Quad Rotorcraft Control develops original control methods for the navigation and hovering flight of an autonomous mini-quad-rotor robotic helicopter. These methods use an imaging system and a combination of inertial and altitude sensors to localize and guide the movement of the unmanned aerial vehicle relative to its immediate environment. The history, classification and applications of UAVs are introduced, followed by a description of modelling techniques for quad-rotors and the experimental platform itself. A control strategy for the improvement of attitude stabilization in quad-rotors is then proposed and tested in real-time experiments. The strategy, based on the use low-cost components and with experimentally-established robustness, avoids drift in the UAV’s angular position by the addition of an internal control loop to each electronic speed controller ensuring that, during hovering flight, all four motors turn at almost the same speed. The quad-rotor’s Euler angles being very close to the origin, other sensors like GPS or image-sensing equipment can be incorporated to perform autonomous positioning or trajectory-tracking tasks. Two vision-based strategies, each designed to deal with a specific kind of mission, are introduced and separately tested. The first stabilizes the quad-rotor over a landing pad on the ground; it extracts the 3-dimensional position using homography estimation and derives translational velocity by optical flow calculation. The second combines colour-extraction and line-detection algorithms to control the quad-rotor’s 3-dimensional position and achieves forward velocity regulation during a road-following task. In order to estimate the translational-dynamical characteristics of the quad-rotor (relative position and translational velocity) as they evolve within a building or other unstructured, GPS-deprived environment, imaging, inertial and altitude sensors are combined in a state observer. The text give the reader a current view of the problems encountered in UAV control, specifically those relating to quad-rotor flying machines and it will interest researchers and graduate students working in that field. The vision-based control strategies presented help the reader to a better understanding of how an imaging system can be used to obtain the information required for performance of the hovering and navigation tasks ubiquitous in rotored UAV operation.
Author(s): Luis Rodolfo García Carrillo, Alejandro Enrique Dzul López, Rogelio Lozano, Claude Pégard
Series: Advances in Industrial Control
Edition: 2013
Publisher: Springer
Year: 2012
Language: English
Pages: 198
Tags: Военные дисциплины;Оружие и военная техника;Авиационное вооружение и техника;Вертолеты;
Quad Rotorcraft Control......Page 2
Series Editors' Foreword......Page 5
Foreword......Page 7
Preface......Page 10
Acknowledgements......Page 11
Contents......Page 12
1.1 Unmanned Aerial Vehicles......Page 15
1.1.1 Brief History......Page 16
1.1.3 UAVs Classification......Page 21
1.2 State of the Art......Page 30
1.3 Problem Statement......Page 34
1.4 Contributions......Page 35
1.5 Book Outline......Page 36
2.1 The Quad-Rotor Mini-Rotorcraft......Page 37
2.2.1 Euler-Lagrange Approach......Page 39
2.2.2 Newton-Euler Approach......Page 43
Torques......Page 44
2.2.3 Newton's Equations to Lagrange's Equations......Page 45
2.2.4 Newton-Euler Approach for an X-type Quad-Rotor......Page 46
2.3 Concluding Remarks......Page 48
3.1 General Overview of UAV Sensing Technologies......Page 49
3.2 System Architecture......Page 51
3.3 Supervisory Ground Station......Page 52
3.4 Quad-Rotor Design......Page 54
3.4.1 Cross-Flyer Design......Page 55
3.4.2 X-Flyer Design......Page 57
3.4.3 Improved X-Flyer Design......Page 59
3.5.1 Altitude and Yaw Control......Page 62
3.5.2 Control of Forward Position and Pitch Angle......Page 63
3.5.3 Control of Lateral Position and Roll Angle......Page 64
3.6 Autonomous Hover Flight Experiments......Page 65
