Intelligent Machine Learning Approaches for Aerospace Applications

130 Pages·2017·4.11 MB·English

Intelligent Machine Learning Approaches
for Aerospace Applications
Anoop Sathyan
College of Engineering and Applied Science
University of Cincinnati
A dissertation submitted for the
partial fulfillment of the degree of
Doctor of Philosophy in Aerospace Engineering & Engineering Mechanics
2017 March
Committee Chair: Dr. Kelly Cohen
Abstract
Machine Learning is a type of artificial intelligence that provides machines or networks
the ability to learn from data without the need to explicitly program them. There are
different kinds of machine learning techniques. This thesis discusses the applications of
twooftheseapproaches: GeneticFuzzyLogicandConvolutionalNeuralNetworks(CNN).
Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of
applications. FLS is a universal approximator that reduces the need for complex mathe-
matics and replaces it with expert knowledge of the system to produce an input-output
mapping using If-Then rules. The expert knowledge of a system can help in obtaining the
parameters for small-scale FLSs, but for larger networks we will need to use sophisticated
approaches that can automatically train the network to meet the design requirements.
This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and
EVE can tune the FLS parameters to minimize a cost function that is designed to meet
the requirements of the specific problem. EVE is an artificial intelligence developed by
PsibernetixthatistrainedtotunelargescaleFLSs. TheparametersofanFLScaninclude
the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs).
The main issue with using the GFS is that the number of parameters in a FIS increase
exponentially with the number of inputs thus making it increasingly harder to tune them.
To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in
cascade(Chapter4)orasalayerofparallelFISs(Chapter7). Wehaveobtainedextremely
good results using GFS for different applications at a reduced computational cost com-
paredtootheralgorithmsthatarecommonlyusedtosolvethecorrespondingproblems. In
this thesis, GFSs have been designed for controlling an inverted double pendulum, a task
allocation problem of clustering targets amongst a set of UAVs, a fire detection problem
and the aircraft conflict resolution problem.
Duringthelastdecade,CNNshavebecomeincreasinglypopularinthedomainofimage
and speech processing. CNNs have a lot more parameters compared to GFSs that are
tuned using the back-propagation algorithm. CNNs typically have hundreds of thousands
or maybe millions of parameters that are tuned using common cost functions such as
integral squared error, softmax loss etc. Chapter 5 discusses a classification problem to
classify images as humans or not and Chapter 6 discusses a regression task using CNN for
producing an approximate near-optimal route for the Traveling Salesman Problem (TSP)
which is regarded as one of the most complicated decision making problem.
Both the GFS and CNN are used to develop intelligent systems specific to the applica-
tion providing them computational efficiency, robustness in the face of uncertainties and
scalability.

