. 33
Figure 2-15 The RRT is shown in a square region. It is shown expanding in a few directions to
explore the four corners of the square [45]. .......................................................................................... 35
Figure 2-16 Probalistic Road Map from node I to G used in robotic path planning [47] ..................... 36
Figure 2-17 Graph to describe the A* search........................................................................................ 38
Figure 2-18 Euclidean distance heuristic that is used on square grids that allow movement in any
direction [56]. ........................................................................................................................................ 41
Figure 2-19 Manhattan distance heuristic is typically used on square grids that allow 4 directions of
movement [56]. ..................................................................................................................................... 42
Figure 2-20 A representation of the Chebyshev distance used on square grids that allow diagonal
movement [56]. ..................................................................................................................................... 42
Figure 2-21 Input devices for tracking [63] .......................................................................................... 45
Figure 2-22 Graph used to describe the TSP with cities A, B, C, D and E. The respective weights on
edges between the cities are shown. The TSP will look to obtain a tour with the lowest weight [76]. 49
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Figure 2-23 The roulette wheel selection [84] is shown. This wheel represents each individual solution
with corresponding weights. The individuals with the larger weighting get selected ahead of the one
with the lower weighting as the wheel is rotated. ................................................................................. 52
Figure 2-24 Flow chart describing how Genetic Algorithm works ...................................................... 54
Figure 2-25 Principle showing how ants choose shortest path between food and nest [87] ................. 55
Figure 2-26 Graph showing the number of possible tours for given waypoints ................................... 59
Figure 3-7 View from the ground of helicopter carrying out an inspection ......................................... 68
Figure 3-8 View from helicopter inspecting left side of pylon ............................................................. 68
Figure 3-9 Google earth image at the end of the inspection ................................................................. 68
Figure 3-1 Stages of environment development ................................................................................... 71
Figure 3-5 Three waypoints A, B and C with an obstacle are shown. A path that goes straight from A
to C cannot be allowed because a collision would occur therefore such a path has a very high
weighting. ............................................................................................................................................. 73
Figure 3-6 Control diagram showing implementation of A* with TSP for the power line inspection
optimisation problem. The A* provides obstacle free paths obtained from the open list and provides
the input to the GA based TSP which then provides an optima ............................................................ 74
Figure 3-4 The triangle forms the basis of determining the distance travelled between two points given
the height difference and the 2D distance given by the A*. ................................................................. 75
Figure 3-5 First instance where two waypoints are separated by the pylon boundary ......................... 76
Figure 3-6 An illustration of unit vectors showing direction and orientation between waypoints. The
green arrows represent orientations whereby the platform is facing one waypoint whilst stationary at
another. The black arrows represent the platform facing the component to be inspected. ................... 78
Figure 4-1 Results for 64 waypoints (top); 128 waypoints (middle); 1000 waypoints (bottom) .......... 82
Figure 4-2 Graphs showing the increase in computational time as the number of waypoints (top) and
iterations (bottom) increases ................................................................................................................. 83
Figure 4-3 Top view(A), side view(B) and front view (C) of the results of loading the pre-processed
waypoints .............................................................................................................................................. 85
Figure 4-4 Graphs showing distribution of constraint values on 10 collected sample waypoints ........ 95
Figure 5-1 Research methodology followed ....................................................................................... 100

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List of Tables
Table 1-1 A summary of the main causes of power line faults. .............................................................. 3
Table 1-2 RUAV Specifications ........................................................................................................... 11
Table 2-1 A summary of work done by other researchers in an attempt to optimise power line
inspections............................................................................................................................................. 27
Table 2-2 Values of 𝒄𝟏 for various flight phases [88] .......................................................................... 62
Table 3-1 Raw GPS data collected from telemetry downlink ............................................................... 69
Table 3-2 Cartesian representation of GPS data collected .................................................................... 70
Table 3-3 Waypoints referenced to pylon corner .................................................................................. 70
Table 3-4 Information in a typical waypoint file detailing the area to be inspected, platform used,
inspector and waypoints with respective coordinate’s. ......................................................................... 73
Table 3-5 The new Distmat used with highlighted values showing the input from A* ........................ 76
Table 4-1 Estimate costs associated with different inspection methods ............................................... 87
Table 4-2 Results of fuel used in various flight phases ........................................................................ 88
Table 4-3 Paths obtained with different optimisation criteria ............................................................... 89
Table 4-4 Paths obtained with different optimisation criteria ............................................................... 90
Table 4-5 Results obtained for the different cases ................................................................................ 92
Table 4-6 Results obtained for the different cases continued .................. Error! Bookmark not defined.
Table 4-7 Fuel flow results ................................................................................................................... 93
Table 4-8 Criteria values obtained using a brute force ......................................................................... 94
Table 4-9 Comparison of brute force and TSP-GA results ................................................................... 96
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List of Acronyms
ACO Ant Colony Optimisation
ATSP Asymmetrical Travelling Salesman Problem
AFCS Automatic Flight Control System
CCD Charged Coupled Device
CIFER Comprehensive Identification FrEquency Responses
CPU Central Processing Unit
DoF Degrees of Freedoms
EA Evolutionary Algorithm
EPDM Ethylene Propylene Diene Monomer
FF Fuel Flow
GA Genetic Algorithm
GIS Geographic Information Systems
GPS Global Positioning System
GUI Graphical User Interface
IMU Inertial Measurement Unit
LiDAR Light Detection And Ranging
NASA National Aeronautics Space Agency
NP Hard Non-deterministic Polynomial-time Hard
PC Personal Computer
PID Proportional, Integral and Derivative
PRM Probabilistic Road Map
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