3.6.1 Cross-Flyer Hover Graphics......Page 66
3.6.2 X-Flyer Hover Graphics......Page 67
3.6.3 Improved X-Flyer Hover Graphics......Page 69
3.7 Concluding Remarks......Page 70
4.1 Introduction......Page 72
4.2 Brushless DC Motor and Electronic Speed Controller......Page 73
4.3 Control Strategy for Attitude Improvement......Page 75
4.3.1 Attitude Control......Page 76
4.3.2 Armature Current Control......Page 77
4.4.1 Aerial Vehicle......Page 79
4.5 Experimental Results......Page 80
4.6 Concluding Remarks......Page 83
5.1 Camera Model......Page 84
Central Projection Using Homogeneous Coordinates......Page 85
Principal Point Offset......Page 86
Intrinsic Properties......Page 87
Calibration Using a Chessboard......Page 89
5.2 Stereo Imaging......Page 91
Triangulation......Page 93
5.2.1 Epipolar Geometry......Page 94
Essential Matrix Math......Page 96
5.2.2 Calibration of the Stereo Imaging System......Page 97
Stereo Rectification......Page 98
Calibrated Stereo Rectification: Bouguet's Algorithm......Page 99
Calibrating a Stereo Rig Using the Camera Calibration Toolbox for Matlab......Page 100
5.3 Optical Flow......Page 103
5.3.1 Computing Methods......Page 104
5.4 Implementing an Imaging System for the Quad-Rotor UAV......Page 106
5.4.1 Deported and Embedded Systems......Page 107
5.4.1.1 Deported Systems......Page 108
5.4.1.2 Embedded Systems......Page 109
Monocular Imaging System......Page 111
Stereo Imaging System......Page 112
5.4.3 Monocular Imaging System Implementation......Page 113
5.4.4 Stereo Imaging System Implementation......Page 114
5.5 Concluding Remarks......Page 115
6.1.1 Introduction......Page 116
6.1.2 Visual System Set-up......Page 117
6.1.3.1 Computing the 3-dimensional Position......Page 118
6.1.3.2 Translational Velocities......Page 120
6.1.4 Control Strategy......Page 121
6.1.4.2 Control of Forward Position and Pitch Angle......Page 122
6.1.4.3 Control of Lateral Position and Roll Angle......Page 124
6.1.5 Experimental System Configuration......Page 125
6.1.6 Experimental Applications......Page 126
6.1.7 Final Comments......Page 127
6.2.1 Introduction......Page 128
6.2.3.1 Nested Saturations Control......Page 129
Control of Lateral Position and Roll Angle......Page 130
Control of Forward Position and Pitch Angle......Page 131
6.2.3.3 Sliding Modes Control......Page 133
6.2.4 Experimental Applications......Page 134
6.2.5 Final Comments......Page 136
6.3.2 System Set-up......Page 138
6.3.3 Image Processing......Page 139
6.3.3.2 Altitude and Position......Page 140
6.3.3.3 Translational Velocities......Page 142
6.3.4.1 Altitude and Yaw Subsystems......Page 143
6.3.4.3 Lateral Subsystem......Page 144
6.3.5 Experimental Application......Page 145
6.4 Concluding Remarks......Page 149
7.1 Estimating Motion......Page 151
7.1.1 Introduction......Page 152
7.1.1.1 Related Work......Page 153
7.1.3 Experimental Platform Overview......Page 154
7.1.3.1 Visual and Inertial Navigation System......Page 155
7.1.3.2 Supervisory Ground Station......Page 156
7.1.4 Stereo Visual Odometry......Page 157
7.1.4.1 Detecting Features......Page 158
7.1.4.2 3-dimensional Reconstruction......Page 159
7.1.4.3 Estimating Motion......Page 160
7.1.5 A Simple Strategy for Imaging, Inertial and Altitude Data Fusion......Page 162
7.1.6 Experimental Results......Page 163
7.1.7 Final Comments......Page 164
7.2 Comparison of Different State Estimation Algorithms for Quad-Rotor Control......Page 165
7.2.1 Introduction......Page 166
7.2.2.1 Measurement Model......Page 167
7.2.3.1 Luenberger State Observer......Page 168
7.2.3.2 Kalman Filter......Page 170
7.2.3.3 Complementary Filter......Page 171
7.2.4 Experimental Results......Page 172
7.2.5 Final Comments......Page 177
7.3 Concluding Remarks......Page 179
Control System for Improving the Attitude Stabilization......Page 180
Development of an Imaging, Inertial and Altitude Sensing System......Page 181
8.2 Future Work......Page 182
References......Page 184
Index......Page 188