Acknowledgements
I would like to thank first and foremost my advisor Dr. Kelly Cohen who has
mentored me through the last 4 years of my academic career. His guidance
has been instrumental to my success and has constantly helped me push the
boundaries. IwouldalsoliketothankDr. ManishKumarwhohelpedmewith
my involvement in the SIERRA project. Apart from the SIERRA project, we
also worked together on the CNN based approach to solving the TSP and
the ACRP which are discussed in Chapters 6 and 7, respectively. I would
also like to thank Dr. Nick Ernest and his company, Psibernetix Inc., for
the support with Chapters 3 and 7 and for providing a trial version of their
popular software, EVE. I am most thankful to all of my professors, co-workers
and peers who assisted me throughout this process and am incredibly grateful
to the National Science Foundation who funded me through my PhD. I would
like to thank my family: my wife, parents and my brother for the support they
keep providing.
Contents
List of Figures v
List of Tables vii
1 Introduction 1
1.1 Genetic Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Inverted Double Pendulum Using Genetic Fuzzy Control 10
2.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 FIS tuned without noise . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 FIS tuned with 5% noise . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Conclusions & Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Genetic Fuzzy Approach to UAV Swarm Routing 26
3.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Fuzzy Clustering Method . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.2 Genetic Fuzzy Clustering Method . . . . . . . . . . . . . . . . . . . 35
3.3.3 Genetic Fuzzy Clustering Using an Approximate Cost Function . . . 37
3.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Conclusions & Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 Genetic Fuzzy Logic for Fire Detection 45
4.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.1 Setting up the FLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.2 Training the FLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
iii
CONTENTS
4.3 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Conclusions & Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5 Human detection using CNN 71
5.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.1 CNN for human classification . . . . . . . . . . . . . . . . . . . . . . 72
5.1.2 Image segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.1.3 Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6 CNN based approach for solving TSP 81
6.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.1 10-city TSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.2 50-city TSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.3.1 10-city TSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.3.2 50-city TSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.4 Conclusions & Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7 Aircraft Conflict Resolution Problem 91
7.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
7.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.2.1 Five aircraft problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.2.2 Ten aircraft problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.3.1 Five aircraft problem with (cid:15) = 1 . . . . . . . . . . . . . . . . . . . . 100
7.3.2 Five aircraft problem with (cid:15) = 2 . . . . . . . . . . . . . . . . . . . . 103
7.3.3 Ten aircraft problem with (cid:15) = 1 . . . . . . . . . . . . . . . . . . . . 104
7.4 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 106
8 Conclusions & Future Work 108
8.1 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
8.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Glossary 113
References 114
iv
List of Figures
1.1 AnexampleofacrossoverinGA.Thepartofthesolutionafterthecrossover
point is swapped between the two parents. . . . . . . . . . . . . . . . . . . . 3
1.2 An example of a mutation to a randomly chosen chromosome in GA. The
value shown in red is modified. . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Schematic of genetic fuzzy system . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 CNN architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Max-pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 Benchmark 1: Double pendulum . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Flowchart showing application of genetic fuzzy to double pendulum . . . . . 14
2.3 Membership functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Time histories of θ and θ . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1 2
2.5 Time histories of θ and θ when noise is applied . . . . . . . . . . . . . . . 17
1 2
2.6 Time histories of θ and θ with non-zero initial angular velocities . . . . . 19
1 2
2.7 Time histories of θ and θ with non-zero initial angular velocities under
1 2
the effect of noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.8 Time histories of θ and θ . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1 2
2.9 Time histories of θ and θ when noise is applied . . . . . . . . . . . . . . . 21
1 2
2.10 Time histories of θ and θ with non-zero initial angular velocities . . . . . 22
1 2
2.11 Time histories of θ and θ with non-zero initial angular velocities under
1 2
the effect of noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 Creation of visibility polygons . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Benchmark 2: PVMTSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Point selection on each polygon . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Membership function tuning for both inputs . . . . . . . . . . . . . . . . . . 36
3.5 Flowchart of the PVMTSP algorithm. . . . . . . . . . . . . . . . . . . . . . 38
3.6 Tuned membership functions for PVMTSP . . . . . . . . . . . . . . . . . . 39
3.7 PVMTSP solution with (a) small and (b) large polygons . . . . . . . . . . . 41
3.8 Computational time and longest distance in relation to number of targets
for small polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.9 Computational time and longest distance in relation to number of targets
for large polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
v
LIST OF FIGURES
4.1 AeroQuad Quadcopter with gimbaled cameras used for the SIERRA project 46
4.2 Schematic showing two-stage cascaded FLS . . . . . . . . . . . . . . . . . . 51
4.3 Membership functions for FIS1 . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4 Membership functions for FIS2 . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.5 Vector R tuned by GA. R(1 : 10) gives the boundaries of the membership
functions and R(11 : 28) gives the antecedents of the rulebase of FIS1 and
FIS2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.6 Centroid and LOM defuzzification . . . . . . . . . . . . . . . . . . . . . . . 57
4.7 Visual and IR images used for training . . . . . . . . . . . . . . . . . . . . . 58
4.8 R after tuning using GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.9 Membership functions for FIS1 after training the FLS . . . . . . . . . . . . 62
4.10 Membership functions for FIS2 after training the FLS . . . . . . . . . . . . 63
4.11 Fire colored pixels detected using FIS1 . . . . . . . . . . . . . . . . . . . . . 65
4.12 ESI test image1: Fire detection using FLS . . . . . . . . . . . . . . . . . . . 66
4.13 ESI test image 2: Fire detection using FLS . . . . . . . . . . . . . . . . . . 67
4.14 UAV MASTER Lab test: Fire detection using FLS . . . . . . . . . . . . . . 68
5.1 FLIR image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2 Sample image from the OTCBVS database and the cropped human images 72
5.3 CNN architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4 Image after segmentation. ROIs are shown in bounding boxes. . . . . . . . 75
5.5 Flowchart of the detection and tracking algorithm . . . . . . . . . . . . . . 78
5.6 Training and validation loss v/s epochs. . . . . . . . . . . . . . . . . . . . . 79
5.7 Processed video frames with humans shown in bounding boxes . . . . . . . 79
6.1 Input image to the CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 CNN architecture for 10-city TSP. . . . . . . . . . . . . . . . . . . . . . . . 84
6.3 CNN architecture for 50-city TSP. . . . . . . . . . . . . . . . . . . . . . . . 85
6.4 Converting CNN predictions into actual TSP solutions . . . . . . . . . . . . 87
6.5 Solution for a 50-city TSP scenario. . . . . . . . . . . . . . . . . . . . . . . 89
6.6 Tradeoff between optimal distance and computational time . . . . . . . . . 90
7.1 Sample scenario for five aircraft . . . . . . . . . . . . . . . . . . . . . . . . . 93
7.2 Maneuver model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.3 FLS architecture for five aircraft problem . . . . . . . . . . . . . . . . . . . 96
7.4 Sample input or output membership function. The boundaries are defined
using the parameters a and b which is tuned using EVE. . . . . . . . . . . . 97
7.5 FLS architecture for ten aircraft problem . . . . . . . . . . . . . . . . . . . 100
7.6 FLS solution for a 5-aircraft scenario with (cid:15) = 1. The polygons represent
the buffered uncertainty regions at each time step . . . . . . . . . . . . . . . 102
7.7 FLS solution for a 5-aircraft scenario with (cid:15) = 2. . . . . . . . . . . . . . . . 103
7.8 FLS solution for a 10-aircraft scenario with (cid:15) = 1. . . . . . . . . . . . . . . 106
vi